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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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Items 201–300 of 1,000
Vision-based user interfaces: methods and applications
Porta, M. (2002) · International Journal of Human-Computer Studies · doi:10.1006/ijhc.2002.1012 · Cited by 88
A Bayesian Deep Learning Framework for End-To-End Prediction of Emotion From Heartbeat
Harper, R. J., & Southern, J. (2020) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2020.2981610 · Cited by 94
“A wealth of research within the field of affective computing has focussed on the analysis of face, voice and text [13], [14], [15], [16], [17], [18], [19], [20].”
BackgroundPose-Invariant Facial Expression Recognition Using Variable-Intensity Templates
Kumano, S., Otsuka, K., Yamato, J., Maeda, E., & Sato, Y. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-008-0185-x · Cited by 88
“Excellent reviews of a number of recent studies assuming near-frontal faces can be found in Pantic and Rothkrantz (2000b) ; Fasel and Luettin (2003); Tian et al. (2005); Pantic and Bartlett (2007).”
BackgroundMethodFace detection and recognition of natural human emotion using Markov random fields
Maglogiannis, I. G., Vouyioukas, D., & Aggelopoulos, C. (2007) · Personal and Ubiquitous Computing · doi:10.1007/s00779-007-0165-0 · Cited by 86
“Various approaches to face detection have been proposed in literature [9–11, 14, 15, 25].”
MethodA GPSO-optimized convolutional neural networks for EEG-based emotion recognition
Gao, Z., Li, Y., Yang, Y., Wang, X., Dong, N., & Chiang, H. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.10.096 · Cited by 88
Appearance Manifold of Facial Expression
Shan, C., Gong, S., & McOwan, P. W. (2005) · Lecture notes in computer science · doi:10.1007/11573425_22 · Cited by 86
“Therefore, automatic facial expression analysis has attracted much attention [5, 12 , 20] in recent years.”
Background3D facial expression recognition: A perspective on promises and challenges
Fang, T., Zhao, X., Ocegueda, O., Shah, S. K., & Kakadiaris, I. A. (2011) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2011.5771466 · Cited by 90
“Comprehensive surveys in this area include those by Fasel and Luettin [2], Pantic et al. [3] and Zeng et al. [4].”
BackgroundAn automated face reader for fatigue detection
Gu, H., & Ji, Q. (2004) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.2004.1301517 · Cited by 87
“A recent survey of existing works can be found in [2].”
BackgroundLocal features based facial expression recognition with face registration errors
Gritti, T., Shan, C., Jeanne, V., & Braspenning, R. (2008) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.2008.4813379 · Cited by 86
“Driven by its important applications and theoretical interests, automatic facial expression recognition has attracted much attention in the last two decades [9,17,18].”
BackgroundDeep-Learning-Based Multimodal Emotion Classification for Music Videos
Pandeya, Y. R., Bhattarai, B., & Lee, J. (2021) · Sensors · doi:10.3390/s21144927 · Cited by 89
“In relation to other visual cues, such as gestures, background scenery, color, and camera position, facial expressions provide clearer visual cues for emotional analysis [51,52].”
BackgroundEmotional Storytelling Using Virtual and Robotic Agents
Costa, S., Brunete, A., Bae, B.-C., & Mavridis, N. (2018) · International Journal of Humanoid Robotics · doi:10.1142/s0219843618500068 · Cited by 85
Fan, X., & Tjahjadi, T. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.04.025 · Cited by 83
“Although much progress has been made since then, accurate recognition of facial expressions is still a challenging problem due to the subtlety, complexity and variability of facial expressions [5, 6].”
BackgroundMethodA Review of Facial Landmark Extraction in 2D Images and Videos Using Deep Learning
Bodini, M. (2019) · Big Data and Cognitive Computing · doi:10.3390/bdcc3010014 · Cited by 83
“Head pose estimation [1], and facial expression recognition [2] algorithms are strongly based on the facial shape information, given by landmark positions.”
Background3D Approaches and Challenges in Facial Expression Recognition Algorithms—A Literature Review
Nonis, F., Dagnes, N., Marcolin, F., & Vezzetti, E. (2019) · Applied Sciences · doi:10.3390/app9183904 · Cited by 81
Audiovisual Behavior Modeling by Combined Feature Spaces
Schuller, B. W., Arsić, D., Rigoll, G., Wimmer, M., & Radig, B. (2007) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2007.366340 · Cited by 82
“A comprehensive overview is found in [9, 10].”
