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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Cited by
Sorted by citations
Items 601–700 of 1,000
Analyzing Facial Expression by Fusing Manifolds
Chang, W.-Y., Chen, C., & Hung, Y. (2007) · Springer eBooks · doi:10.1007/978-3-540-76390-1_61 · Cited by 19
“More detailed surveys about facial expression analysis can be found in [8, 12].”
BackgroundTowards Automatic Recognition of Spontaneous Facial Actions
Movellan, J. R., Littlewort, G. C., Braathen, B., Frank, M. G., B, T. J. S. A., & Darwin, C. (2003) · Cited by 19
Emotion inference from human body motion
Bernhardt, D. (2021) · OpenGrey (Institut de l'Information Scientifique et Technique) · doi:10.48456/tr-787 · Cited by 19
Robust facial expressions recognition using 3D average face and ameliorated adaboost
Chen, J., Ariki, Y., & Takiguchi, T. (2013) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2502081.2502173 · Cited by 18
“So facial expressions recognition is an interesting research topic, and many researchers attach great importance to this field [6].”
BackgroundCS-3DLBP and geometry based person independent 3D facial action unit detection
Bayramoğlu, N., Zhao, G., & Pietikäinen, M. (2013) · International Conference on Biometrics · doi:10.1109/icb.2013.6612977 · Cited by 19
“We refer reader to the comprehensive surveys in this area such as [16, 28] and [7].”
BackgroundFacial Expression Recognition Using Facial-component-based Bag of Words and PHOG Descriptors
Li, Z., Imai, J., & Kaneko, M. (2010) · The Journal of The Institute of Image Information and Television Engineers · doi:10.3169/itej.64.230 · Cited by 18
A Novel Feature Extraction Method for Facial Expression Recognition
Feng, X., Lv, B., Li, Z., & Zhang, J. (2006) · Advances in intelligent systems research/Advances in Intelligent Systems Research · doi:10.2991/jcis.2006.259 · Cited by 18
Facial Affect Recognition Using Regularized Discriminant Analysis-Based Algorithms
Lee, C.-C., Huang, S.-S., & Shih, C.-Y. (2010) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/2010/596842 · Cited by 18
“Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010, Article ID 596842, 10 pages doi:10.1155/2010/596842”
BackgroundInfluentialRobust automatic facial expression detection method based on sparse representation plus LBP map
Ouyang, Y., Sang, N., & Huang, R. (2013) · Optik · doi:10.1016/j.ijleo.2013.05.076 · Cited by 18
Gaussian mixture model based estimation of the neutral face shape for emotion recognition
Ulukaya, S., & Erdem, Ç. E. (2014) · Digital Signal Processing · doi:10.1016/j.dsp.2014.05.013 · Cited by 18
“lighted in several survey papers [2–6].”
Automatic Facial Expression Recognition Based on Local Binary Patterns of Local Areas
Liu, W., Li, S., & Wang, Y. (2009) · WASE International Conference on Information Engineering · doi:10.1109/icie.2009.36 · Cited by 18
“Keywords-facial expression recognition, local binary patterns, LLBP, facial feature extraction, eye location I. INTRODUCTION Facial expression plays a great role in human-computer interaction.”
BackgroundFACIAL EXPRESSION RECOGNITION BASED ON LOCAL BINARY PATTERN FEATURES AND SUPPORT VECTOR MACHINE
Cao, N. T., Ton-That, A. H., & Choi, H. I. (2014) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001414560126 · Cited by 18
Vision-Based Technique and Issues for Multimodal Interaction in Augmented Reality
Ismail, A. W., Billinghurst, M., & Sunar, M. S. (2015) · International Symposium on Visual Information Communication and Interaction (VINCI) · doi:10.1145/2801040.2801058 · Cited by 18
“Most facial expression recognition research [42] on coding facial expressions based on the basic movements of facial features.”
