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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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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]
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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]
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe 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
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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]
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe 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
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
- 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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]
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably 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]
Strength:plain· not yet calibratedn = 3,000Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Strength:plain· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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]
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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]
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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]
Strength:plain· not yet calibratedn = 9Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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]
Strength:plain· not yet calibratedn = 15Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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]
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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]
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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]
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedn = 100Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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]
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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]
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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]
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:hedged· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passesProbably 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]
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Strength:hedged· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✗ Based: failsProbably 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
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably 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
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✗ Based: failsProbably 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]
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably 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
Strength:plain· not yet calibratedn = 25Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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]
Strength:plain· not yet calibratedn = 15Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Strength:hedged· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✗ Based: failsProbably 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
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Strength:plain· not yet calibratedn = 25Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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]
Strength:plain· not yet calibratedn = 10Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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]
Strength:plain· not yet calibratedn = 10Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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]
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably 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
Strength:plain· not yet calibratedn = 25Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclearIt 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedn = 8Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedn = 8Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe 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
Strength:plain· not yet calibratedn = 8Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✗ Asserted: fails✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedn = 8Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe 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
Strength:plain· not yet calibratedn = 2Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:plain· not yet calibratedn = 100Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Strength:plain· not yet calibratedn = 100Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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]
Strength:plain· not yet calibratedn = 40Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe 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.
Conclusion says all systems classify only basic emotions
MajorThe conclusion says every surveyed approach performs only basic-emotion classification, but the discussion states that four surveyed systems also perform facial-action coding.
“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].”
“Four of the surveyed systems perform facial action coding from an input image or an image sequence (Table 7).”
Citation for Ekman's six categories points to different authors
MinorThe statement attributes the six categories to Ekman and cites reference [19], but the paper's reference [19] is by Eisert and Girod.
“Ekman defined six such categories, referred to as the basic emotions: happiness, sadness, surprise, fear, anger, and disgust [19].”
“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.”
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Cited by
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Items 701–800 of 1,000
E-learning Platforms and E-learning Students: Building the Bridge to Success
Rodrigues, M., Gonçalves, S., & Fdez‐Riverola, F. (2013) · ADCAIJ ADVANCES IN DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE JOURNAL · doi:10.14201/adcaij2012122134 · Cited by 14
Virtual Mouse Control by Webcam for the Disabled
Baştuğ, R. S., Yeşilkaya, B., Unay, M., & Akan, A. (2018) · Medical Technologies Congress (TIPTEKNO) · doi:10.1109/tiptekno.2018.8597147 · Cited by 14
“girdilerin ve çıktıların ne olduğu ile birlikte nasıl çalıştıklarını gösterir [5].”
Support Vector Machines and Affective Science
Miller, C. H., Sacchet, M. D., & Gotlib, I. H. (2020) · Emotion Review · doi:10.1177/1754073920930784 · Cited by 15
Abbasi, N. I., Song, S., & Güneş, H. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747102 · Cited by 14
Interactive Technology: Teaching People with Autism to Recognize Facial Emotions
Jose, C., Fernandes, T., Augusto, A., & Verónica, C. (2011) · InTech eBooks · doi:10.5772/19968 · Cited by 14
“In daily life, we interact with others by exchanging a huge quantity of information, including our current states of emotions, through facial expressions.”
BackgroundMethodPerson independent facial expression analysis using Gabor features and Genetic Algorithm
Shojaeilangari, S., Yun, Y. W., & Teoh, E. K. (2011) · International Conference on Information, Communications & Signal Processing (ICICS) · doi:10.1109/icics.2011.6173537 · Cited by 14
“Essentially, facial expression as a visual information is considered the most important cue for assessment of human affective behavior.”