Action unit detection with segment-based SVMs
Simon, T., Nguyen, M. H., la Torre, F. D., & Cohn, J. F. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5539998 · Cited by 82
“This section reviews some related work; for comprehensive surveys see [9, 19, 25, 30].”
Background3D/4D facial expression analysis: An advanced annotated face model approach
Fang, T., Zhao, X., Ocegueda, O., Shah, S. K., & Kakadiaris, I. A. (2012) · Image and Vision Computing · doi:10.1016/j.imavis.2012.02.004 · Cited by 81
“Comprehensive surveys in this area include those by Fasel and Luettin [2], Pantic et al .”
BackgroundLocal binary pattern and its derivatives for face recognition
Suruliandi, A., Meena, K., & Rose, R. R. (2012) · IET Computer Vision · doi:10.1049/iet-cvi.2011.0228 · Cited by 81
Recognition of facial expressions based on salient geometric features and support vector machines
Ghimire, D., Lee, J., Li, Z.-N., & Jeong, S. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-3428-9 · Cited by 84
“Several researchers have presented review on FER; among them early reviews can be found in [8, 9, 10], whereas recent reviews can be found in [11, 12].”
BackgroundBaseline CNN structure analysis for facial expression recognition
Shin, M., Kim, M., & Kwon, D. (2016) · IEEE RO-MAN · doi:10.1109/roman.2016.7745199 · Cited by 85
“I. INTRODUCTION Human facial expression recognition is considered important in the human-robot interaction field and has been studied with much interest in the past 10 years [1], [2], [3].”
BackgroundFacial Expression Recognition: A Review of Trends and Techniques
Ekundayo, O., & Viriri, S. (2021) · IEEE Access · doi:10.1109/access.2021.3113464 · Cited by 92
“Existing FER review works [7]–[10] have diversely presented comprehensive studies on the traditional FER implementation methods, including the handcrafted techniques and the machine learning algorithms.”
MethodTalaat, F. M., Ali, Z. H., Mostafa, R. R., & El-Rashidy, N. M. (2024) · Soft Computing · doi:10.1007/s00500-023-09477-y · Cited by 80
RetractedStatic topographic modeling for facial expression recognition and analysis
Wang, J., & Yin, L. (2007) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2006.10.011 · Cited by 80
Shape analysis of local facial patches for 3D facial expression recognition
Maalej, A., Amor, B. B., Daoudi, M., Srivastava, A., & Berretti, S. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.02.012 · Cited by 83
A Survey of Computer-Based Deformable Models
Moore, P., & Molloy, D. (2007) · International Machine Vision and Image Processing Conference (IMVIP) · doi:10.1109/imvip.2007.31 · Cited by 81
“Techniques range from template based methods [18], to neural network based methods [8], or a combination of the two [20, 11].”
MethodSimultaneous Facial Action Tracking and Expression Recognition in the Presence of Head Motion
Dornaika, F., & Davoine, F. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-007-0059-7 · Cited by 80
“Surveys of facial expression recognition methods can be found in (Fasel and Luettin 2003; Pantic and Rothkrantz 2000).”
BackgroundBejani, M., Gharavian, D., & Charkari, N. M. (2012) · Neural Computing and Applications · doi:10.1007/s00521-012-1228-3 · Cited by 77
“In the survey of Pantic and Rothkrantz [12] in 2000, only four studies were found to focus on audiovisual affect recognition.”
BackgroundFacial expression recognition based on a multi-task global-local network
Yu, M., Zheng, H., Peng, Z., Dong, J., & Du, H. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.01.016 · Cited by 78
Bousmalis, K., Méhu, M., & Pantić, M. (2012) · Image and Vision Computing · doi:10.1016/j.imavis.2012.07.003 · Cited by 83
A survey on mobile affective computing
Politou, E., Alepis, E., & Patsakis, C. (2017) · Computer Science Review · doi:10.1016/j.cosrev.2017.07.002 · Cited by 80
Deep Disturbance-Disentangled Learning for Facial Expression Recognition
Ruan, D., Yan, Y., Chen, S., Xue, J., & Wang, H. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3413907 · Cited by 77
“On the one hand, psychologists have shown that salient facial regions (such as mouth, nose, and eyes) play a crucial role for FER [26].”
