The DOI matches its Crossref record; 107 of the 111 references it cites were found in a registry or an index; none of the 124 sources its claims cite is checked for support yet.
Automatic analysis of facial expressions: the state of the art
Open accessAbstract
Humans detect and interpret faces and facial expressions in a scene with little or no effort. Still, development of an automated system that accomplishes this task is rather difficult. There are several related problems: detection of an image segment as a face, extraction of the facial expression information, and classification of the expression (e.g., in emotion categories). A system that performs these operations accurately and in real time would form a big step in achieving a human-like interaction between man and machine. The paper surveys the past work in solving these problems. The capability of the human visual system with respect to these problems is discussed, too. It is meant to serve as an ultimate goal and a guide for determining recommendations for development of an automatic facial expression analyzer.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This survey reviews the state of the art (through 2000) in automatic facial expression analysis, decomposing the problem into face detection, facial expression data extraction, and facial expression classification. It compares surveyed systems against an "ideal analyzer" modeled on the human visual system and concludes that none of the three subproblems has been solved in the general case.
Key findings
- The paper identifies three related subproblems — face detection, facial expression information extraction, and facial expression classification — and argues an ideal system must automate all three, ideally in real time for HCI use.
- Early systems (1991–1995) achieved correct recognition rates of 70–92 percent when recognizing 3–8 emotion categories; none performed quantified expression classification in terms of facial actions, and only Moses et al. [57] performed in real time.
- Among the surveyed recent systems, reported recognition rates include 92 percent (Yoneyama et al., four expressions), 89 percent for familiar subjects and 73 percent for unknown persons (Hong et al.), 84.5 percent (Huang and Huang), 92 percent generalization / 75 percent for a novel subject (Lyons et al.), 90.1 percent (Zhang et al.), 92 percent upper-face AUs and 86 percent lower-face AUs (Pantic and Rothkrantz), 92/88/83 percent for eyebrow/eye/nose-mouth regions (Cohn et al.), 98 percent (Essa and Pentland), 95 percent (Wang et al.), and 88 percent (Black and Yacoob).
- Most systems rely on frontal-view images of faces without facial hair or glasses, assume constant illumination and immobile subjects, and perform only singular classification into Ekman's six basic emotion categories; only two systems ([62], [106]) perform quantified classification into multiple emotion categories, and none quantifies facial action codes or distinguishes all 44 FACS AUs.
Methods
- Design: a survey/comparative review of vision-based automatic facial expression analysis, covering work from the mid-1970s onward with emphasis on the late 1990s; systems are tabulated (Tables 2–8) against properties of an "ideal analyzer" (Table 1).
- Scope: selectively discusses systems addressing face detection, facial expression data extraction (template-based vs. feature-based; holistic, analytic, and hybrid face representations), and classification (template-, neural-network-, and rule-based methods) from both static images and image sequences.
- Analysis: qualitative comparison of reported techniques, assumptions, and recognition rates; the authors deliberately refrain from ranking systems, arguing that no single common testing database exists.
Limitations
- Stated by the paper: no single well-defined, commonly used testing database exists, so the authors deliberately do not rank systems objectively; several surveyed methods were tested only on the images used for training, so their person-independence is uncertain; most systems assume frontal views, constant illumination, no facial hair or glasses, and immobile subjects, which is unrealistic for the target application domains; only two systems handle automatic face detection in an arbitrary scene and only three deal to some extent with rigid head motions; only Essa and Pentland's method handles faces with facial hair and/or eyeglasses; no system performs quantification of facial action codes or distinguishes all 44 FACS AUs.
- Evident from the text: the survey is selective rather than exhaustive, so coverage of the field is incomplete by the authors' own statement; the "ideal analyzer" is a normative construct rather than an implemented benchmark, and the comparison is largely qualitative with heterogeneous, non-comparable evaluation protocols across systems.
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Claims the paper makes
95 Not checked yet · 42 No basis given · 19 Own interpretation · 3 Own result · 1 Own method
In the paper
“So far, the majority of studies treat various human communication channels separately, as indicated by Nakatsu [58].”
§ Automatic Analysis of Facial Expressions: The State of the Art > 1 INTRODUCTION
What the sources say
- Not checked yetcites Nakatsu [58]
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Relatively few existing works combine different modalities into a single system for human communicative reaction analysis.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 1 INTRODUCTION
What the sources say
- Not checked yetcites [9] source
- Not checked yetcites [15]
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Most of the studies on automated expression analysis perform an emotional classification.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 2.3 Facial Expression Classification
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As shown in Table 3, several surveyed systems can be classified as methods for facial expression analysis from static images.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.2.1 Facial Data Extraction from Static Images: Template-Based Methods
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“None of these systems performs a quantified expression classification in terms of facial actions.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4 AUTOMATIC FACIAL EXPRESSION ANALYSIS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The correct recognition rates reported range from 70-92 percent when recognizing 3-8 emotion categories.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4 AUTOMATIC FACIAL EXPRESSION ANALYSIS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This issue has not been handled by the currently existing systems.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 2.3 Facial Expression Classification
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“In general, three types of face representation are mainly used in facial expression analysis: holistic (e.g., isodensity maps [38]), analytic (e.g., deformable templates [105]), and hybrid (e.g., analytic-to-holistic approach [45]).”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.2 Facial Expression Data Extraction
What the sources say
- Not checked yetcites [38]
- Not checked yetcites [105] source
- Not checked yetcites [45]
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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In the paper
“Also, except the system proposed by Moses et al. [57], no system listed in Table 2 performs in real-time.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4 AUTOMATIC FACIAL EXPRESSION ANALYSIS
What the sources say
- Not checked yetcites [57] source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Nevertheless, most of the studies on vision-based facial expression analysis rely on Ekman's emotional categorization of facial expressions.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 2.3 Facial Expression Classification
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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- Probably not a substantial claim. 58
- It may state two separate claims as one. 47
- The quote only partly states this claim. 6
In the paper
“Although Hong et al. utilize the PersonSpotter system [81], which deals with real-time processing of video sequences, they perform facial expression analysis from static images.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.2.1 Facial Data Extraction from Static Images: Template-Based Methods
What the sources say
- Not checked yetcites [81] source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“No facial hair or glasses are allowed.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.1.1 Face Detection in Facial Images
What the sources say
- Not checked yetcites [62] source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The observed subject should face the camera while siting at approximately 1m distance in front of it.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.1.1 Face Detection in Facial Images
What the sources say
- Not checked yetcites [42]
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“None of these systems are able to perform facial expression information extraction from images of occluded faces or a quantified expression classification in terms of facial actions.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4 AUTOMATIC FACIAL EXPRESSION ANALYSIS
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“These systems primarily concern facial expression animation and do not attempt to classify the observed facial expression either in terms of facial actions or in terms of emotion categories.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4 AUTOMATIC FACIAL EXPRESSION ANALYSIS
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The AAM search algorithm has been tested on 100 hand labeled face images.Of these, 19.2 percent failed to converge to a satisfactory result [12].”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.2.1 Facial Data Extraction from Static Images: Template-Based Methods
What the sources say
- Not checked yetcites [12] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Since the mid 1970s, different approaches are proposed for facial expression analysis from either static facial images or image sequences.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4 AUTOMATIC FACIAL EXPRESSION ANALYSIS
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“The method of Pentland et al. [65] is real-time and has been successfully tested on a database of 7,562 images of some 3,000 people of both sexes, ranged in age and ethnicity, having varying head positions, headwear, and facial hair.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.1.2 Face Detection in Arbitrary Images
What the sources say
- Not checked yetcites [65]
plainn = 3,000- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Depending on the face model a template-based or a feature-based method is applied for facial expression data extraction.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.2 Facial Expression Data Extraction
plainconditional- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“The utilized techniques include holistic spatial analysis [13], [70], [54], spatio-temporal analysis [52], [100], [73], [57], and analytic spatial analysis [93], [43], [44], [92], [40].”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4 AUTOMATIC FACIAL EXPRESSION ANALYSIS
What the sources say
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The method works with images of faces without facial hair and glasses, which are hand-labeled with the landmark points beforehand approximated with the proposed AAM.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.2.1 Facial Data Extraction from Static Images: Template-Based Methods
What the sources say
- Not checked yetcites [18] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Some of the methods listed in Table 2 do not perform automatic facial data extraction (see column 8); the others achieve this by using facial motion analysis [52], [100], [101], [73], [57].”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4 AUTOMATIC FACIAL EXPRESSION ANALYSIS