BackgroundFacial expression recognition based on Gabor features and sparse representation
Liu, W., Song, C., Wang, Y., & Jia, L. (2012) · International Conference on Control, Automation, Robotics and Vision (ICARCV) · doi:10.1109/icarcv.2012.6485394 · Cited by 14
Automatic emotion recognition in video
Kalaiselvi, R., Kavitha, P., & Shunmuganathan, K. L. (2014) · International Conference on Green Computing Communication and Electrical Engineering · doi:10.1109/icgccee.2014.6921398 · Cited by 14
A realistic dynamic facial expression transfer method
Zhang, Y., & Wei, W. (2012) · Neurocomputing · doi:10.1016/j.neucom.2012.01.019 · Cited by 14
Stratou, G., Ghosh, A., Debevec, P. E., & Morency, L. (2012) · Image and Vision Computing · doi:10.1016/j.imavis.2012.02.001 · Cited by 14
“…in facial expression are manifested as deformation of the shape and texture of the facial surface, ii) illumination variance has been shown to influence perception of shape, which confounds face recognition, iii) most methods for automated expression recognition use image representations,…”
MethodA Neuro Fuzzy Approach for Facial Expression Recognition Using LBP Histograms
Gomathi, V., Ramar, K., & Jeevakumar, A. S. (2010) · International Journal of Computer Theory and Engineering · doi:10.7763/ijcte.2010.v2.147 · Cited by 14
“M.Pantic and Rothkrantz [14] presented detail survey about facial expression recognition.”
BackgroundMethodEvaluating Instantaneous Psychological Stress from Emotional Composition of a Facial Expression
Das, S., & Yamada, K. (2013) · Journal of Advanced Computational Intelligence and Intelligent Informatics · doi:10.20965/jaciii.2013.p0480 · Cited by 14
Facial expression recognition using local Gabor filters and PCA plus LDA
Pumlumchiak, T., & Vittayakorn, S. (2017) · doi:10.1109/iciteed.2017.8250446 · Cited by 14
“Because of its potential applications, automatic facial expression recognition has attracted much attention over the decades [1–3].”
Joint analysis of simultaneous EEG and eye tracking data for video images
Szajerman, D., Napieralski, P., & Lecointe, J. (2018) · COMPEL The International Journal for Computation and Mathematics in Electrical and Electronic Engineering · doi:10.1108/compel-07-2018-0281 · Cited by 14
Verma, R., Bhardwaj, N., Singh, P. D., Bhavsar, A. V., & Sharma, V. (2021) · Forensic Science International Reports · doi:10.1016/j.fsir.2021.100226 · Cited by 15
Adjusted pixel features for robust facial component classification
Mayer, C., Wimmer, M., & Radig, B. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2009.07.012 · Cited by 16
Development of a Facial Expression Imitation System
Kim, D., Jung, S. N., An, K. H., Lee, H. S., & Chung, M. J. (2006) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2006.282329 · Cited by 14
“Various pattern recognition methods have been used in order to recognize facial expressions [1].”
MethodExtracting subtle facial expression for emotional analysis
Cowell, J., & Ayesh, A. (2005) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2004.1398378 · Cited by 14
“A useful review of current systems and a discussion of their capabilities is given by Pantic[11].”
Background3D model fitting for facial expression analysis under uncontrolled imaging conditions
Maurel, P., McGonigal, A., Keriven, R., & Chauvel, P. (2008) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2008.4761736 · Cited by 14
Weighted Voting Of Sparse Representation Classifiers For Facial Expression Recognition
Cotter, S. F. (2010) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.41873 · Cited by 14
Cazenave, T., Kruskal, C. P., & Bouzy, B. (2007) · ICGA Journal · doi:10.3233/icg-2007-30215 · Cited by 14
Mansoorizadeh, M., & Charkari, N. M. (2008) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/1389586.1389688 · Cited by 14
“Initially, features like “wrinkles on forehead and between eyes” were mentioned; as described and used by several studies[10].”
Timing is everything: A spatio-temporal approach to the analysis of facial actions
Valstar, M. (2008) · Spiral (Imperial College London) · doi:10.25560/1394 · Cited by 14
Feedback interpretation based on facial expressions in human-robot interaction
Lang, C., Hanheide, M., Lohse, M., Wersing, H., & Sagerer, G. (2009) · IEEE RO-MAN · doi:10.1109/roman.2009.5326199 · Cited by 15
“Many techniques have been developed for automatic facial expression recognition in general; Fasel & Luettin [9], and Pantic & Rothkrantz [10] presented surveys on this topic.”
BackgroundPersonalized Human Emotion Classification Using Genetic Algorithm
Rizon, M., Desa, H., Karthigayan, M., Nagarajan, R., Alajlan, N., & Sazali, Y. (2009) · International Conference in Visualisation (VIZ) · doi:10.1109/viz.2009.54 · Cited by 13
“The application of neural network gives easier solution to complex problems such as facial expression [10].”