BackgroundKhan, M. M., Ingleby, M., & Ward, R. D. (2006) · ACM Transactions on Autonomous and Adaptive Systems · doi:10.1145/1152934.1152939 · Cited by 79
“EXPRESSION CLASSIFICATION 5.1 Discriminant Analysis Linear discriminant analysis (LDA) is a widely used method for recognizing faces and affects and classifying facial expression [Fasel and Luettin 2003; Pantic and Rothkrantz 2000; Swets and Weng 1998].”
MethodInfluentialSpontaneous facial expression recognition: A robust metric learning approach
Wan, S., & Aggarwal, J. K. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2013.11.025 · Cited by 74
“For a comprehensive survey, we refer readers to [13].”
BackgroundOccupant-Facade interaction: a review and classification scheme
Luna-Navarro, A., Loonen, R., Juaristi, M., Monge-Barrio, A., Attia, S., & Overend, M. (2020) · Building and Environment · doi:10.1016/j.buildenv.2020.106880 · Cited by 74
“Facial expressions can be used to detect levels of emotions [145], [146] or environmental satisfaction in a contact-less manner, however they may not always be detectable [147].”
BackgroundThe Auto-eFACE: Machine Learning–Enhanced Program Yields Automated Facial Palsy Assessment Tool
Miller, M. Q., Hadlock, T. A., Fortier, E., & Guarín, D. L. (2020) · Plastic & Reconstructive Surgery · doi:10.1097/prs.0000000000007572 · Cited by 75
“17–20 Researchers have used these advances in machine learning techniques to create Emotrics, a high-throughput software platform with automated facial landmark localization and computation of facial measurements.”
MethodGuo, G., & Dyer, C. R. (2003) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2003.1211374 · Cited by 75
Spontaneous Facial Expression Recognition: A Part Based Approach
Perveen, N., Singh, D., & Mohan, C. K. (2016) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2016.0147 · Cited by 74
“[3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13].”
Yang, Y., Gao, Z., Wang, X.-M., Li, Y., Han, J., Marwan, N., & Kurths, J. (2018) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.5023857 · Cited by 74
Hammal, Z., Couvreur, L., Caplier, A., & Rombaut, M. (2007) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2007.02.003 · Cited by 77
Recognition of Facial Expressions in the Presence of Occlusion
Bourel, F., Chibelushi, C. C., & Low, A. A. (2001) · British Machine Vision Conference (BMVC) · doi:10.5244/c.15.23 · Cited by 74
Affective State Level Recognition in Naturalistic Facial and Vocal Expressions
Meng, H., & Bianchi‐Berthouze, N. (2013) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2013.2253768 · Cited by 75
“IN the affective computing field [1], various studies have been carried out to create systems that can recognize the affective states of their user by analyzing their vocal [2], [3], facial [4]–[6], body expressions [7]–[10], touch [11] and even”
BackgroundHybrid-Boost Learning for Multi-Pose Face Detection and Facial Expression Recognition
Chen, H.-Y., Huang, C.-L., & Fu, C.-M. (2007) · IEEE International Conference on Multimedia and Expo · doi:10.1016/j.patcog.2007.08.010 · Cited by 89
“Keywords: Pattern classification; Adaboost; Hybrid-boost; Face detection; Face expression recognition”
BackgroundManifold Based Analysis of Facial Expression
Hu, C., Chang, Y. H., Feris, R. S., & Turk, M. (2005) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvpr.2004.390 · Cited by 74
Modeling Naturalistic Affective States Via Facial, Vocal, and Bodily Expressions Recognition
Karpouzis, K. C., Caridakis, G., Kessous, L., Amir, N., Raouzaiou, A. T., Malatesta, L., & Kollias, S. (2007) · Springer eBooks · doi:10.1007/978-3-540-72348-6_5 · Cited by 72
“A wide variety of machine learning techniques have been used in emotion recognition approaches ([50], [31], [42]).”
BackgroundFoundations of Human Computing: Facial Expression and Emotion
Cohn, J. F. (2007) · Springer eBooks · doi:10.1007/978-3-540-72348-6_1 · Cited by 77
Sun, B., Li, L., Zuo, T., Chen, Y., Zhou, G., & Wu, X. (2014) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2663204.2666272 · Cited by 71
“At early stage researchers mostly focused on emotion analysis from single static facial images under ideal circumstance [2].”