BackgroundEmotion recognition using eigenvalues and Levenberg–Marquardt algorithm-based classifier
Gaidhane, V. H., Hote, Y. V., & Singh, V. (2016) · Sadhana · doi:10.1007/s12046-016-0479-6 · Cited by 18
“In the past, different techniques have been proposed to classify facial emotions, such as neural network [19, 20], support vector machines (SVM) [21], Bayesian network [22], and fuzzy rule-based classifiers [23, 24].”
Emotion recognition from face dataset using deep neural nets
Das, D., & Chakrabarty, A. (2016) · International Conference on Innovations in Intelligent Systems and Applications (INISTA) · doi:10.1109/inista.2016.7571861 · Cited by 18
“A task of evaluation of facial expression/emotion can be done in three main steps [11]: A) Detection of the face.”
MethodHigh-Level Geometry-based Features of Video Modality for Emotion Prediction
Weber, R., Barrielle, V., Soladié, C., & Séguier, R. (2016) · International Workshop on Audio/Visual Emotion Challenge (AVEC) · doi:10.1145/2988257.2988262 · Cited by 18
“Since non-verbal cues are an important part of communication [23] and affect display, great interest has been carried to facial expression analysis [25].”
BackgroundImage Segmentation Based on Minimal Spanning Tree and Cycles
Janakiraman, T. N., & Mouli, C. (2007) · International Conference on Computational Intelligence and Multimedia Applications (ICCIMA) · doi:10.1109/iccima.2007.54 · Cited by 18
Exploring Domain Knowledge for Facial Expression-Assisted Action Unit Activation Recognition
Wang, S., Peng, G., & Ji, Q. (2018) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2822303 · Cited by 18
“WITH the development of user-centered humancomputer interaction, automatic facial expression analysis has attracted increasing attention in the fields of human behavior research [1] and computer vision [2], [3], [4], [5], since facial expression is one of the most important chan-”
MethodFace detection and tracking using hybrid margin-based ROI techniques
Rehman, B., Ong, W.-H., Tan, A. C. H., & Ngo, T. D. (2019) · The Visual Computer · doi:10.1007/s00371-019-01649-y · Cited by 18
“, gender, age, and beauty) [17–20], behavior analysis for face [21], face morphing and relighting [22], shape reconstruction for faces [23], retrieval and organization of image and video within photo albums [24].”
BackgroundA simple approach to facial expression recognition
Su, M., Hsieh, Y.-J., & Huang, D.-Y. (2007) · International Conference on Computer Engineering and Applications · Cited by 18
Real Time Facial Expression Recognition Using a Novel Method
Srivastava, S. (2012) · The International journal of Multimedia & Its Applications · doi:10.5121/ijma.2012.4204 · Cited by 18
Quantifying Facial Expression Abnormality in Schizophrenia by Combining 2D and 3D Features
Wang, P., Kohler, C. G., Barrett, F. S., Gur, R. E., Gur, R. C., & Verma, R. (2007) · 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.2007.383061 · Cited by 18
Texture based Emotion Recognition from Facial Expressions using Support Vector Machine
Punitha, A. A. A., & Geetha, M. K. (2013) · International Journal of Computer Applications · doi:10.5120/13854-1715 · Cited by 18
“Detailed review of existing methods on facial expression is seen in [6, 7, 8].”
Classifying Emotion in News Sentences: When Machine Classification Meets Human Classification
Bhowmick, P. K., Basu, A., & Mitra, P. (2010) · Cited by 18
Geometric positions and optical flow based emotion detection using MLP and reduced dimensions
Khan, G., Siddiqi, A., Khan, M. U. G., Wahla, S. Q., & Samyan, S. (2019) · IET Image Processing · doi:10.1049/iet-ipr.2018.5728 · Cited by 18
Xu, D., Yan, S., Zhang, L., Liu, Z., & Zhang, H. (2004) · Cited by 18
Real-time facial expression recognition based on boosted embedded hidden Markov model
Zhou, X., Huang, X.-S., Xu, B., & Wang, Y.-S. (2004) · International Conference on Image and Graphics · doi:10.1109/icig.2004.118 · Cited by 14
“The two major contributions of this work are: First, we proposed a novel network structure and parameters learning algorithm for embedded HMM [1] based on AdaBoost [2].”