BackgroundA NOVEL FEATURE EXTRACTION TECHNIQUE FOR FACIAL EXPRESSION RECOGNITION
Islam, M. S., & Auwatanamongkol, S. (2013) · Cited by 13
Transferred Deep Convolutional Neural Network Features for Extensive Facial Landmark Localization
Zhang, S., Yang, H. S., & Yin, Z. (2016) · IEEE Signal Processing Letters · doi:10.1109/lsp.2016.2533721 · Cited by 13
“E XTENSIVE facial landmark localization (EFLL) is critical for face verification [1], expression recognition [2], and animation [3].”
BackgroundVariable-state Latent Conditional Random Field models for facial expression analysis
Walecki, R., Rudovic, O. O., Pavlović, V., & Pantić, M. (2016) · Image and Vision Computing · doi:10.1016/j.imavis.2016.04.009 · Cited by 15
“Early research on facial expression analysis focused mainly on recognition of prototypic facial expressions of six basic emotions (anger, happiness, fear, surprise, sadness, and disgust) and detection of AUs from static facial images [3].”
BackgroundPredicting Active Facial Expressivity in People with Parkinson's Disease
Joshi, A., Tickle‐Degnen, L., Gunnery, S. D., Ellis, T. D., & Betke, M. (2016) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/2910674.2910686 · Cited by 13
“Automatic analysis of facial expressions and affect has been an active research topic in the fields of computer vision and machine learning [5, 9].”
BackgroundChernorizov, A. M., Isaychev, S. A., Zinchenko, Y. P., Galatenko, V., Znamenskaya, I. A., Zakharov, P. N., Khakhalin, A. V., & Gradoboeva, O. N. (2016) · Psychology in Russia State of Art · doi:10.11621/pir.2016.0403 · Cited by 13
“The theoretical basis for such systems and the mathematical algorithms and technical components used to create them are described in reviews by Pantic and Rothkrantz (2000).”
MethodExpression Classification in Children Using Mean Supervised Deep Boltzmann Machine
Nagpal, S., Singh, M., Mayank Vatsa, A., Singh, R., & Afzel Noore, B. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00033 · Cited by 13
“Automated facial expression classification [5,31] has widespread application in different domains such as human computer inter-Adult action, affective computing, health, and entertainment.”
BackgroundSolving Image Processing Critical Problems Using Machine Learning
Sharma, A. K., Gupta, A. R., & Jaiswal, V. K. (2021) · Studies in big data · doi:10.1007/978-981-15-9492-2_11 · Cited by 13
Selection of Optimal EEG Electrodes for Human Emotion Recognition
Zhang, J., & Chen, P. (2020) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2020.12.2753 · Cited by 13
AI-Based Emotion Recognition to Study Users’ Perception of Dark Patterns
Avolicino, S., Gregorio, M. D., Palomba, F., Romano, M., Sebillo, M., & Vitiello, G. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-17615-9_13 · Cited by 13
“For a long time, facial expression recognition and analysis, particularly FACS detection [ 13 ] and discrete emotion detection, has been a hot area in computer science, with numerous promising algorithms [ 14 ] [ 15 ] [ 16 ].”
BackgroundAn Efficient Face Recognition System Using DWT-ICA Features
Babu, N. T. N., Fathima, A. A., & Vaidehi, V. (2011) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2011.31 · Cited by 13
“Oncel Tuzel, Fatih Porikli and Peter Meer categorize feature extraction schemes into sparse and dense representations [5].”
BackgroundFacial expression modeling using component AAM models — Gaming applications
Bacivarov, I., & Corcoran, P. (2009) · International IEEE Consumer Electronics Society's Games Innovations Conference (ICE-GIC) · doi:10.1109/icegic.2009.5293579 · Cited by 13
“This approach is based on the work of Pantic et al [16]-[18], and is adapted for our AAM models.”
Associating gesture expressivity with affective representations
Malatesta, L., Asteriadis, S., Caridakis, G., Vasalou, A., & Karpouzis, K. C. (2016) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2016.01.010 · Cited by 13
“In computational behaviour analysis, according to a survey paper by Kleinsmith and Bianchi-Berthouze (2013), research on non-verbal affect recognition has mostly focused on facial expressions starting with the FACS coding system developed by Ekman and Friesen (1977) and moving to more recent computational approaches (…”
BackgroundMethodFace Recognition Techniques - A Review
Asstt. Prof., C., Taqdir, T., & Dhir, R. (2014) · IOSR Journal of Computer Engineering · doi:10.9790/0661-162102327 · Cited by 13
AutisMitr: Emotion Recognition Assistive Tool for Autistic Children
Singh, A., & Dewan, S. (2020) · Open Computer Science · doi:10.1515/comp-2020-0006 · Cited by 13
“The paper [31] provides comprehensive literature of presentmethods and applications of automatic facial recognition systems.”