What the sources say
- Not checked yetcites [52]
- Not checked yetcites [100]
- Not checked yetcites [101] source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Independently of the kind of input imagesÐfacial images or arbitrary imagesÐdetection of the exact face position in an observed image or image sequence has been approached in two ways.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.1 Face Detection
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Two of the works surveyed in this paper perform automatic face detection in an arbitrary scene.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.1.2 Face Detection in Arbitrary Images
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The face should be without facial hair and glasses, no rigid head motion may be encountered and illumination variations must be linear for the system to work correctly.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.1.1 Face Detection in Facial Images
What the sources say
- Not checked yetcites [32]
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Steffens et al. [81] reported that their system performs well in the presence of background motion, but fails in the case of covered or too much rotated faces.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.1.2 Face Detection in Arbitrary Images
What the sources say
- Not checked yetcites [81] source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“It is not stated which method has been applied and no limitation of the used method has been reported by Yoneyama et al.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.1.1 Face Detection in Facial Images
What the sources say
- Not checked yetcites [104]
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Still, there is no psychological scrutiny on this topic.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 2.3 Facial Expression Classification
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“The camera is either mounted on a helmetlike device worn by the subject (e.g., [62], [59]) or placed in such a way that the image has the face in frontal view.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.1 Face Detection
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A first category of these utilizes a holistic or a hybrid approach to face representation (Table 5) and applies a template-based method for facial expression information extraction from an input image.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.2.1 Facial Data Extraction from Static Images: Template-Based Methods
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
In the paper
“The face should be without facial hair and glasses and in a direct face-to-face position with the camera [99].”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.1.1 Face Detection in Facial Images
What the sources say
- Not checked yetcites [99] source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The surveyed facial expression analyzers classify the encountered expression (i.e., the extracted facial expression information) either as a particular facial action or a particular basic emotion.Some of the systems perform both.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.3 Facial Expression Classification
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“The system cannot deal with minor inaccuracies of the extracted facial data and it deals merely with images of faces without facial hair or glasses.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.2.2 Facial Data Extraction from Static Images: Feature-Based Methods
What the sources say
- Not checked yetcites [62] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A first category of the surveyed methods for automatic expression analysis from static images applies a template-based method for expression classification.The methods in this category perform expression classification into a single basic emotion category.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.3.1 Expression Classification from Static Images: Template-Based Methods
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“In the approach proposed by Black and Yacoob, the initial regions for the head and the facial features were selected by hand and thereafter automatically tracked.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.2.3 Facial Data Extraction from Image Sequences: Template-Based Methods
What the sources say
- Not checked yetcites [2] source
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Zhang et al. deal only with PST Â PST pixels frontal view images of nine female Japanese subjects, manually normalized so that the distance between the eyes is 60 pixels.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.2.1 Facial Data Extraction from Static Images: Template-Based Methods
What the sources say
- Not checked yetcites [106]
plainn = 9- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“The method has been applied to frontal-view facial image sequences.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.2.3 Facial Data Extraction from Image Sequences: Template-Based Methods
What the sources say
- Not checked yetcites [24] source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“A similar face representation was recently used by Lyons et al. [51] for expression classification into the six basic plus ªneutralº emotion categories.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.2.1 Facial Data Extraction from Static Images: Template-Based Methods
What the sources say
- Not checked yetcites [51] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The used PDM has been generated from 90 facial feature points that have been manually localized in 90 images of 15 Chinese subjects showing six basic emotions (Figs. 3 and 4).”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.2.1 Facial Data Extraction from Static Images: Template-Based Methods
What the sources say
- Not checked yetcites [32]
plainn = 15- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Independent of the used classification categories, the mechanism of classification applied by a particular surveyed expression analyzer is either a template-based-or a neural-network-based-or a rule-based-classification method.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.3 Facial Expression Classification
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“However, this issue has not been discussed by Hong et al. [30].”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.2.1 Facial Data Extraction from Static Images: Template-Based Methods
What the sources say
- Not checked yetcites [30] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“However, Kimura and Yachida have not discussed this issue.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.2.3 Facial Data Extraction from Image Sequences: Template-Based Methods
What the sources say
- Not checked yetcites [41]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A first category of the surveyed approaches to automatic facial expression analysis from image sequences uses a holistic or a hybrid approach to face representation (Table 3, Table 5) and applies a template-based method for facial expression information extraction from an input image sequence.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.2.3 Facial Data Extraction from Image Sequences: Template-Based Methods
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The second category of the surveyed methods for automatic facial expression analysis from static images uses an analytic approach to face representation (Table 3, Table 5) and applies a feature-based method for expression information extraction from an input image.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.2.2 Facial Data Extraction from Static Images: Feature-Based Methods
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Zhao et al. [107] also utilize a point-based frontal-view face model but do not deal with automatic facial expression data extraction.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.2.2 Facial Data Extraction from Static Images: Feature-Based Methods
What the sources say
- Not checked yetcites [107] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The face should be without facial hair and glasses and no rigid head motion may be encountered for the method to work correctly.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.2.1 Facial Data Extraction from Static Images: Template-Based Methods
What the sources say
- Not checked yetcites [104]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Padgett and Cottrell [61] also use a holistic face representation, but they do not deal with facial expression information extraction in an automatic way.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.2.1 Facial Data Extraction from Static Images: Template-Based Methods
What the sources say
- Not checked yetcites [61] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The initial location of the FFPs in the first frame of an input image sequence is assumed to be known.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.2.3 Facial Data Extraction from Image Sequences: Template-Based Methods
What the sources say
- Not checked yetcites [95] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The performance of the detection scheme was tested on 496 dual views.Human observers in 89 percent approved when visually inspected the achieved localization of the facial features.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.2.2 Facial Data Extraction from Static Images: Feature-Based Methods
What the sources say
- Not checked yetcites [62] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A second category of the surveyed methods for automatic facial expression analysis from static images applies a neural network for facial expression classification.Except the method proposed by Zhang et al. [106], the methods belonging to this category perform facial expression classification into a single basic emotion category.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.3.2 Expression Classification from Static Images: Neural-Network-Based Methods
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The real-time system developed by Kobayashi and Hara works with online taken images of subjects with no facial hair or glasses facing the camera while siting at approximately 1m distance from it.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.2.2 Facial Data Extraction from Static Images: Feature-Based Methods
What the sources say
- Not checked yetcites [42]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The first category of the surveyed methods for automatic facial expression analysis from facial image sequences applies a template-based method for expression classification.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.3.4 Expression Classification from Image Sequences: Template-Based Methods
plainn = 100- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Also, the method does not deal with blends of emotional expressions.For example, a blend of the angry and scared expressions is recognized as disgust.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.3.5 Expression Classification from Image Sequences: Rule-Based Methods
What the sources say
- Not checked yetcites [2] source
- Not checked yetcites [3]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Some common assumptions are: the images contain frontal facial view, the illumination is constant, the light source is fixed, the face has no facial hair or glasses, the subjects are young (i.e., without permanent wrinkles), and of the same ethnicity.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 5.1 Detection of the Face and Its Features
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Mehrabian indicated that the verbal part (i.e., spoken words) of a message contributes only for 7 percent to the effect of the message as a whole, the vocal part (e.g., voice intonation) contributes for 38 percent, while facial expression of the speaker contributes for 55 percent to the effect of the spoken message [55].”