MethodDevelopment of the calf grimace scale for pain and stress assessment in castrated Angus beef calves
Farghal, M., Pajor, E. A., Luna, S. P. L., Pang, D., Windeyer, M. C., & Ceballos, M. C. (2024) · Scientific Reports · doi:10.1038/s41598-024-77147-6 · Cited by 17
“To our knowledge, the highest scores for facial expressions that can be obtained under certain conditions have not been reported in animals, but have been reported for adult humans 56 and infants, in cases of joy, anger and sadness 57,58 .”
BackgroundMulti-label learning with prior knowledge for facial expression analysis
Zhao, K., Zhang, H., Ma, Z., Song, Y.-Z., & Guo, J. H. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.01.005 · Cited by 17
Sohail, A. S. M., & Bhattacharya, P. (2011) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001411008762 · Cited by 17
Zia, M. S., & Jaffar, M. A. (2013) · Multimedia Tools and Applications · doi:10.1007/s11042-013-1803-3 · Cited by 17
“A number of methods that have been developed for facial expression analysis include measurement of facial motion through optic flow [8,23] and analysis of surface textures.”
MethodNeuro-Fuzzy Quantification of Personal Perceptions of Facial Images Based on a Limited Data Set
Diago, L., Kitaoka, T., Hagiwara, I., & Kambayashi, T. (2011) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2011.2176349 · Cited by 17
“Most of the vision-based affect recognition studies (see survey in [29]) focus on facial expression analysis [11]–[14] due to the importance of the face in emotion expression and perception.”
InfluentialFacial recognition system for driver vigilance mlonitoring
Dikkers, H. J., Spaans, M. A., Daten, D., Novak, M., & Rothkrantz, L. M. (2005) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2004.1400934 · Cited by 17
“All facial expression recognition models can be placed within a three-tier framework, as described by Pantic and Rothkrantz [ 6 ].”
BackgroundMethodInfluentialSelf-citationEmotion in Robots Using Convolutional Neural Networks
Ghayoumi, M., & Bansal, A. K. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-47437-3_28 · Cited by 18
Benson, D., Khan, M. M., Tan, T., & Hargreaves, T. (2016) · International Conference on Advanced Robotics and Mechatronics (ICARM) · doi:10.1109/icarm.2016.7606898 · Cited by 17
“For example in [6], following the principles outlined in [6-8], adherence to standard display rules is evident during the robot-human proto-dialog.”
BackgroundUsing Kinect for Facial Expression Recognition under Varying Poses and Illumination
Malawski, F., Kwolek, B., & Sako, S. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-09912-5_33 · Cited by 17
“Most of the previous work focused primarily on 2D domain, see survey [1].”
MethodRETRACTED CHAPTER: Real-Life Facial Expression Recognition Systems: A Review
Goyal, S., Upadhyay, A., Jadon, R. S., & Goyal, R. (2018) · Smart innovation, systems and technologies · doi:10.1007/978-981-10-5544-7_31 · Cited by 17
RetractedFeature Guided CNN for Baby’s Facial Expression Recognition
Lin, Q., He, R., & Jiang, P. (2020) · Complexity · doi:10.1155/2020/8855885 · Cited by 17
Facial Expression Invariants for Estimating Mental States of Person
Hakura, J., Fujita, H., & Kurematsu, M. (2009) · Frontiers in artificial intelligence and applications · doi:10.3233/978-1-60750-049-0-518 · Cited by 17
Facial expression recognition in the presence of occlusion using local Gabor binary patterns
Azmi, R., & Yegane, S. (2012) · Iranian Conference on Electrical Engineering · doi:10.1109/iraniancee.2012.6292452 · Cited by 17
Comparative Study of Facial Expression Recognition Techniques
Kaur, M., & Vashisht, R. (2011) · International Journal of Computer Applications · doi:10.5120/1741-2368 · Cited by 17
“The optimum facial feature extraction algorithm, Canny Edge Detector, is applied to localize face images, and a hierarchical clustering-based scheme reinforces the search region of extracted highly textured facial clusters[8].”
