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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.”
Citations
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Cited by
Sorted by citations
Items 101–200 of 1,000
Multimodal information fusion application to human emotion recognition from face and speech
Mansoorizadeh, M., & Charkari, N. M. (2009) · Multimedia Tools and Applications · doi:10.1007/s11042-009-0344-2 · Cited by 165
“Since then much works have been published that try to computationally extract and interpret facial expressions by applying machine learning methods (see comprehensive reviews by Pantic and Rothkrantz [24] and Fasel and Luitten [12]).”
Deep learning the dynamic appearance and shape of facial action units
Jaiswal, S., & Valstar, M. (2016) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2016.7477625 · Cited by 170
“Automatic facial expression recognition has attracted considerable attention in the past few years [6, 18, 20, 23] due to their potential application in the field of psychology, mental health, human-computer interaction, etc.”
BackgroundDoma, V., & Pirouz, M. (2020) · Journal Of Big Data · doi:10.1186/s40537-020-00289-7 · Cited by 161
“An example of this research is using EEG sensor data to detect emotions [4], alongside using face markers or a fusion of these with body signals like pupil dilation [5].”
MethodFacial Sentiment Analysis Using AI Techniques: State-of-the-Art, Taxonomies, and Challenges
Patel, K., Mehta, D., Mistry, C., Gupta, R., Tanwar, S., Kumar, N., & Alazab, M. (2020) · IEEE Access · doi:10.1109/access.2020.2993803 · Cited by 157
“The current research mainly focuses on 2D FER data, which faces challenges to illumination factors and pose variations [168].”
BackgroundCohen, I., Sebe, N., Gozman, F. G., Cirelo, M. C., & Huang, T. S. (2003) · 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. · doi:10.1109/cvpr.2003.1211408 · Cited by 155
“Recent work on facial expression analysis has used these “basic expressions” or a subset of them (see Pantic and Rothkrantz’s [19] detailed review of many of the research done in recent years).”
MethodEmotion recognition using dynamic grid-based HoG features
Dahmane, M., & Meunier, J. (2011) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2011.5771368 · Cited by 154
“To meet these challenges, various techniques have been developed and applied, that we can divide into two main categories: model– based and image–based approaches [1], [2].”
MethodMultimodal approaches for emotion recognition: a survey
Sebe, N., Cohen, I., Gevers, T., & Huang, T. S. (2005) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.600746 · Cited by 154
Video analysis of human dynamics—a survey
Wang, J. J., & Singh, S. (2003) · Real-Time Imaging · doi:10.1016/j.rti.2003.08.001 · Cited by 151
A comparative study of face landmarking techniques
Çeliktutan, O., Ulukaya, S., & Sankur, B. (2013) · EURASIP Journal on Image and Video Processing · doi:10.1186/1687-5281-2013-13 · Cited by 151
“Some of the approaches that use landmarks for recognizing Action Units are [1,2] and for interpreting head gestures and facial expressions are [16,17].”
BackgroundWu, D., Courtney, C. G., Lance, B. J., Narayanan, S. S., Dawson, M. E., Oie, K. S., & Parsons, T. D. (2010) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2010.12 · Cited by 151
“Arousal, an important dimension of affect, is focused upon because of its close relation to a user’s cognitive performance, as indicated by the Yerkes-Dodson Law.”
BackgroundFacial Expression Recognition Using 3D Facial Feature Distances
Soyel, H., & Demirel, H. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74260-9_74 · Cited by 146
“The two recent surveys about facial expression analysis [ 8 , 9] provide detailed review of many of the research done in recent years.”
A survey of affect recognition methods: audio, visual and spontaneous expressions
Zeng, Z., Pantic, M., Roisman, G. I., & Huang, T. S. (2007) · Proceedings of the 9th international conference on Multimodal interfaces · doi:10.1145/1322192.1322216 · Cited by 144
Facial expression recognition with temporal modeling of shapes
Jain, S. D., Hu, C., & Aggarwal, J. K. (2011) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2011.6130446 · Cited by 140
“A comprehensive survey of some of these techniques may be found in [12].”
BackgroundReal time facial expression recognition with AdaBoost
Wang, Y., Ai, H., Wu, B., & Huang, C. (2004) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2004.1334680 · Cited by 142
“There have been a number of related works on this problem, see [2] for a review.”
BackgroundHow effective are landmarks and their geometry for face recognition?
Shi, J., Samal, A. K., & Marx, D. B. (2006) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2005.10.002 · Cited by 141
“Representation of faces is studied extensively in the vast body of literature in different aspects of face recognition including detection (see Young s recent survey [22]), identification (see [2], for a recent survey), and analysis of facial expression (see Pantic s recent survey [23]).”
BackgroundTalaat, F. M. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08372-9 · Cited by 140
A Unified Probabilistic Framework for Spontaneous Facial Action Modeling and Understanding
Tong, Y., Chen, J., & Ji, Q. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.293 · Cited by 141
“Detailed surveys of previous work can be found in [7], [8], [9], [10], [1], [11].”
Audio–Visual Affective Expression Recognition Through Multistream Fused HMM
Zeng, Z., Tu, J., Pianfetti, B., & Huang, T. S. (2008) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2008.921737 · Cited by 134
“For exhaustive surveys of these efforts in the field, readers are referred to the following articles: • overviews of early work on facial expression analysis: Samal & Iyengar, 1992, [30], and Pantic & Rothkrantz, 2000, [22], • surveys of techniques for automatic facial muscle action recognition and facial expression a…”
BackgroundAdvances in Facial Expression Recognition: A Survey of Methods, Benchmarks, Models, and Datasets
Kopalidis, T., Solachidis, V., Vretos, N., & Daras, P. (2024) · Information · doi:10.3390/info15030135 · Cited by 205
Affective and cognitive design for mass personalization: status and prospect
Zhou, F., Ji, Y., & Jiao, J. R. (2012) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-012-0673-2 · Cited by 135
“More information about facial recognition can be referred to (Pantic and Rothkrantz 2000; Cowie et al. 2001; Fasel and Luettin 2003) for a review.”