§ Automatic Analysis of Facial Expressions: The State of the Art > 1 INTRODUCTION
What the sources say
- Not checked yetcites [55] source
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“As indicated by Ellis [23], human encoding of the visual stimulus (face and its expression) may be in the form of a primal sketch and may be hardwired.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 2.2 Facial Expression Data Extraction
What the sources say
- Not checked yetcites Ellis [23]
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“In a form of rules, FACS provides a linguistic description of all possible, visually detectable, facial changes in terms of 44 so-called Action Units (AUs).”
§ Automatic Analysis of Facial Expressions: The State of the Art > 2.3 Facial Expression Classification
What the sources say
- Not checked yetcites [21]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The terms ªface-to-faceº and ªinterfaceº indicate that the face plays an essential role in interpersonal communication.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 1 INTRODUCTION
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“It is generally believed that two-gray-levels images of 100 to 200 pixels form a lower limit for detection of a face by a human observer [75], [8].”
§ Automatic Analysis of Facial Expressions: The State of the Art > 2.1 Face Detection
What the sources say
- Not checked yetcites [75] source
- Not checked yetcites [8]
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Ekman defined six such categories, referred to as the basic emotions: happiness, sadness, surprise, fear, anger, and disgust [19].”
§ Automatic Analysis of Facial Expressions: The State of the Art > 2.3 Facial Expression Classification
What the sources say
- Not checked yetcites [19] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“While the human mechanisms for face detection are very robust, the same is not the case for interpretation of facial expressions.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 2.3 Facial Expression Classification
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In practice, it may not be possible to define a face model that can satisfy both, to reflect each and every change in facial appearance and whose features are detectable in a facial image or image sequence.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 3 AN IDEAL SYSTEM FOR FACIAL EXPRESSION ANALYSIS
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“It is interesting to note that the appearance of the upper face features plays a more important role in face interpretation as opposed to lower face features [22].”
§ Automatic Analysis of Facial Expressions: The State of the Art > 2.3 Facial Expression Classification
What the sources say
- Not checked yetcites [22] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Recently, the problem of automatic face detection in an arbitrary scene has drawn great attention (e.g., see [74], [83], [85]).”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.1 Face Detection
What the sources say
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: In most real-life situations where an automated facial expression analyzer is to be employed, the location of a face in the image is not known a priori.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Nevertheless, the information about the context in which a facial expression appears is very difficult to obtain in an automatic way.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 2.3 Facial Expression Classification
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“The approaches that have been explored lately also include systems for automatic analysis and synthesis of facial expressions [86], [56], [39], [47], [89], [88], [53], [14], [19]”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4 AUTOMATIC FACIAL EXPRESSION ANALYSIS
What the sources say
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“He described each basic emotion in terms of a facial expression that uniquely characterizes that emotion.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 2.3 Facial Expression Classification
What the sources say
- Not checked yetcites [19] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“First, Ekman's description of the six prototypic facial expressions of emotion is linguistic (and, thus, ambiguous).”
§ Automatic Analysis of Facial Expressions: The State of the Art > 2.3 Facial Expression Classification
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Also, it is not at all certain that each facial expression able to be displayed on the face can be classified under the six basic emotion categories.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 2.3 Facial Expression Classification
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It is often very difficult to determine the exact nature of the expression on a person's face.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 2.3 Facial Expression Classification
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“According to Bassili [1], a trained observer can correctly classify faces showing six basic emotions with an average of 87 percent.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 2.3 Facial Expression Classification
What the sources say
- Not checked yetcites Bassili [1]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The face should be without facial hair/ glasses, no rigid head motion may be encountered, the first frame should be an expressionless face, and the facial landmark points should be marked in the first frame for the method to work correctly.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.2.4 Facial Data Extraction from Image Sequences: Feature-Based Methods
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The generalization rate was 75 percent for recognition of expression of a novel subject.”
§ Automatic Analysis of Facial Expressions: The State of the Art > TABLE 8 The Methods for Facial Expression Emotional Classification
What the sources say
- Not checked yetcites [51] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
In the paper
“The time necessary for performing a full analysis of an incoming facial image is about eight seconds.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.3.1 Expression Classification from Static Images: Template-Based Methods
What the sources say
- Not checked yetcites [30] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“There are infinitely a lot of combinations of different facial actions and their intensities that should be modeled with a finite set of templates.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.3 Facial Expression Classification
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is not known how the method will behave in the case of an unknown subject.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.3.1 Expression Classification from Static Images: Template-Based Methods
plainn = 25- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is not known how the method will behave in the case of unknown subjects.”
§ Automatic Analysis of Facial Expressions: The State of the Art > TABLE 8 The Methods for Facial Expression Emotional Classification
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The problem becomes even more difficult due to the fact that everybody has his/her own maximal intensity of displaying a certain facial action.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.3 Facial Expression Classification
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The average recognition rate was 85 percent.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.3.2 Expression Classification from Static Images: Neural-Network-Based Methods
What the sources say
- Not checked yetcites [42]
plainn = 15- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The generalization rate was 92 percent.”
§ Automatic Analysis of Facial Expressions: The State of the Art > TABLE 8 The Methods for Facial Expression Emotional Classification
What the sources say
- Not checked yetcites [51] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one. The quote only partly states this claim.
In the paper
“The dense model-graph seems very suitable for facial action coding based on the extracted deformations of the graph.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.2.1 Facial Data Extraction from Static Images: Template-Based Methods
What the sources say
- Not checked yetcites [30] source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Also, the descriptions of the emotional expressions, given in terms of facial actions, are incomplete.For example, an expression with lowered mouth corners and raised eyebrows will be classified as sadness.”
§ Automatic Analysis of Facial Expressions: The State of the Art > TABLE 8 The Methods for Facial Expression Emotional Classification
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The achieved recognition rate was 89 percent in the case of the familiar subjects and 73 percent in the case of unknown persons.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.3.1 Expression Classification from Static Images: Template-Based Methods
What the sources say
- Not checked yetcites [30] source
plainn = 25- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The average recognition rate was 92 percent.”
§ Automatic Analysis of Facial Expressions: The State of the Art > TABLE 8 The Methods for Facial Expression Emotional Classification
What the sources say
- Not checked yetcites [104]
plainn = 10- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The images used for training of the networks were also used for their testing.”