MethodRecognizing facial expressions at low resolution
Shan, C., Gong, S., & McOwan, P. W. (2006) · IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) · doi:10.1109/avss.2005.1577290 · Cited by 18
“Due to its potential applications, such as human-computer interaction and visual surveillance, automatic facial expression recognition has attracted much attention [2, 3] over the past two decades, and much progress has been made [4, 5, 6, 7, 8, 9, 10, 11].”
BackgroundA New Approach to Implicit Human-Robot Interaction Using Affective Cues
Rani, P., & Sarkar, N. (2006) · doi:10.5772/4693 · Cited by 17
A new information fusion method for SVM-based robotic audio-visual emotion recognition
Han, M.-J., Hsu, J. I.-H., Song, K., & Chang, F. (2007) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2007.4413990 · Cited by 16
“The majority of studies focus on facial expression recognition [2], [ 3 ] and speech signal processing [4], [5].”
BackgroundA model-based facial expression recognition algorithm using Principal Components Analysis
Vretos, N., Nikolaidis, N., & Pitas, I. (2009) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2009.5413959 · Cited by 17
“An overview of the state of the art can be found in [2] In this paper, we propose a novel model-based facial expression technique which utilizes the Candide facial grid (Figure 1), deformed so as to match the apex of an expression.”
BackgroundMethodBezawada, S., Hu, Q., Gray, A., Brick, T. R., & Tucker, C. S. (2017) · Journal of Mechanical Design · doi:10.1115/1.4035428 · Cited by 17
“The most current approaches to computer-based analysis of human affective states are limited to either face images or the speech signals [40].”
Background3-Dimensional facial expression recognition in human using multi-points warping
Agbolade, O., Nazri, A., Yaakob, R., Ghani, A., & Cheah, Y. K. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3153-2 · Cited by 17
Amin, M. A., Afzulpurkar, N., Dailey, M. N., Esichaikul, V., & Batanov, D. N. (2005) · Lecture notes in computer science · doi:10.1007/11539506_62 · Cited by 17
Towards robotic facial mimicry: System development and evaluation
Mayer, C., Sosnowski, S., Kühnlenz, K., & Radig, B. (2010) · IEEE RO-MAN · doi:10.1109/roman.2010.5598629 · Cited by 17
Real-time facial expression recognition with illumination-corrected image sequences
He, L., Buenaposada, J. M., & Baumela, L. (2008) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.2008.4813328 · Cited by 17
“Different facial expression recognition algorithms have been introduced in the literature depending on the discriminative information extracted from the image and the classification procedure used [6, 11].”
MethodGiannakakis, G. A., Roussos, A., Andreou, C., Borgwardt, S., & Korda, A. I. (2024) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108507 · Cited by 16
“Various interesting approaches have been developed for FER [10, 11, 12].”
Smile detection using hybrid face representation
Arigbabu, O. A., Mahmood, S., Ahmad, S. M. S., & Arigbabu, A. A. (2015) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-015-0333-4 · Cited by 16
“Face expression is vital in determining the current emotional and behavioral mind set of human subjects (Pantic and Rothkrantz 2000).”
BackgroundA Robust-Facial Expressions Recognition System using Deep Learning Architectures
Ilyas, B. R., Abderrazak, T. A., Bendelhoum, M. S., Boukenadil, B., Imane, H., & Miloud, K. (2023) · International Conference on Decision Aid Sciences and Applications (DASA) · doi:10.1109/dasa59624.2023.10286798 · Cited by 16
“Somewhat recently, Facial Expression Recognition (FER) procedures excited increasingly more interest in the scientific local area [2, 3].”
BackgroundMachine Learning Techniques for Face Analysis
Valenti, R., Sebe, N., Gevers, T., & Cohen, I. (2008) · Springer eBooks · doi:10.1007/978-3-540-75171-7_7 · Cited by 16
“The two recent surveys in the area [19, 35] provide an in-depth review of many of the research done in automatic facial expression recognition in recent years.”