BackgroundRecognition of Affective and Grammatical Facial Expressions: A Study for Brazilian Sign Language
da Silva, E. P., Costa, P. D. P., Kumada, K. M. O., Martino, J. M. D., & Florentino, G. A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-66096-3_16 · Cited by 13
The integration of an emotional system in the Intelligent tutoring system
Ammar, M. B., Néji, M., & Alimi, A. M. (2005) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa.2005.1387134 · Cited by 13
Elguebaly, T., & Bouguila, N. (2012) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2012.08.003 · Cited by 14
Person Recognition based on Head and Mouth Dynamics
Saeed, U., Matta, F., & Dugelay, J. (2006) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp.2006.285262 · Cited by 13
“Lip detection and segmentation techniques have mostly been studied for audiovisual speech recognition [6], facial expression recognition [7] and lip-reading but very rarely for biometrics, where they have been classified as appearance based [8] or geometric feature based [9].”
BackgroundMetodologias para identificação de faces em imagens : introdução e exemplos de resultados
Carvalho, F. J. S., & Tavares, J. M. R. S. (2005) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · Cited by 13
On Assisting a Visual-Facial Affect Recognition System with Keyboard-Stroke Pattern Information
Σταθοπούλου, Ι., Alepis, E., Tsihrintzis, G. A., & Virvou, M. K. (2009) · Springer eBooks · doi:10.1007/978-1-84882-983-1_35 · Cited by 12
Transfer-learning enabled micro-expression recognition using dense connections and mixed attention
Gan, C., Xiao, J., Zhu, Q., Jain, D. K., & Štruc, V. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112640 · Cited by 12
“Facial expressions are the primary means of expressing emotions in day–to–day interactions [1, 2, 3].”
BackgroundFeature-Adaptive Motion Energy Analysis for Facial Expression Recognition
Noh, S., Park, H., Jin, Y., & Park, J.-I. (2007) · International Symposium on Visual Computing · doi:10.1007/978-3-540-76858-6_45 · Cited by 16
“More reviews on the state of art in the field of facial expression recognition can be found in [11, 6].”
BackgroundSemi-Qualitative Probabilistic Networks in Computer Vision Problems
de Campos, C. P., Zhang, L., Tong, Y., & Ji, Q. (2009) · Journal of Statistical Theory and Practice · doi:10.1080/15598608.2009.10411920 · Cited by 12
“Consider a problem of recognizing facial expressions from real image data (Pantic and Rothkrantz, 2000; Fasel and Luettin, 2003).”
BackgroundFully automatic 3D facial expression recognition using local depth features
Xue, M., Mian, A. S., Liu, W., & Li, L. (2014) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2014.6835736 · Cited by 12
“Prior research has mainly focused on 2D images or videos [10] but recently 3D facial expression recognition [12] has gained popularity due to its invariance to pose and illumination.”
MethodTwo-stage Multi-class AdaBoost for Facial Expression Recognition
Deng, H., Zhu, J., Lyu, M. R.-T., & King, I. (2007) · IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · doi:10.1109/ijcnn.2007.4371439 · Cited by 12
“…order to facilitate a more intelligent and natural human machine interface for new multimedia products, automatic facial expression recognition [3], [5], [13] had been widely studied in the past decade or two, which has become a very active research area in computer vision and pattern recognition.”
BackgroundA Facial Expression Recognition Approach for Social IoT Frameworks
Barra, S., Hossain, S., Pero, C., & Umer, S. (2022) · Big Data Research · doi:10.1016/j.bdr.2022.100353 · Cited by 13
Moodle and affective computing : knowing who´s on the other side
Rodrigues, M., Fdez‐Riverola, F., & Nováis, P. (2011) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · Cited by 12
Identification-driven emotion recognition system for a social robot
Żarkowski, M. (2013) · International Conference on Methods and Models in Automation and Robotics · doi:10.1109/mmar.2013.6669895 · Cited by 12
“The exhaustive review of studies in this field has been presented in [6], [7].”