§ Automatic Analysis of Facial Expressions: The State of the Art > TABLE 8 The Methods for Facial Expression Emotional Classification
What the sources say
- Not checked yetcites [104]
plainn = 10- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The method seems suitable for facial action encoding based on the extracted deformations of the net.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.2.3 Facial Data Extraction from Image Sequences: Template-Based Methods
What the sources say
- Not checked yetcites [41]
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“The expression recognition performance of the AAM has been trained and tested on P Â PHH images of six basic emotional expressions shown by 25 subjects.The images were chosen for limited pose and lightning variation.The achieved recognition rate for the six basic and ªneutralº emotion categories was 74 percent.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.3.1 Expression Classification from Static Images: Template-Based Methods
What the sources say
- Not checked yetcites [18] source
plainn = 25- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We are doing so because a typical neural network can perform a quantified facial expression categorization into multiple classes while, in general, the template-based methods cannot achieve such a performance.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.3 Facial Expression Classification
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
In the paper
“The average recognition rate was 92 percent for the upper face AUs and 86 percent for the lower face AUs.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.3.3 Expression Classification from Static Images: Rule-Based Methods
What the sources say
- Not checked yetcites [62] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is not known how the method will behave in the case of an unknown subject.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.3.1 Expression Classification from Static Images: Template-Based Methods
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Black and Yacoob did not give a full list of the midlevel predicates and the number of different facial actions that the method can recognize is not known.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.3.5 Expression Classification from Image Sequences: Rule-Based Methods
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“The average process time was 2.5s/frame.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.3.4 Expression Classification from Image Sequences: Template-Based Methods
What the sources say
- Not checked yetcites [95] source
plainn = 8- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The quote only partly states this claim.
In the paper
“The average recognition rate was 95 percent.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.3.4 Expression Classification from Image Sequences: Template-Based Methods
What the sources say
- Not checked yetcites [95] source
plainn = 8- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
In the paper
“The dual-views used for testing of the system have been recorded under constant illumination and none of the subjects had a moustache, a beard, or wear glasses.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.3.3 Expression Classification from Static Images: Rule-Based Methods
What the sources say
- Not checked yetcites [62] source
plainn = 8- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We have identified three different but related aspects of the problem: face detection, facial expression information extraction, and facial expression classification.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 6 CONCLUSION
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A very small number of training examples (six sequences) and an insufficient diversity of the subjects (one person) have probably caused this.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.3.4 Expression Classification from Image Sequences: Template-Based Methods
hedgedconditionalSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Another appealing but still not investigated property of the human visual system is assigning a higher ªpriorityº to the upper face features than to the lower face features since they play a more important role in facial expression interpretation [22].”
§ Automatic Analysis of Facial Expressions: The State of the Art > 5.2 Facial Expression Classification
What the sources say
- Not checked yetcites [22] source
hedgeddirect- Asserted: fails — The authors may not put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The authors may not put it forward as true. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“A correct recognition ratio of 91 percent has been reported.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.3.3 Expression Classification from Static Images: Rule-Based Methods
What the sources say
- Not checked yetcites [62] source
plainn = 8- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
In the paper
“The HMM was trained on 120 image sequences, shown by two male subjects.The method was tested on image sequences shown by the same subjects.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.3.4 Expression Classification from Image Sequences: Template-Based Methods
What the sources say
- Not checked yetcites [60] source
plainn = 2- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“The accuracy of the classification was 92 percent for the eyebrow region, 88 percent for the eye region and 83 percent for the nose/ mouth region.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.3.4 Expression Classification from Image Sequences: Template-Based Methods
What the sources say
- Not checked yetcites [10] source
plainn = 100- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The method proposed by Cohn et al. deals neither with image sequences containing several facial actions in a row, nor with inaccurate facial data, nor with facial action intensity (yet the concept of the method makes it possible).”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.3.4 Expression Classification from Image Sequences: Template-Based Methods
What the sources say
- Not checked yetcites [10] source
plainn = 100- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The NN was trained and tested on the whole set of data (94 images) with 100 percent recognition rate.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.3.2 Expression Classification from Static Images: Neural-Network-Based Methods
What the sources say
- Not checked yetcites [107] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“The achieved recognition rate was 88 percent.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.3.5 Expression Classification from Image Sequences: Rule-Based Methods
What the sources say
- Not checked yetcites [2] source
- Not checked yetcites [3]
plainn = 40- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
The paper relies on this without support.
A face is perceived as a whole, not as a collection of the facial features.
“Another characteristic of the human visual system is that a face is perceived as a whole, not as a collection of the facial features.”
The paper relies on this without support.
When a face is partially occluded, humans perceive a whole face as if the perceptual system fills in the missing parts.
“When a face is partially occluded (e.g., by a hand), we perceive a whole face, as if our perceptual system fills in the missing parts.”
The paper relies on this without support.
Each person has his/her own maximal intensity of displaying a particular facial expression.
“On the other hand, each person has his/her own maximal intensity of displaying a particular facial expression.”
The paper relies on this without support.
In most real-life situations, complete immovability of the observed subject cannot be assumed.
“In most real-life situations, complete immovability of the observed subject cannot be assumed.”
The paper relies on this without support.
Considering the state-of-the-art in image processing techniques, inaccurate, noisy, and missing data could be expected.
“Considering the stateof-the-art in image processing techniques, inaccurate, noisy, and missing data could be expected.”
The paper relies on this without support.
Long delays make interaction desynchronized and less efficient.
“Long delays make the interaction desynchronized and less efficient.”
The paper relies on this without support.
In most real-life situations where an automated facial expression analyzer is to be employed, the location of a face in the image is not known a priori.
“Yet, in most of the real-life situations where an automated facial expression analyzer is to be employed (e.g., in a multimodal/media HCI), the location of a face in the image is not known a priori.”
The paper relies on this without support.
In most real-life situations, the observed subject will not remain immovable.
“Also, in most of the real-life situations, it cannot be assumed that the observed subject will remain immovable, as assumed by some methods.”
The paper relies on this without support.
Pure emotional expressions are seldom elicited.
“First, ªpureº emotional expressions are seldom elicited.”
The paper relies on this without support.
Most of the time, people show blends of emotional expressions.
“Most of the time, people show blends of emotional expressions.”
The paper relies on this without support.
Some facial actions such as blink, wink, and lips sucked into the mouth are either encountered or not.
“Second, some facial actions such as blink, wink, and lips sucked into the mouth, are either encountered or not.”
The paper relies on this without support.
Each person has his or her own maximal intensity of displaying a particular facial action.
“In addition, each person has his/her own maximal intensity of displaying a particular facial action.”
The paper relies on this without support.
FACS provides an option to score intensity on a 3-level intensity scale for only five different AUs.
“First, there are merely five different AUs for which FACS provides an option to score intensity on a 3-level intensity scale (low, medium, and high).”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
§ Automatic Analysis of Facial Expressions: The State of the Art > 6 CONCLUSION
“Also, all of the proposed approaches to automatic expression analysis perform only facial expression classification into the basic emotion categories defined by Ekman and Friesen [20].”
§ Automatic Analysis of Facial Expressions: The State of the Art > 5.2 Facial Expression Classification
“Four of the surveyed systems perform facial action coding from an input image or an image sequence (Table 7).”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Automatic Analysis of Facial Expressions: The State of the Art > 2.3 Facial Expression Classification
“Ekman defined six such categories, referred to as the basic emotions: happiness, sadness, surprise, fear, anger, and disgust [19].”