Candid portrait selection from video
Fiss, J., Agarwala, A., & Curless, B. (2011) · ACM Transactions on Graphics · doi:10.1145/2070781.2024162 · Cited by 16
Höfling, T. T. A., Alpers, G. W., Büdenbender, B., Föhl, U., & Gerdes, A. B. M. (2022) · PLoS ONE · doi:10.1371/journal.pone.0263863 · Cited by 16
“Recent advances in computer vision technology enable researchers to automatically measure facial activity in dynamic videos or static photos [8, 9].”
BackgroundIjaz, K., Bogdanovych, A., & Simoff, S. J. (2011) · Cited by 17
Local Gabor directional pattern for facial expression recognition
Ishraque, S. M. Z., Banna, A. K. M. H., & Chae, O. (2012) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccitechn.2012.6509762 · Cited by 16
“Several researchers [1-6] have attempted automatic facial expression recognition.”
BackgroundAnalysis of landmarks in recognition of face expressions
Alugupally, N., Samal, A. K., Marx, D. B., & Bhatia, S. (2011) · Pattern Recognition and Image Analysis · doi:10.1134/s105466181104002x · Cited by 16
Liu, J., Liu, B., Zhang, S., Yang, F. H., Yang, P., Metaxas, D., & Neidle, C. (2013) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2013.6553781 · Cited by 16
“Computer-based recognition of non-manual aspects of signed languages has made use of more general methods developed for facial expression recognition [12]–[14] and head pose estimation [15].”
MethodA New Facial Expression Recognition Technique using 2-D DCT and Neural Networks Based Decision Tree
Xiao, Y., Ma, L., & Khorasani, K. (2006) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2006.1716418 · Cited by 16
On the use of dynamic features in face biometrics: recent advances and challenges
Hadid, A., Dugelay, J., & Pietikäinen, M. (2011) · Signal Image and Video Processing · doi:10.1007/s11760-011-0247-3 · Cited by 16
“Complete surveys on the large number of works on facial expression recognition can be found in [47,10].”
BackgroundFacial expression recognition as a creative interface
Valenti, R., Jaimes, A., & Sebe, N. (2008) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/1378773.1378853 · Cited by 16
“Most automatic methods [4, 5] are based on Ekman’s work and extract features from images or video and use them as inputs to a classifier.”
MethodYanushkevich, S. N., Stoica, A., & Shmerko, V. P. (2006) · Annual Conference of the IEEE Industrial Electronics Society (IECON) · doi:10.1109/iecon.2006.347949 · Cited by 16
“It is well documented in medical applications that biometric data is associated with certain physiological and behavioral characteristics [5], [8], [9], [10], [12].”
BackgroundCapturing Feature and Label Relations Simultaneously for Multiple Facial Action Unit Recognition
Wang, S., Wu, S., Peng, G., & Ji, Q. (2017) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2737540 · Cited by 16
“fore, automatical face expression recognition has attracted increasing attention in the fields of both human behavior research [1] and computer vision [2], [3], [4], [5], [6].”
BackgroundEnd-To-End Prediction of Emotion from Heartbeat Data Collected by a Consumer Fitness Tracker
Harper, R. J., & Southern, J. (2019) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2019.8925520 · Cited by 16
“The majority of research in this field has focussed on predicting emotion from face, voice and text [1]– [8].”
BackgroundFacial expression recognition based on diffeomorphic matching
Yousefi, S., Nguyen, M. P., Kehtarnavaz, N., & Cao, Y. (2010) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2010.5650670 · Cited by 16
“The reader is also referred to the surveys [5] and [6] for facial expression analysis and face recognition methods covered in the literature and their applications in other fields.”
BackgroundLearning probabilistic classifiers for human–computer interaction applications
Sebe, N., Cohen, I., Cozman, F. G., Gevers, T., & Huang, T. S. (2005) · Multimedia Systems · doi:10.1007/s00530-005-0177-4 · Cited by 16
“The two recent surveys in the area [23, 45] provide an in-depth review of much of the research done in automatic facial expression recognition in recent years.”