BackgroundFace Recognition Based on Local Derivative Ternary Pattern
Meena, K., Suruliandi, A., & Rose, R. R. (2014) · IETE Journal of Research · doi:10.1080/03772063.2014.890811 · Cited by 12
Using sparse coding for landmark localization in facial expressions
Cuculo, V., Lanzarotti, R., & Boccignone, G. (2014) · European Workshop on Visual Information Processing · doi:10.1109/euvip.2014.7018369 · Cited by 12
Adaptive discriminative metric learning for facial expression recognition
Yan, H., Ang, M. H., & POO, A. N. (2012) · IET Biometrics · doi:10.1049/iet-bmt.2012.0006 · Cited by 12
Clay, A., Couture, N., & Nigay, L. (2009) · ASME World Conference on Innovative Virtual Reality (WINVR) · doi:10.1115/winvr2009-704 · Cited by 12
Real Time Facial Expression Recognition
Srivastava, S. (2012) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-642-27317-9_13 · Cited by 12
Sequential forward feature selection for facial expression recognition
Gacav, C., Benlıgıray, B., & Topal, C. (2016) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2016.7496031 · Cited by 12
Anticipating movie success through crowdsourced social media videos
Singh, J., & Goyal, G. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2018.08.050 · Cited by 13
Emotion Recognition from Sensory and Bio-Signals: A Survey
Vora, K., Shah, S., Harsoda, H., Sheth, J., Agarwal, S., Thakkar, A., & Mankad, S. H. (2018) · Proceedings of the 2nd International Conference on Data Engineering and Communication Technology · doi:10.1007/978-981-13-1610-4_35 · Cited by 11
Towards Thermal Region of Interest for Human Emotion Estimation
Nguyen, T., Tran, K. B., & Nguyen, H. S. (2018) · International Conference on Knowledge and Systems Engineering/Proceedings · doi:10.1109/kse.2018.8573373 · Cited by 12
Dual-graph regularized discriminative transfer sparse coding for facial expression recognition
Chen, D., & Song, P. (2020) · Digital Signal Processing · doi:10.1016/j.dsp.2020.102906 · Cited by 12
YOLOv3 Attention Face Detector with High Accuracy and Efficiency
Liu, Q., Lu, S., & Lingqiang, L. (2021) · Computer Systems Science and Engineering · doi:10.32604/csse.2021.014086 · Cited by 12
Chu, G., Moscovich, T., & Balakrishnan, R. (2009) · HAL · Cited by 12
Evaluation of Edge Orientation Histograms in smile detection
Timotius, I. K., & Setyawan, I. (2014) · doi:10.1109/iciteed.2014.7007905 · Cited by 12
“I. INTRODUCTION There are many research works that have been done in the field of automatic facial expression recognition [1] [2] [3] [4] [5] [6] [7].”
BackgroundRelative facial action unit detection
Khademi, M., & Morency, L. (2014) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2014.6835983 · Cited by 12
“Recent application areas include pain assessment, psychological science, and neurology [1, 2].”
BackgroundTowards Knowledge-Based Affective Interaction: Situational Interpretation of Affect
Abbasi, A. R., Uno, T., Dailey, M. N., & Afzulpurkar, N. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74889-2_40 · Cited by 12
“There has been extensive work on automatic analysis of facial expressions [10,11,12], but it is still an open question how reliably we can infer underlying emotion from such analysis.”
BackgroundA Multi-Modal Emotion-Diagnosis System to Support e-Learning
Nosu, K., & Kurokawa, T. (2006) · International Conference on Innovative Computing, Information and Control · doi:10.1109/icicic.2006.203 · Cited by 12
Autonomous Robot Control Using Facial Expressions
Mittal, R., Srivastava, P., George, A., & Mukherjee, A. (2012) · International Journal of Computer Theory and Engineering · doi:10.7763/ijcte.2012.v4.546 · Cited by 12
Multimodal Facial Expression Recognition Based on 3D Face Reconstruction from 2D Images
Moeini, A., & Moeini, H. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-13737-7_5 · Cited by 12
“The performance of the appearance-based methods is excellent in a constrained environment but their performance degrades in environmental variation [7].”