BackgroundFacial-component-based bag of words and PHOG descriptor for facial expression recognition
Li, Z., Imai, J., & Kaneko, M. (2009) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2009.5346254 · Cited by 67
“A survey on the research concerning facial expression recognition can be found in [2-3].”
BackgroundChen, S., Tian, Y., Liu, Q., & Metaxas, D. N. (2013) · Image and Vision Computing · doi:10.1016/j.imavis.2012.06.014 · Cited by 72
Facial emotion recognition in real-time and static images
Gupta, S. (2018) · International Conference on Inventive Systems and Control (ICISC) · doi:10.1109/icisc.2018.8398861 · Cited by 70
“After the extraction of these features, the machine learning algorithms were applied for training and classifying the different emotions [5].”
MethodEffect of illumination on automatic expression recognition: A novel 3D relightable facial database
Stratou, G., Ghosh, A., Debevec, P. E., & Morency, L. (2011) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2011.5771467 · Cited by 69
Exploring facial expressions with compositional features
Yang, P., Liu, Q., & Metaxas, D. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5539978 · Cited by 73
“Thus, in the communities of computer vision and pattern recognition, most of automatic facial expression analysis work only focused on identifying an input facial image or sequence as one of six basic emotions [13][24][8][3] [6][22][16][21][2][27].”
BackgroundAutomatic Facial Expression Analysis A Survey
Sumathi, C. P. (2012) · International Journal of Computer Science & Engineering Survey · doi:10.5121/ijcses.2012.3604 · Cited by 71
“[7] Maja Pantic, Student Member, IEEE, and Leon J.”
BackgroundAn efficient real-time emotion detection using camera and facial landmarks
Nguyen, B. T., Trinh, M. H., Phan, T. V., & Nguyen, H. D. (2017) · International Conference on Information Science and Technology · doi:10.1109/icist.2017.7926765 · Cited by 70
“Pantic and Rothkrantz [4] showed that in order to detect emotions from human images, there were usually three following steps: face detection, feature selection from face images and emotion classification from the extracted features.”
Background3D Facial Expression Recognition Based on Primitive Surface Feature Distribution
Wang, J., Yin, L., Wei, X., & Sun, Y. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.14 · Cited by 307
“There is a long history of interest in the problem of recognizing human emotion from facial expressions, as well as extensive studies on face perception over the last three decades [8, 17].”
BackgroundSparse Representation for accurate classification of corrupted and occluded facial expressions
Cotter, S. F. (2010) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2010.5494903 · Cited by 73
Physiological-Based Affect Event Detector for Entertainment Video Applications
Fleureau, J., Guillotel, P., & Huynh-Thu, Q. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2012.2 · Cited by 72
“Face analysis through an adapted camera would be an alternative [4], but the associated algorithms may be very sensitive to the user environment.”
BackgroundFacial expression recognition from near-infrared video sequences
Taini, M., Zhao, G., Li, S. Z., & Pietikäinen, M. (2008) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2008.4761697 · Cited by 69
“Facial expression recognition research has in many cases based on static images [2-8].”
RankBoost with l1 regularization for facial expression recognition and intensity estimation
Yang, P., Liu, Q., & Metaxas, D. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459371 · Cited by 69
“vision and pattern recognition, most of automatic facial expression analysis works are still based on six basic expressions [20][31][10][4] [8][30][25][29][3].”
BackgroundDifferential optical flow applied to automatic facial expression recognition
Sánchez, Á., Ruiz, J. V., Moreno, A. B., Montemayor, A. S., Hernández, J., & Pantrigo, J. J. (2010) · Neurocomputing · doi:10.1016/j.neucom.2010.07.017 · Cited by 67
“Different survey papers [1] [2] [7] [20] summarize the main facial expression analysis techniques and algorithms.”
BackgroundEfficient smile detection by Extreme Learning Machine
An, L., Yang, S., & Bhanu, B. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.04.072 · Cited by 68
“Six basic facial expressions and emotions that are commonly referred include anger, surprise, disgust, sadness, happiness and fear.”