BackgroundInductive Transfer Learning for Handling Individual Differences in Affective Computing
Wu, D., & Parsons, T. D. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24571-8_15 · Cited by 16
“, speech [12], facial expressions [5], physiological signals [10], and multimodal combination [15].”
BackgroundTemporal modeling of facial actions from face profile image sequences
Pantić, M., & Patras, I. (2005) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme.2004.1394122 · Cited by 16
Introduction to Emotion Recognition
Konar, A., Halder, A., & Chakraborty, A. (2015) · Wiley eBooks · doi:10.1002/9781118910566.ch1 · Cited by 17
Machine learning techniques in pain recognition.
Monwar, M. M. (2007) · doi:10.24124/2007/bpgub468 · Cited by 16
Gaya-Morey, F. X., Ramis, S., Manresa-Yee, C., & Rubio, J. M. B. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20090-5 · Cited by 15
“Deep learning approaches, such as Convolutional Neural Networks (CNNs), have shown remarkable results in FER tasks and achieved state-of-the-art performance.”
Can the image of food mislead the brain? Neurogastronomy research with EEG and emotion recognition
UÇUK, C., Perçin, N. Ş., Çevik, Ç., Al, T., & Kara, İ. (2025) · International Journal of Gastronomy and Food Science · doi:10.1016/j.ijgfs.2024.101097 · Cited by 15
Recognizing Continuous Grammatical Marker Facial Gestures in Sign Language Video
Nguyen, T. D., & Ranganath, S. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-19282-1_53 · Cited by 15
“Previous works on recognizing facial expressions were reviewed in [5] and [6].”
BackgroundModel Based Analysis of Face Images for Facial Feature Extraction
Riaz, Z., Mayer, C., Beetz, M., & Radig, B. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-03767-2_12 · Cited by 15
“[1] for facial expression recognition.”
Facial expression transfer method based on frequency analysis
Wei, W., Tian, C., Maybank, S. J., & Zhang, Y. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.08.004 · Cited by 15
“For extensive reviews on facial expression synthesis, we refer the reader to [42] and [43].”
Affective interaction based on person-independent facial expression space
Wang, H., & Wang, K. (2008) · Neurocomputing · doi:10.1016/j.neucom.2007.10.022 · Cited by 15
Moeini, A., Moeini, H., & Karim Faez, A. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.307 · Cited by 15
“The performance of the appearance-based methods is excellent in a constrained environment but their performance degrades in environmental variation [7].”
An Active Snake Model for Classification of Extreme Emotions
Mahapatra, D., Routray, A., & Mishra, C. (2006) · IEEE International Conference on Industrial Technology (ICIT) · doi:10.1109/icit.2006.372523 · Cited by 15
An efficient approach to smile detection
Shan, C. (2011) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2011.5771343 · Cited by 15
“Meanwhile, as a promising unobtrusive solution, automatic facial expression analysis from video or images has received much attention in the last two decades [1], [2], [3], [4], [5].”
BackgroundActive appearance models for facial expression recognition and monocular head pose estimation
Martins, P. A. D. (2004) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · Cited by 14
Towards Real-Life Facial Expression Recognition Systems
Benta, K.-I., & Vaida, M.-F. (2015) · Advances in Electrical and Computer Engineering · doi:10.4316/aece.2015.02012 · Cited by 14
Local Binary Patterns Based Facial Expression Recognition for Efficient Smart Applications
Nigam, S., Singh, R. K., & Misra, A. K. (2018) · Lecture notes in intelligent transportation and infrastructure · doi:10.1007/978-3-030-01560-2_13 · Cited by 16
Kalsum, T., Mehmood, Z., Kulsoom, F., Chaudhry, H. N., Rehman, A. U., Rashid, M., & Saba, T. (2021) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-201799 · Cited by 17
“It is shown by a research study that the speaker’s facial expressions convey a 55% impact of the spoken message while voice intonation contributes 38% and spoken words contribute merely 7% of the overall message effect [12].”