Semisupervised learning of classifiers with application to human-computer interaction
Huang, T. S., & Cohen, I. L. (2003) · Cited by 12
Hierarchical multistream recognition of facial expressions
Chibelushi, C. C., & Bourel, F. (2004) · IEE Proceedings - Vision Image and Signal Processing · doi:10.1049/ip-vis:20040748 · Cited by 12
Li, H., Sun, J., Wang, D., Xu, Z., & Chen, L. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.03015 · Cited by 18
“significant advances have achieved in 2D FER, it still fails to solve the two major challenges: illumination changes and pose variations [21].”
BackgroundMethodInfluentialEmotional analysis of facial expressions
Ayesh, A., & Cowell, J. (2005) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2004.1398377 · Cited by 11
“Keywords: emotions modeling, facial expressions, cognitive reasoning.”
BackgroundA Multimodal Perception Framework for Users Emotional State Assessment in Social Robotics
Cominelli, L., Carbonaro, N., Mazzei, D., Garofalo, R., Tognetti, A., & Rossi, D. D. (2017) · Future Internet · doi:10.3390/fi9030042 · Cited by 11
“Typically, these audiovisual acquisition systems are focused on face detection [7,8], face recognition [9], facial expression analysis [10,11], vocal emotion [12,13], gesture recognition [14,15], human motion analysis [16–18], audiovisual automatic speech recognition [19], and eye tracking [20,21].”
BackgroundSocial skills training with virtual assistant and real-time feedback
Ali, M. R., & Hoque, M. E. (2017) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/3123024.3123196 · Cited by 11
Towards Facial De-Expression and Expression Recognition in the Wild
Hu, J., Yu, B., Yang, Y., & Feng, B. (2019) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2019.8925461 · Cited by 10
“Experiments on the CK+ dataset : Following the testing protocol in [29], we use the last three frames of each video sequence as the emotion image, and the first frame as the neutral emotion image.”
BackgroundMethodKalanadhabhatta, M., Santana, A. M., Zhang, Z., Ganesan, D., Grabell, A. S., & Rahman, T. (2022) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3534583 · Cited by 11
“There has been significant work in the computer vision community focusing on automatically detecting both the abovementioned AUs themselves as well as the affective states denoted by combinations of these AUs (see [20, 101, 111] for detailed surveys).”
BackgroundLakshmi, A. V., & Mohanaiah, P. (2022) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2022.104718 · Cited by 12
Facial Expression Recognition: Deep Survey, Progression and Future Perspective
Grover, R., & Bansal, S. R. (2023) · International Conference on Advancement in Computation & Computer Technologies (InCACCT) · doi:10.1109/incacct57535.2023.10141843 · Cited by 11
Feature Synthesis Using Genetic Programming for Face Expression Recognition
Bhanu, B., Yu, J., Tan, X., & Lin, Y. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-24855-2_103 · Cited by 11
“A review of different approaches for face expression recognition can be found in [2].”
BackgroundFEATURE EXTRACTION THROUGH CROSS-PHASE CONGRUENCY FOR FACIAL EXPRESSION ANALYSIS
Buciu, I., & Nafornita, I. (2009) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s021800140900717x · Cited by 11
Automatic Detecting Neutral Face for Face Authentication and Facial Expression Analysis
Tian, Y., & Bolle, R. M. (2003) · Cited by 11
Facial expression recognition using depth map estimation of Light Field Camera
Shen, T.-W., Fu, H. C., Chen, J., Yu, W., Lau, C., Lo, W. L., & Chi, Z. (2016) · IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) · doi:10.1109/icspcc.2016.7753695 · Cited by 11
“An effective HOG method is further proposed in [8].”
MethodThe Intellectual Structure of Human Computer Interaction Research.
Shiau, W., Yan, C.-M., & Kuo, C.-C. (2016) · Journal of information science and engineering · Cited by 11
Dynamic Behavior Analysis via Structured Rank Minimization
Georgakis, C., Panagakis, Y., & Pantić, M. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-016-0985-3 · Cited by 11
“…actions (e.g., walking, running, and hand-clapping) (Dollár et al. 2005; Niebles et al. 2008; Georgakis et al. 2012) or discrete,basicemotionalstates(e.g.,happiness,sadness)(Pantic and Rothkrantz 2000; Cohen et al. 2003; Littlewort et al. 2006) mainlyfrom poseddataacquiredinlaboratorysettings.”