BackgroundAfzal, S., Khan, H. A., Piran, M. J., & Lee, J. W. (2024) · IEEE Access · doi:10.1109/access.2024.3422480 · Cited by 87
“In another study [75], researchers explores the challenges in developing automated systems for detecting and interpreting facial expressions, aiming for human-like interaction between humans and machines.”
BackgroundLemaire, P., Ardabilian, M., Chen, L., & Daoudi, M. (2013) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2013.6553821 · Cited by 69
Facial Expressions Recognition Based on Cognition and Mapped Binary Patterns
Qi, C., Li, M., Wang, Q., Zhang, H., Xing, J., Gao, Z., & Zhang, H. (2018) · IEEE Access · doi:10.1109/access.2018.2816044 · Cited by 67
“After decades of development, automatic recognition of facial expressions has been summarized as a complete system [41], [43].”
BackgroundEmotion and Reinforcement: Affective Facial Expressions Facilitate Robot Learning
Broekens, J. (2007) · Springer eBooks · doi:10.1007/978-3-540-72348-6_6 · Cited by 66
Pain monitoring: A dynamic and context-sensitive system
Hammal, Z., & Miriam Kunz, A. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.09.014 · Cited by 66
Salient feature and reliable classifier selection for facial expression classification
Kyperountas, M., Tefas, A., & Pitas, I. (2009) · Pattern Recognition · doi:10.1016/j.patcog.2009.07.007 · Cited by 66
Fan, X., & Tjahjadi, T. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.12.002 · Cited by 68
Acoustic feature analysis in speech emotion primitives estimation
Wu, D., Parsons, T. D., & Narayanan, S. S. (2010) · Interspeech (USB) · doi:10.21437/interspeech.2010-285 · Cited by 65
“, speech [1–3], facial expressions [4,5], phy siological signals [6,7], or their multimodal combination [8,9].”
BackgroundIntelligent Virtual Humans with Autonomy and Personality: State-of-the-Art
Kasap, Z., & Magnenat‐Thalmann, N. (2008) · Studies in computational intelligence · doi:10.1007/978-3-540-79868-2_2 · Cited by 65
A Real-Time Facial Expression Recognition System for Online Games
Zhan, C., Li, W., Ogunbona, P., & Safaei, F. (2008) · Int. J. Comput. Games Technol. · doi:10.1155/2008/542918 · Cited by 68
“Fasel and Luttin [4] and Pantic and Rothkrantz [5] published two comprehensive survey papers which summarized the facial expression recognition methods proposed before 1999.”
BackgroundGeneralization of a Vision-Based Computational Model of Mind-Reading
el Kaliouby, R., & Robinson, P. (2005) · Lecture notes in computer science · doi:10.1007/11573548_75 · Cited by 67
“Over the past decade, significant progress has been made with automated facial expression analysis (FEA) (a survey can be found in Pantic and Rothkrantz [15] and in Fasel and Leuttin [6]).”
BackgroundFuzzy case-based reasoning for facial expression recognition
Khanum, A., Mufti, M., Javed, M. Y., & Shafiq, M. Z. (2008) · Fuzzy Sets and Systems · doi:10.1016/j.fss.2008.05.022 · Cited by 64
Human Facial Expression Recognition using a 3D Morphable Model
Ramanathan, S., Kassim, A., Venkatesh, Y. V., & Wah, W. S. (2006) · 2006 International Conference on Image Processing · doi:10.1109/icip.2006.312417 · Cited by 62
“Image-based facial expression analysis [10] consists of 3 steps”
MethodPartial least squares regression on grassmannian manifold for emotion recognition
Liu, M., Wang, R., Huang, Z., Shan, S., & Chen, X. (2013) · Proceedings of the 15th ACM on International conference on multimodal interaction · doi:10.1145/2522848.2531738 · Cited by 62
“…of Intelligence Information Processing Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China {mengyi.liu, ruiping.wang, zhiwu.huang, shiguang.shan, xilin.chen}@vipl.ict.ac.cn ABSTRACT In this paper, we propose a method for video-based human emotion recognition.”