§ Automatic Analysis of Facial Expressions: The State of the Art > References
“19. P. Eisert and B. Girod, ªAnalysing Facial Expressions for Virtual Conferencing,º IEEE Trans Computer Graphics and Applications, vol. 18, no. 5, pp. 70-78, 1998.”
Citations
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A direct adaptive method for faster backpropagation learning: the RPROP algorithm
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- kawakami, 1995, 3d emotion space for interactive communication, Proc Computer Science Conf, 471
- ekman, 1978, Facial Action Coding System (FACS) Manual
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Cited by
Sorted by citations
Items 301–400 of 1,000
In the Eye of the Beholder: Changes in Perceived Emotion Expression after Smile Reanimation
Dusseldorp, J. R., Guarín, D. L., van Veen, M. M., Jowett, N., & Hadlock, T. A. (2019) · Plastic & Reconstructive Surgery · doi:10.1097/prs.0000000000005865 · Cited by 55
Facial animation parameters extraction and expression recognition using Hidden Markov Models
Pardàs, M., & Bonafonte, A. (2002) · Signal Processing: Image Communication · doi:10.1016/s0923-5965(02)00078-4 · Cited by 81
“A complete review of this techniques can be found in [24]”
BackgroundLearning Expressionlets via Universal Manifold Model for Dynamic Facial Expression Recognition
Liu, M., Shan, S., Wang, R., & Chen, X. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2615424 · Cited by 55
“Among these video-based facial expression recognition methods, one of the main concerns is how to effectively encode the dynamic information in videos.”
Overview on emotion recognition system
Varghese, A. A., Cherian, J. P., & Kizhakkethottam, J. J. (2015) · International Conference on Soft-computing and Network Security (ICSNS) · doi:10.1109/icsns.2015.7292443 · Cited by 55
“The emotion that received the best probability in the three modalities is selected in this technique [11] [12].”
MethodUSING BRAIN–COMPUTER INTERFACES TO DETECT HUMAN SATISFACTION IN HUMAN–ROBOT INTERACTION
Esfahani, E. T., & Sundararajan, V. (2011) · International Journal of Humanoid Robotics · doi:10.1142/s0219843611002356 · Cited by 55
“Position or displacement of specific points and regions of the face are used to find a correlation between emotion and facial expression.(10,11) It should be noted that using facial expression is limited to face-to-face interaction and may not always be detectable.”
BackgroundCross-database evaluation for facial expression recognition
Mayer, C., Eggers, M., & Radig, B. (2014) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661814010106 · Cited by 55
Automatic Facial Expression Recognition System Based on Geometric and Appearance Features
Youssif, A. A. A., & Asker, W. A. A. (2011) · Computer and Information Science · doi:10.5539/cis.v4n2p115 · Cited by 55
“Based on the work published (Pantic & Rothkrantz, 2000), the essential role in interpersonal communication is usually clarified by the terms “face-to-face” and “interface”.”
BackgroundAutomatic expression spotting in videos
Shreve, M., Brizzi, J., Fefilatyev, S., Luguev, T. S., Goldgof, D. B., & Sarkar, S. (2014) · Image and Vision Computing · doi:10.1016/j.imavis.2014.04.010 · Cited by 55
An Enhanced LBP Feature Based on Facial Expression Recognition
He, L., Zou, C., Zhao, L., & Hu, D. (2005) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2005.1617182 · Cited by 54
“On the other hand, in light of recent advances in image and video processing, various features have been proposed for recognizing facial expression since early 1990s [1], [2], [3], [4], [5] and a thorough survey of the existing works can be found in [6], [7], [8].”
BackgroundRegulating Feelings During Interpersonal Conflicts by Changing Voice Self-perception
Costa, J. M. D. R., Jung, M. F., Czerwinski, M. P., Guimbretière, F. V., Le, T., & Choudhury, T. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3174205 · Cited by 60
“The technology could rely on signals such as users’ voice [44], facial expressions [51] or physiological signals [32], and automatically adjust the voice feedback accordingly.”
MethodFacial Emotion Recognition Using NLPCA and SVM
Chirra, V. R. R., Reddy, U. S., & Kolli, V. K. K. (2019) · Traitement du signal · doi:10.18280/ts.360102 · Cited by 54
“In general, facial expression classification comprises of 3 steps: To detect faces, to extract features, and recognize facial expressions [4].”
BackgroundFan, W., & Bouguila, N. (2013) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2268461 · Cited by 53
“This problem is challenging and far from straightforward especially under variable illumination conditions and head motion [52], [53].”
BackgroundA Statistical Method for 2-D Facial Landmarking
Dibeklioğlu, H., Salah, A. A., & Gevers, T. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2163162 · Cited by 53
New research advances of facial expression recognition
Liu, S.-S., Tian, Y., & Li, D. (2009) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2009.5212409 · Cited by 54
“From the distance between the irises, the length of the vertical lines is empirically determined. Fig.2 Facial Characteristic Points ([ 8 ])”
BackgroundLearning partially-observed hidden conditional random fields for facial expression recognition
Chang, K.-Y., Liu, T.-L., & Lai, S. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2009.5206612 · Cited by 53
“To classify facial expressions into a set of basic emotion classes, Pantic and Rothkrantz [14] propose a rule-based system to code the emotions via action units.”
BackgroundAutomatic facial emotion recognition using weber local descriptor for e-Healthcare system
Alhussein, M. A. (2016) · Cluster Computing · doi:10.1007/s10586-016-0535-3 · Cited by 53
Jiang, S., Li, Z., Zhou, P., & Li, M. (2019) · ACM Transactions on Sensor Networks · doi:10.1145/3281630 · Cited by 53
“Existing work exploits the recognition of emotions by leveraging the acoustic [32] or the visual signals [9, 16, 45], where the facial or spoken expressions are carefully analyzed.”
BackgroundFine-grained facial expression analysis using dimensional emotion model
Zhou, F., Kong, S., Fowlkes, C. C., Chen, T., & Lei, B. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.01.067 · Cited by 55
Latent semantic analysis of facial action codes for automatic facial expression recognition
Fasel, B., Monay, F., & Gática-Pérez, D. (2004) · doi:10.1145/1026711.1026742 · Cited by 55
“Automatic facial expression recognition is an active research area with applications in human-computer interfaces and human emotion analysis [15][10].”
BackgroundFacial expression recognition using encoded dynamic features
Yang, P., Liu, Q., Cui, X., & Metaxas, D. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587717 · Cited by 52
“The previous work can be categorized into two classes: image based methods and video based methods [8] [19] [30].”
Discriminant Graph Structures for Facial Expression Recognition
Zafeiriou, S. P., & Pitas, I. (2008) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2008.2007292 · Cited by 51
“The interested reader may refer to [4]–[6] and in the references therein for facial expression recognition methods.”
Background4D facial expression recognition
Fang, T., Zhao, X., Shah, S. K., & Kakadiaris, I. A. (2011) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2011.6130440 · Cited by 51
“Comprehensive surveys in this area include those by Fasel and Luettin [8], Pantic et al. [22], and Zeng et al. [39].”