Introduction to Synthesis in Biometrics
Yanushkevich, S. N., Shmerko, V. P., Stoica, A., Wang, P. S. P., & Srihari, S. N. (2007) · Series in machine perception and artificial intelligence · doi:10.1142/9789812770677_0001 · Cited by 11
“Psychologists distinguish two kinds of short-time facial expressions: controlled and non-controlled facial expressions [40].”
BackgroundReal Time Facial Expression Recognition Using Local Binary Patterns and Linear Programming
Feng, X., Cui, J., Pietikäinen, M., & Hadid, A. (2005) · Lecture notes in computer science · doi:10.1007/11579427_33 · Cited by 11
Automatic Facial Pose Determination of 3D Range Data for Face Model and Expression Identification
Wei, X., Longo, P., & Yin, L. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74549-5_16 · Cited by 11
“The facial pose estimation is a first critical step towards developing a successful system for both face recognition [21] and facial expression recognition [15].”
BackgroundNavarretta, C. (2014) · Knowledge-Based Systems · doi:10.1016/j.knosys.2014.04.034 · Cited by 11
Audio-visual emotion recognition using Boltzmann Zippers
Lu, K., & Jia, Y. (2012) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2012.6467428 · Cited by 11
“Index Terms emotion recognition, audio-visual fusion, Boltzmann Zipper, second-order method”
MethodDatabase independent human emotion recognition with Meta-Cognitive Neuro-Fuzzy Inference System
Subramanian, K. R., Babu, R. V., & Ramasamy, S. (2014) · International Conference on Intelligent Sensors, Sensor Networks and Information Processing · doi:10.1109/issnip.2014.6827690 · Cited by 11
A framework for the Recognition of Human Emotion using Soft Computing models
IqbalQuraishi, M., Choudhury, J. P., De, M., & Chakraborty, P. (2012) · International Journal of Computer Applications · doi:10.5120/5087-7154 · Cited by 11
“One may refer to [4][5][6][7] for a survey.”
BackgroundFacial Expression Recognition using KCCA with Combining Correlation Kernels and Kansei Information
Horikawa, Y. (2007) · International Conference on Computational Science and Its Applications · doi:10.1109/iccsa.2007.78 · Cited by 11
“Kernel canonical correlation analysis (kCCA) with combining correlation kernels of multiple-orders and Kansei information is applied to facial expression recognition.”
MethodMental Health Informatics: Current Approaches
Diederich, J., & Song, I. (2013) · Studies in computational intelligence · doi:10.1007/978-3-642-38550-6_1 · Cited by 11
Pantić, M., & Rothkrantz, L. J. M. (2003) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2002.1175613 · Cited by 11
“Most approaches to automatic facial gesture analysis in face image sequences attempt to recognize a set of prototypic emotional facial expressions, i.e., happiness, sadness, fear, surprise, anger and disgust [3].”
BackgroundSelf-citationView and texture-independent facial expression recognition in videos using dynamic programming
Dornaika, F., & Davoine, F. (2005) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2005.1530305 · Cited by 11
“A survey on facial expression recognition methods can be found in [3].”
BackgroundTowards Analyzing and Predicting the Experience of Live Performances with Wearable Sensing
Gedik, E., Cabrera-Quiros, L., Martella, C., Englebienne, G., & Hung, H. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2875987 · Cited by 10
“Most of the early work focused on video and/or audio inputs [9], used datasets of single input modalities [20], included a limited set of deliberate affec-”
Background
Metadata
- Authors
- Pantie, M., & Rothkrantz, L. J. M.
- Affiliations
- Delft University of Technology (NL)
- ORCID
- Rothkrantz, L.J.M.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2000
- Volume
- 22
- Issue
- 12
- Pages
- 1424-1445
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2159017231
- Semantic Scholar
- 087a6f471b5177014b08a342968901e1ef083ed1
- Cited by
- 1,778
- References
- 111
- Citations by year
- 2026: 33 · 2025: 31 · 2024: 30 · 2023: 43 · 2022: 41 · 2021: 43 · 2020: 58 · 2019: 65 · 2018: 72 · 2017: 71 · 2016: 86 · 2015: 87 · 2014: 105 · 2013: 105 · 2012: 108
- FWCI
- 23.88
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
- Research Repository (Delft University of Technology) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- facial expression analysis, face detection, facial expression recognition, emotion classification, human visual system, human-machine interaction, real-time systems
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