MethodSaha, S., Ghosh, M., Ghosh, S., Sen, S., Singh, P. K., Geem, Z. W., & Sarkar, R. (2020) · Applied Sciences · doi:10.3390/app10082816 · Cited by 63
The emotional hearing aid: an assistive tool for children with Asperger syndrome
el Kaliouby, R., & Robinson, P. (2005) · Universal Access in the Information Society · doi:10.1007/s10209-005-0119-0 · Cited by 63
“A detailed survey of existing approaches and implementations of automated facial analysis is outside the scope of this paper, but the reader is referred to Pantic and Rothkrantz [31].”
BackgroundA review of motion analysis methods for human Nonverbal Communication Computing
Metaxas, D., & Zhang, S. (2013) · Image and Vision Computing · doi:10.1016/j.imavis.2013.03.005 · Cited by 65
“Thepreviousworks of automatic facial expression recognition can be categorized into two main categories: image based methods [60–62] and video-based methods [63,64,53].”
BackgroundA Comparative Study of 3-D Face Recognition Under Expression Variations
Smeets, D., Claes, P. D., Hermans, J., Vandermeulen, D., & Suetens, P. (2011) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2011.2174221 · Cited by 63
“state of the art in expression recognition [18] and expression analysis [18], [19] starting from 2-D images.”
BackgroundFacial expression recognition based on shape and texture
Xie, X., & Lam, K. (2008) · Pattern Recognition · doi:10.1016/j.patcog.2008.08.034 · Cited by 62
Berlin Brain–Computer Interface—The HCI communication channel for discovery
Krepki, R., Curio, G., Blankertz, B., & Müller, K. (2007) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2006.11.010 · Cited by 63
“Recent research tries to include also olfaction (Harel et al., 2003), tactile sensation (MacIntyre and Feiner, 1996; Hardwick et al., 1996), interpretation of facial emotions (Pantic and Rothkrantz, 2000) and gestures (Pentland, 1995; Quek et al., 2002).”
BackgroundSpeech emotion recognition in emotional feedback for Human-Robot Interaction
Javier, G., Sundgren, D., Rahmani, R., Larsson, A., Moran, A., & Bonet, I. (2015) · INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ARTIFICIAL INTELLIGENCE · doi:10.14569/ijarai.2015.040204 · Cited by 62
“…in humans, many of these indicators have been previously analyzed to provide affective knowledge to machines, focusing on facial expressions [5], [6], vocal features [7], [8], [9], body movements and postures [10], [11], [12], [13] and the integration of all of them in emotion analysis…”
BackgroundAutomatic Facial Expression Analysis
Valstar, M. (2014) · Springer eBooks · doi:10.1007/978-81-322-1934-7_8 · Cited by 62
Walecki, R., Rudovic, O., Pavlovic, V., & Pantic, M. (2015) · 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG) · doi:10.1109/fg.2015.7163137 · Cited by 65
“and disgust) and detection of AUs from static facial images [1].”
BackgroundReal-Time Inference of Complex Mental States from Facial Expressions and Head Gestures
El Kaliouby, R., & Robinson, P. (2005) · 2004 Conference on Computer Vision and Pattern Recognition Workshop · doi:10.1109/cvpr.2004.427 · Cited by 282
Matching expression variant faces
Martı́nez, A. M. (2003) · Vision Research · doi:10.1016/s0042-6989(03)00079-8 · Cited by 61
“…for classifying facial expressions of emotions and to identity the AUs of an expression in several previous studies (Bart-lett et al., 1999; Calder, Burton, Miller, Young, & Ak-amatsu, 2001; Donato et al., 1999; Lyons, Budynek, & Akamatsu, 1999; Pantic & Rothkrantz, 2000; Yacoob & Davis, 1996).”
MethodResultChildren's Information Seeking and the Design of Digital Interfaces in the Affective Paradigm
Bilal, D. (2005) · Library trends · doi:10.1353/lib.2006.0013 · Cited by 61
“While this may be true, researchers in artifi cial intelligence and affective computing (Burleson & Picard, 2004; Picard, Vyzas, & Healey, 2001; Pantic & Rothkrantz, 2000) seek to develop intelligent applications that detect a user’s affect while interacting with computers to help cope with negative…”
BackgroundAnnotating and modeling empathy in spoken conversations
Alam, F., Danieli, M., & Riccardi, G. (2017) · Computer Speech and Language · doi:10.1016/j.csl.2017.12.003 · Cited by 71
“The medium may be speech [29], image [30] or multimodal [31].”