BackgroundSantos, O. C., Saneiro, M., Boticario, J. G., & Rodriguez‐Sanchez, M. C. (2015) · New Review of Hypermedia and Multimedia · doi:10.1080/13614568.2015.1058428 · Cited by 51
“…as well as in the voice status, can relate to positive and negative affective states (Gnjatovic & Rosner, 2010; Kleinsmith & Bianchi-Berthouze, 2013; Pantie & Rothkrantz, 2000; Schindler, Van Gool, & De Gelder, 2008), influencing engagement and learning achievement (D ’ Mello & Graesser, 2010).”
BackgroundA Comprehensive Empirical Study on Linear Subspace Methods for Facial Expression Analysis
Shan, C., Gong, S., & McOwan, P. W. (2006) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2006.13 · Cited by 51
“Automatic facial expression analysis is a vital component of intelligent Human-Computer Interaction (HCI), and has attracted much attention in recent years (see, for instance, the surveys [ 14 , 6]).”
BackgroundFacial expression recognition in virtual reality environments: challenges and opportunities
Zhang, Z., Mir, J. M. F., & Mateu, L. G. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1280136 · Cited by 50
Authentic Emotion Detection in Real-Time Video
Sun, Y., Sebe, N., Lew, M. S., & Gevers, T. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-24837-8_10 · Cited by 50
“The two recent surveys in the area [12, 13] provide an in depth review of many of the research done in recent years.”
Dibeklioğlu, H., Valenti, R., Salah, A. A., & Gevers, T. (2010) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1873951.1874056 · Cited by 50
“The literature on automated recognition of facial expressionsis is extensive, we refer the reader to [11] and to the more recent [16].”
BackgroundRostaminia, S., Lamson, A., Maji, S., Rahman, T., & Ganesan, D. (2019) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3314410 · Cited by 50
“There is a vast body of literature on using computer vision based approaches to detect facial actions and expressions (surveyed in [6, 15, 37, 48]).”
BackgroundFusing dynamic deep learned features and handcrafted features for facial expression recognition
Fan, X., & Tjahjadi, T. (2019) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2019.102659 · Cited by 50
“Nevertheless, since facial expressions are subtle, complex and variable, accurate FER still poses a challenging problem [10] and [11].”
BackgroundDetecting social interactions of the elderly in a nursing home environment
Chen, D., Yang, J., Malkin, R. E., & Wactlar, H. D. (2007) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/1198302.1198308 · Cited by 50
“…al. 2003], hand gesture analysis [Freeman and Roth 1995; Kirishima et al. 2005], facial expression understanding [Essa and Pentland 1995; Pantic and Rothkrantz 2000], sitting, standing, and walking analyses [Lee and Mase 2002; Ramanan and Forsyth 2003; Gavrila 1999], and speech detection…”
BackgroundInfluentialEmotion recognition with consideration of facial expression and physiological signals
Chang, C., Tsai, J.-S., Wang, C., & Chung, P. (2009) · IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) · doi:10.1109/cibcb.2009.4925739 · Cited by 55
Facial Expression Recognition via a CBAM Embedded Network
Cao, W., Feng, Z., Zhang, D., & Huang, Y. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.06.115 · Cited by 52
BAUM-2: a multilingual audio-visual affective face database
Erdem, Ç. E., Turan, C., & Aydın, Z. (2014) · Multimedia Tools and Applications · doi:10.1007/s11042-014-1986-2 · Cited by 49
“In the last decade many studies have been published on facial expressions recognition, which are summarized in several survey papers [21, 24, 39, 40, 58].”
BackgroundSpatiotemporal feature extraction for facial expression recognition
Kamarol, S. K. A., Jaward, M. H., Parkkinen, J. P. S., & Parthiban, R. (2016) · IET Image Processing · doi:10.1049/iet-ipr.2015.0519 · Cited by 52
Review of Face Recognition Techniques
Solanki, K., & Pittalia, P. P. (2016) · International Journal of Computer Applications · doi:10.5120/ijca2016907994 · Cited by 49
3D facial expression recognition using kernel methods on Riemannian manifold
Hariri, W., Tabia, H., Farah, N., Benouareth, A., & Declercq, D. (2017) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2017.05.009 · Cited by 50
Deep Multi-task Learning to Recognise Subtle Facial Expressions of Mental States
Hu, G., Liu, L., Yuan, Y., Yu, Z., Yang, H., Zhang, Z., Shen, F., Shao, L., Hospedales, T. M., Robertson, N., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01258-8_7 · Cited by 51
“The classic problem Our dataset contains mental states that are richer – including a variety of cognitive states, conveyed by subtle expressions, and exhibited in the wild. is to recognise six basic emotions [39] (Fig.”
BackgroundFacial expression recognition using distance and texture signature relevant features
Barman, A., & Dutta, P. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.01.011 · Cited by 49
“Comprehensive survey on the facial expression recognition is available in [4] and [5].”
Affective negotiation support systems
Broekens, J., Jonker, C. M., & Meyer, J. C. (2010) · Journal of Ambient Intelligence and Smart Environments · doi:10.3233/ais-2010-0065 · Cited by 47
“A large body of research exists on implicit meth-ods to automatically recognize human emotion in a wide variety of modalities [38,40,68,118,120].”
An automatic 3D expression recognition framework based on sparse representation of conformal images
Zeng, W., Li, H., Chen, L., Morvan, J.-M., & Gu, X. D. (2013) · 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG) · doi:10.1109/fg.2013.6553749 · Cited by 48
“Despite the great advancements has achieved, 2D/2D video facial texture image based facial expression recognition system still fails to solve the two major challenges: illumination changes and pose variations [7], [20], [17], [41].”
BackgroundModeling facial expression space for recognition
Wu, Y., Liu, H., & Zha, H. (2005) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2005.1545532 · Cited by 48
“But most of those methods use shape features to recognize Action Units (AU) [12,13].”
MethodFacial Expression Recognition Influenced by Human Aging
Guo, G., Guo, R., & Li, X. (2013) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2013.13 · Cited by 48
“Two databases from the psychology society are introduced to the computer vision community and used for our study.”
MethodFacial expression (mood) recognition from facial images using committee neural networks
Kulkarni, S., Reddy, N. P., & Hariharan, S. I. (2009) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-8-16 · Cited by 50
“Table 5 presents a summary of results reported in the literature [5,7,9,10,17,18,20-23].”
BackgroundAutomatic facial expression recognition based on spatiotemporal descriptors
Ji, Y., & Idrissi, K. (2012) · Pattern Recognition Letters · doi:10.1016/j.patrec.2012.03.006 · Cited by 48
“The early work was summarized by Pantic and Rothkrantz (2000) since one of the first attempts for FER system done by Suwa et al. (1978).”
BackgroundSemantic-based facial expression recognition using analytical hierarchy process
Cheng, S., Chen, M.-Y., Chang, H., & Chou, T. (2006) · Expert Systems with Applications · doi:10.1016/j.eswa.2006.04.019 · Cited by 49
Non-frontal view facial expression recognition based on ergodic hidden Markov model supervectors
Tang, H., Hasegawa‐Johnson, M., & Huang, T. S. (2010) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2010.5582576 · Cited by 48
“N(·|μ [1] ik ,Σ [1] ik )‖N(·|μ [2] ik ,Σ [2] ik ) )”
BackgroundInfluentialTransfer subspace learning for cross-dataset facial expression recognition
Yan, H. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.11.113 · Cited by 48
Crossing Domains for AU Coding: Perspectives, Approaches, and Measures
Ertuğrul, I. Ö., Cohn, J. F., Jeni, L. A., Zhang, Z., Yin, L., & Ji, Q. (2020) · IEEE Transactions on Biometrics Behavior and Identity Science · doi:10.1109/tbiom.2020.2977225 · Cited by 49
“Automatic detection of FACS action units has been an active area of research [3], [4], [5].”