Roshanzamir, M., Jafari, M., Alizadehsani, R., Roshanzamir, M., Shoeibi, A., Górriz, J. M., Khosravi, A., Nahavandi, S., & Acharya, U. R. (2024) · Information Systems Frontiers · doi:10.1007/s10796-023-10466-7 · Cited by 17
Khademi, M., Manzuri, M. T., Kiapour, M. H., & Kiaei, A. A. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-12127-2_31 · Cited by 15
Facial Expressions Recognition Using Eigenspaces
Jasni (2012) · Journal of Computer Science · doi:10.3844/jcssp.2012.1674.1679 · Cited by 15
“A more recent and complete overview can be found in (Pantic and Rothkrantz, 2000; Fasel and Luettin, 2003).”
BackgroundMethodSubjective Assessment of Stress in HCI
Liapis, A., Katsanos, C., Sotiropoulos, D. G., Xenos, M., & Καρούσος, Ν. (2015) · Biannual Conference of the Italian SIGCHI Chapter (CHItaly) · doi:10.1145/2808435.2808450 · Cited by 16
Valenti, R., Jaimes, A., & Sebe, N. (2010) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/1873951.1874219 · Cited by 15
Automatic Facial Expression Recognition Based on Hybrid Features
Zhang, L., Chen, S., Wang, T., & Liu, Z. (2012) · Energy Procedia · doi:10.1016/j.egypro.2012.02.317 · Cited by 15
“Pantic and Rothkrantz [2], Fasel and Luettin [3] reviewed facial expression recognition methods for nearly front view images, and summarized the three main component of facial 2 Published by Elsevier Ltd. Sel ction and/or peer-revi w under r sponsibility of Haina University.”
BackgroundA Study of Learner-Oriented Negative Emotion Compensation in E-learning
Qin, J., Zheng, Q., & Li, H. (2014) · Educational Technology & Society · Cited by 15
Emotion Recognition Using Time-frequency Analysis of EEG Signals and Machine Learning
Zhang, J., Chen, P., Nichele, S., & Yazidi, A. (2019) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci44817.2019.9003057 · Cited by 16
Facial expression recognition for multiplayer online games
Zhan, C., Li, W., Ogunbona, P., & Safaei, F. (2006) · Research Online (University of Wollongong) · doi:10.5555/1231894.1231903 · Cited by 15
Why is facial expression analysis in the wild challenging?
Gehrig, T., & Ekenel, H. K. (2013) · Proceedings of the 2013 on Emotion recognition in the wild challenge and workshop · doi:10.1145/2531923.2531924 · Cited by 21
“Research on it already goes back to more than two decades [7, 13, 20, 23].”
BackgroundLandmark-Based Facial Feature Construction and Action Unit Intensity Prediction
Ma, J., Li, X., Ren, Y., Yang, R., & Zhao, Q. (2021) · Mathematical Problems in Engineering · doi:10.1155/2021/6623239 · Cited by 14
“Remarkable progress has beenmade in the field of automatic recognition of facial expressions over the past two decades [7, 8].”
BackgroundMulti-Query Content Based Image Retrieval System using Local Binary Patterns
Joseph, S., & Balakrishnan, K. (2011) · International Journal of Computer Applications · doi:10.5120/2235-2857 · Cited by 14
“A survey of automatic facial expression analysis is given in [20].”
Dynamic soft encoded patterns for facial event analysis
Yang, P., Liu, Q., & Metaxas, D. (2011) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2010.11.015 · Cited by 14
“The previous works of automatic facial expression recognition can be categorized into two main categories: image based methods [23,28,30] and video-based methods [12,25,45].”
BackgroundFacial expression recognition using HessianMKL based multiclass-SVM
Zhang, X., Mahoor, M. H., & Voyles, R. M. (2013) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2013.6553807 · Cited by 14
“For a detailed survey on the existing methods, we refer our reader to [6], [7].”
MethodDistributing expressional faces in 2-D emotional space
Du, Y., Bi, W., Wang, T., Zhang, Y., & Ai, H. (2007) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1282280.1282339 · Cited by 14
“To link low-level face features with emotional factors, we propose a simple method that builds an emotional mapping by a coarse labeling on Cohn-Kanade database and a linear .tting on the labeled data.”
MethodInfluential
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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