Recognising facial expressions in video sequences
Buenaposada, J. M., Muñoz, E., & Baumela, L. (2007) · Pattern Analysis and Applications · doi:10.1007/s10044-007-0084-8 · Cited by 60
“the literature depending on the discriminative information extracted from the image and the classification procedure used (see [26, 45] for a comprehensive survey).”
MethodBodily Expression for Automatic Affect Recognition
Gunes, H., Shan, C., Chen, S., & Tian, Y.-L. (2015) · doi:10.1002/9781118910566.ch14 · Cited by 58
Emotion recognition from facial expressions using hybrid feature descriptors
Kalsum, T., Anwar, S. M., Majid, M., Khan, B., & Ali, S. M. (2018) · IET Image Processing · doi:10.1049/iet-ipr.2017.0499 · Cited by 57
Meng, H., & Bianchi‐Berthouze, N. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24571-8_49 · Cited by 57
“In the affective computing field [12], various studies have been carried out to create systems that can recognize the affective states of their user by analyzing their vocal [1], facial [11] [17], and body expressions [4], and even their physiological changes [6].”
BackgroundAffective Benchmarking of Movies Based on the Physiological Responses of a Real Audience
Fleureau, J., Guillotel, P., & Orlac, I. (2013) · 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction · doi:10.1109/acii.2013.19 · Cited by 61
“Face analysis through an adapted camera could be an alternative, but the associated algorithms [1] may be very sensitive to the user environment (low lighting conditions) and not always adapted to natural “close-to-neutral” facial expressions.”
BackgroundPrecise detailed detection of faces and facial features
Ding, L., & Martı́nez, A. M. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587812 · Cited by 57
“Accurate and detailed facial feature detection is also necessary in the analysis of facial expressions with FACS (Facial Action Coding System) [4, 11] and in the analysis of non-manuals in American Sign Language (ASL) [9].”
MethodCombining Multimodal Features within a Fusion Network for Emotion Recognition in the Wild
Sun, B., Li, L., Zhou, G., Wu, X., He, J. Y. D., Yu, L., Li, D., & Wei, Q. (2015) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2818346.2830586 · Cited by 57
“At the early stage, researchers focused mostly on the emotion analysis from a single static facial image under constrained circumstances [2], resulting in the loss of spatial domain information and not available for practical application scenarios.”
BackgroundCaroppo, A., Leone, A., & Siciliano, P. (2020) · Journal of Computer Science and Technology · doi:10.1007/s11390-020-9665-4 · Cited by 60
A Coarse-to-Fine Classification Scheme for Facial Expression Recognition
Feng, X., Hadid, A., & Pietikäinen, M. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30126-4_81 · Cited by 56
“Recognizing the expressions of novel individual is st ll a challenging task and only few works have addressed this issue [1, 4, 10].”
BackgroundMethodFacial expression recognition in dynamic sequences: An integrated approach
Fang, H., Parthaláin, N. M., Aubrey, A. J., Tam, G. K. L., Borgo, R., Rosin, P. L., Grant, P. W., Marshall, D., & Chen, M. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2013.09.023 · Cited by 57
“This system forms the basis for many expression recognition systems [15, 16, 17, 18].”
BackgroundA psycho-ethological approach to social signal processing
Méhu, M., & Scherer, K. R. (2012) · Cognitive Processing · doi:10.1007/s10339-012-0435-2 · Cited by 58
“The acquisition of these measurements depends on the tools that are currently being developed towards automatic analysis of facial behaviour (Bartlett et al. 1999; Cohn et al. 1999; for a review see Pantic and Rothkrantz 2000) and the extraction of vocal parameters (Boersma 2001).”
MethodNebula feature: A space-time feature for posed and spontaneous 4D facial behavior analysis
Reale, M. J., Zhang, X., & Yin, L. (2013) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2013.6553746 · Cited by 56
“Traditionally, most expression work has focused on 2D images [3], [10] and 2D videos [1], [4], [9], and thus the aforementioned issues remain”
Background3D facial expression recognition with geometrically localized facial features
Soyel, H., & Demirel, H. (2008) · International Symposium on Computer and Information Sciences · doi:10.1109/iscis.2008.4717898 · Cited by 56
“The two recent surveys about facial expression analysis [ 8 , 9] provide detailed review of many of the research done in recent years.”
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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