BackgroundManipulating Video Sequences to Determine the Components of Conversational Facial Expressions
Cunningham, D. W., Kleiner, M., Wallraven, C., & Bülthoff, H. (2005) · ACM Transactions on Applied Perception · doi:10.1145/1077399.1077404 · Cited by 48
Situated Preferences and Preference Repositories for Personalized Database Applications
Holland, S., & Kießling, W. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30464-7_39 · Cited by 48
“We know with mimic recognition [21] that she is feeling satisfied.”
Automatic facial emotion recognition
Azcarate, A., Hageloh, F., Koen Van De, S., & Valenti, R. (2005) · Cited by 48
Recognition of Facial Expressions with Principal Component Analysis and Singular Value Decomposition
Kaur, M., Vashisht, R., & Neeru, N. (2010) · International Journal of Computer Applications · doi:10.5120/1434-1933 · Cited by 47
Hossain, S., Umer, S., Asari, V. K., & Rout, R. K. (2021) · Applied Sciences · doi:10.3390/app11199174 · Cited by 47
“In contrast, some [17] template-based and feature-based approaches have also been investigated.”
BackgroundCross-domain AU Detection: Domains, Learning Approaches, and Measures
Ertuğrul, I. Ö., Cohn, J. F., Jeni, L. A., Zhang, Z., Yin, L., & Ji, Q. (2019) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2019.8756543 · Cited by 47
“been an active area of research [3], [4], [5].”
Rajendra Kurup, A., Ajith, M., & Martínez Ramón, M. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.08.029 · Cited by 49
Facial action recognition in face profile image sequences
Pantić, M., Patras, I., & Rothkruntz, L. (2003) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2002.1035712 · Cited by 46
“Most approaches to automatic facial expression analysis attempt to recognize a small set of prototypic emotional facial expressions, i.e., fear, sadness, disgust, anger, surprise and happiness [3].”
BackgroundMethodInfluentialExtreme Learning Machine Ensemble Using Bagging for Facial Expression Recognition
Ghimire, D., & Lee, J.-W. (2014) · Journal of Information Processing Systems · doi:10.3745/jips.02.0004 · Cited by 47
“Another detailed survey on the automatic analysis of facial expressions was recently presented by Pantic and Rothkrantz [15].”
BackgroundBhowmik, P. K., Mitra, P., & Basu, A. (2008) · doi:10.3115/1611628.1611637 · Cited by 46
Sun, B., Li, L., Wu, X., Zuo, T., Chen, Y., Zhou, G., He, J. Y. D., & Zhu, X. (2015) · Journal on Multimodal User Interfaces · doi:10.1007/s12193-015-0203-6 · Cited by 46
“At the early stage, researchers focused mostly on emotion analysis from single static facial images under constrained circumstances [2].”
BackgroundFacial expression recognition techniques: a comprehensive survey
Rajan, S., Chenniappan, P., Devaraj, S., & Madian, N. (2019) · IET Image Processing · doi:10.1049/iet-ipr.2018.6647 · Cited by 49
Adaptive Deep Disturbance-Disentangled Learning for Facial Expression Recognition
Ruan, D., Mo, R., Yan, Y., Chen, S., Xue, J., & Wang, H. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-021-01556-7 · Cited by 46
A Review on Facial Micro-Expressions Analysis: Datasets, Features and MetricsPreprint
Merghani, W., Davison, A. K., & Yap, M. H. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.02397 · Cited by 65
“For review in macro-expressions, please refer to [17], [18].”
BackgroundOuyang, Y., Sang, N., & Huang, R. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.03.073 · Cited by 45
AffectButton: Towards a standard for dynamic affective user feedback
Broekens, J., & Brinkman, W. (2009) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2009.5349347 · Cited by 45
“Further, emotion recognition has been a topic of research for quite some time in the area of affective computing (See ,e.g., [20] [21] [22]).”
BackgroundWei, X., Li, H., Sun, J. Z., & Chen, L. (2018) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2018.00015 · Cited by 44
“brow raiser, lip tightener, and mouth stretch, using static or dynamic 2D face images [1].”
BackgroundUnderstanding cartoon emotion using integrated deep neural network on large dataset
Jain, N., Gupta, V., Shubham, S., Madan, A., Chaudhary, A., & Santosh, K. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06003-9 · Cited by 45
“Facial expressions are the significant contributor to interpersonal communication [43].”
BackgroundHuman-Robot Interaction Using Affective Cues
Liu, C., Rani, P., & Sarkar, N. (2006) · IEEE RO-MAN · doi:10.1109/roman.2006.314431 · Cited by 45
FACIAL BIOMETRICS USING NONTENSOR PRODUCT WAVELET AND 2D DISCRIMINANT TECHNIQUES
Zhang, D., You, X., Wang, P. S. P., Yanushkevich, S. N., & Tang, Y. Y. (2009) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001409007260 · Cited by 44
Feature level analysis for 3D facial expression recognition
Sha, T., Song, M., Bu, J., Chen, C., & Tao, D. (2011) · Neurocomputing · doi:10.1016/j.neucom.2011.01.008 · Cited by 44
Gharavian, D., Bejani, M., & Sheikhan, M. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-015-3180-6 · Cited by 44
“In the survey of Pantic and Rothkrantz [39], only four studies were found to focus on audio-visual affect recognition.”
BackgroundKabir, M. H., Jabid, T., & Chae, O. (2012) · Cited by 44
Aran, O., Bürger, T., Akarun, L., & Caplier, A. (2008) · Springer eBooks · doi:10.1007/978-3-540-78345-9 · Cited by 44
Sun, Y., Reale, M. J., & Yin, L. (2008) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.2008.4813336 · Cited by 46
“Many successful approaches have utilized action unit (AU) recognition [27, 20, 33, 2, 11, 16] or motion unit (MU) detection [5, 24, 36].”
BackgroundEmotion based mood enhancing music recommendation
Iyer, A. V., Pasad, V., Sankhe, S. R., & Prajapati, K. (2017) · IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology · doi:10.1109/rteict.2017.8256863 · Cited by 45
“Pantic and Rothkrantz [12] proposed system which processes images of frontal and profile face view.”
BackgroundAutomatic Facial Feature Extraction and Expression Recognition based on Neural Network
P, S., Thool, R. C., & Khandait, P. D. (2011) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2011.020117 · Cited by 44
Computerized measurement of facial expression of emotions in schizophrenia
Alvino, C. V., Kohler, C. G., Barrett, F. S., Gur, R. E., Gur, R. C., & Verma, R. (2007) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.03.002 · Cited by 44
“2 Present address: Hospital of the University of Pennsylvania, 3400 Spruce treet, 10th Floor, Gates Building Philadelphia, PA 19104, United States. el.”
BackgroundInfluentialThe components of conversational facial expressions
Cunningham, D. W., Kleiner, M., Bülthoff, H., & Wallraven, C. (2004) · Symposium on Applied Perception in Graphics and Visualization (APGV) · doi:10.1145/1012551.1012578 · Cited by 44
“Within these (cid:2)elds, an impressive variety of representational systems have been developed to describe facial expressions (see, e.g., [Sayette et al. 2001]).”
Background2003: Designing, Playing, and Performing with a Vision-Based Mouth Interface
Lyons, M. J., Hähnel, M., & Tetsutani, N. (2017) · Current research in systematic musicology · doi:10.1007/978-3-319-47214-0_8 · Cited by 44
Facial Expression Recognition: A Fully Integrated Approach
Valenti, R., Sebe, N., & Gevers, T. (2007) · doi:10.1109/iciapw.2007.25 · Cited by 44
“The two recent surveys in the area [16, 8] provide an in depth review of the existing approaches towards automatic facial expression recognition.”
BackgroundFacial Affect “In-the-Wild”: A Survey and a New Database
Zafeiriou, S. P., Papaioannou, A., Kotsia, I., Nicolaou, M. A., & Zhao, G. (2016) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2016.186 · Cited by 45
“The interested reader may refer to [68, 25] for a comprehensive overview of the first methods in literature tackling this problem.”
BackgroundLi, M., Chau, P. Y. K., & Ge, L. (2020) · Internet Research · doi:10.1108/intr-02-2020-0094 · Cited by 50
On assisting a visual-facial affect recognition system with keyboard-stroke pattern information
Σταθοπούλου, Ι., Alepis, E., Tsihrintzis, G. A., & Virvou, M. K. (2009) · Knowledge-Based Systems · doi:10.1016/j.knosys.2009.11.007 · Cited by 43
Multimodal affective state recognition in serious games applications
Psaltis, A., Kaza, K., Stefanidis, K., Thermos, S., Apostolakis, K. C., Dimitropoulos, K., & Daras, P. (2016) · IEEE International Conference on Imaging Systems and Techniques (IST) · doi:10.1109/ist.2016.7738265 · Cited by 45
Identifying Human Emotions from Facial Expressions with Deep Learning
Phavish, B., Suddul, G., Armoogum, S., & Foogooa, R. (2020) · Zooming Innovation in Consumer Technologies Conference (ZINC) · doi:10.1109/zinc50678.2020.9161445 · Cited by 43
“Two methods can be used for feature extraction: Analytic approach, Holistic approach [18].”
MethodToward multimodal fusion of affective cues
Paleari, M., & Lisetti, C. (2006) · doi:10.1145/1178745.1178762 · Cited by 43
“Other researchers have developed systems to extract affective information from video signals (see [42, 41, 40] for surveys of the current state of the art).”
BackgroundZhu, J.-Y., Agarwala, A., Efros, A. A., Shechtman, E., & Wang, J. (2014) · ACM Transactions on Graphics · doi:10.1145/2661229.2661287 · Cited by 42
“There is significant work in the computer vision literature on the automatic recognition of facial expressions [Pantic and Rothkrantz 2000]; most of this work focuses on FACS recognition.”
BackgroundEmotion Classification Using Facial Expression
Arumugam, D., & S. (2011) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2011.020714 · Cited by 43
Constrained Maximum Likelihood Learning of Bayesian Networks for Facial Action Recognition
de Campos, C. P., Tong, Y., & Ji, Q. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88690-7_13 · Cited by 41
“Although we use a simpler model and fewer training data than state-of-the-art algorithms do, inferences with our networks display recognition rates that are comparable to other results [7,17,18,19].”
BackgroundResultAnalyzing Facial Expressions Using Intensity-Variant 3D Data For Human Computer Interaction
Yin, L., Wei, X., Longo, P., & Bhuvanesh, A. (2006) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2006.271 · Cited by 42
“The 2D-based approaches are successful [8, 11, 5, 4, 1, 14], however, they are limited by the capability of evaluating facial surface features and pose variations in 3D space.”
BackgroundGüneş, H., & Hung, H. (2016) · Image and Vision Computing · doi:10.1016/j.imavis.2016.03.013 · Cited by 42
Learning Local Objective Functions for Robust Face Model Fitting
Wimmer, M., Stulp, F., Pietzsch, S., & Radig, B. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.70793 · Cited by 43
“For comprehensive overviews of current research, we refer to [39] and [5].”
BackgroundAn exposition of facial expression recognition techniques
Saeed, S., Mahmood, M. K., & Khan, Y. D. (2016) · Neural Computing and Applications · doi:10.1007/s00521-016-2522-2 · Cited by 42
Feature selection for facial emotion recognition using late hill-climbing based memetic algorithm
Ghosh, M., Kundu, T., Ghosh, D., & Sarkar, R. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-07811-x · Cited by 41
An Automatic Framework for Textured 3D Video-Based Facial Expression Recognition
Hayat, M., & Bennamoun, M. (2014) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2014.2330580 · Cited by 41
“Local video-patches of variable lengths are extracted from numerous locations of the training videos and represented as points on the Grassmannian manifold.”
BackgroundLow-resolution facial expression recognition: A filter learning perspective
Yan, Y., Zhang, Z., Chen, S., & Wang, H. (2019) · Signal Processing · doi:10.1016/j.sigpro.2019.107370 · Cited by 43
Emotion recognition by two view SVM_2K classifier on dynamic facial expression features
Meng, H., Romera‐Paredes, B., & Bianchi‐Berthouze, N. (2011) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2011.5771362 · Cited by 40
“Pantic and Rothkrantz [21] provide an in depth review of studies covering these three types of approaches.”
BackgroundFace expression recognition: A brief overview of the last decade
Căleanu, C. D. (2013) · IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI) · doi:10.1109/saci.2013.6608958 · Cited by 40
“For comprehensive surveys of the past efforts in the field, readers are referred to [2], [3] or [4].”
BackgroundIntelligent human interface based on mental cloning-based software
Fujita, H., Hakura, J., & Kurematu, M. (2008) · Knowledge-Based Systems · doi:10.1016/j.knosys.2008.11.005 · Cited by 40
“One of the most popular approaches to automatic facial expression analysis is relying on the facial action coding system (FACS) (for FACS, see Ekman and Friesen [4]; for a survey of the literature see [19].”
MethodRázuri, J. G., Sundgren, D., Rahmani, R., & Morán, A. (2013) · Mexican International Conference on Artificial Intelligence · doi:10.1109/micai.2013.16 · Cited by 40
“A comprehensive review related with the analysis of facial expressions can be found in [23].”
BackgroundKundu, T., & Chandran, S. (2017) · International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT) · doi:10.1109/iceeccot.2017.8284512 · Cited by 40
“Humans are able to detect faces and interpret emotional expressions on them without any serious difficulty [2].”
Background
Metadata
- Authors
- Pantie, M., & Rothkrantz, L. J. M.
- Affiliations
- Delft University of Technology (NL)
- ORCID
- Rothkrantz, L.J.M.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2000
- Volume
- 22
- Issue
- 12
- Pages
- 1424-1445
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2159017231
- Semantic Scholar
- 087a6f471b5177014b08a342968901e1ef083ed1
- Cited by
- 1,778
- References
- 111
- Citations by year
- 2026: 33 · 2025: 31 · 2024: 30 · 2023: 43 · 2022: 41 · 2021: 43 · 2020: 58 · 2019: 65 · 2018: 72 · 2017: 71 · 2016: 86 · 2015: 87 · 2014: 105 · 2013: 105 · 2012: 108
- FWCI
- 23.88
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- Research Repository (Delft University of Technology) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- facial expression analysis, face detection, facial expression recognition, emotion classification, human visual system, human-machine interaction, real-time systems
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