BackgroundModeling naturalistic affective states via facial and vocal expressions recognition
Caridakis, G., Malatesta, L., Kessous, L., Amir, N., Raouzaiou, A. T., & Karpouzis, K. C. (2006) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/1180995.1181029 · Cited by 133
“A wide variety of machine learning techniques have been used in emotion recognition approaches ([13], [18], [42]).”
Backgroundde la Torre, F. N., & Cohn, J. F. (2011) · Springer eBooks · doi:10.1007/978-0-85729-997-0_19 · Cited by 141
“For other comprehensive surveys see [44, 95, 111, 133].”
BackgroundReal-Time Inference of Complex Mental States from Facial Expressions and Head Gestures
el Kaliouby, R., & Robinson, P. (2005) · Springer eBooks · doi:10.1007/0-387-27890-7_11 · Cited by 140
Li, S., & Deng, W. (2018) · International Journal of Computer Vision · doi:10.1007/s11263-018-1131-1 · Cited by 134
“One line of approaches (Ding et al, 2013; Eleftheriadis et al, 2015b; Pantic and Rothkrantz, 2000; Zeng et al, 2015) to this complex emotion problem are based on facial action coding system (FACS) analysis (Ekman and Rosenberg, 1997).”
Boosted multi-resolution spatiotemporal descriptors for facial expression recognition
Zhao, G., & Pietikäinen, M. (2009) · Pattern Recognition Letters · doi:10.1016/j.patrec.2009.03.018 · Cited by 129
“Pantic and Rothkrantz (2000) gave an overview of automatic expression recognition, presenting the main system components and some research challenges.”
BackgroundGabor-based kernel PCA with doubly nonlinear mapping for face recognition with a single face image
Xie, X., & Lam, K. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.877435 · Cited by 128
Dynamics of Facial Expression Extracted Automatically from Video
Littlewort, G., Bartlett, M. S., Fasel, I., Susskind, J., & Movellan, J. (2005) · 2004 Conference on Computer Vision and Pattern Recognition Workshop · doi:10.1109/cvpr.2004.327 · Cited by 126
Real-time 2D+3D facial action and expression recognition
Tsalakanidou, F., & Malassiotis, S. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2009.12.009 · Cited by 125
HERO: Human Emotions Recognition for Realizing Intelligent Internet of Things
Hua, W., Dai, F., Huang, L., Xiong, J., & Gui, G. (2019) · IEEE Access · doi:10.1109/access.2019.2900231 · Cited by 125
“Pantic and Rothkrantz [15] considered that FER mainly consists of three steps: face detection, feature extraction, and facial expression recognition.”
BackgroundA new fractal pattern feature generation function based emotion recognition method using EEG
Tuncer, T., Doğan, Ş., & Subaşı, A. (2021) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2021.110671 · Cited by 126
Recognition of Affect in the Wild Using Deep Neural Networks
Kollias, D., Nicolaou, M. A., Kotsia, I., Zhao, G., & Zafeiriou, S. P. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.247 · Cited by 123
“Emotion recognition is a key issue, dealing with multimodal patterns, such as facial expressions, head pose, hand and body gestures, linguistic and paralinguistic acoustic cues, as well as physiological data [19] [5] [18].”
BackgroundThe Ordinal Nature of Emotions: An Emerging Approach
Yannakakis, G. N., Cowie, R., & Busso, C. (2018) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2018.2879512 · Cited by 128
“Early research in affective computing gravitated towards nominal description—associating a sample with an emotion class (‘angry’, ‘happy’, etc.) [3], [4].”
BackgroundUnderstanding vision-based continuous sign language recognition
Aloysius, N., & Geetha, M. K. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08961-z · Cited by 124
A Model Based Method for Automatic Facial Expression Recognition
van Kuilenburg, H., Wiering, M., & den Uyl, M. J. (2005) · Lecture notes in computer science · doi:10.1007/11564096_22 · Cited by 124
“of all the advances on the field of automatic expression analysis can be found in [17] or [10].”
BackgroundFacial Expression Recognition Based on 3D Dynamic Range Model Sequences
Sun, Y., & Yin, L. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88688-4_5 · Cited by 125
“Many successful approaches have utilized Action Units (AU) recognition [2,3,4,5,6,7,8] or Motion Units (MU) detection [9,10,11].”
MethodCascaded Classification of Gender and Facial Expression using Active Appearance Models
Saatçi, Y., & Town, C. P. (2006) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fgr.2006.29 · Cited by 120
“Pantic and Rothkrantz [11] survey the large variety of such techniques and find that all of them consist of three major steps: face detection, a mechanism for extracting facial expression information, and a mechanism to classify the information extracted according to some pre-defined set of categories.”
BackgroundFacial expression recognition using fisher weight maps
Shinohara, Y., & Otsu, N. (2004) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.2004.1301582 · Cited by 117
“Although our experiments focused on the problem of facial expression recognition [8], [2], our method can be applied to other tasks in face image analysis, such as gender classification from a face image.”
MethodAutomatic facial expression recognition on a single 3D face by exploring shape deformation
Gong, B., Wang, Y., Liu, J., & Tang, X. (2009) · Proceedings of the 17th ACM international conference on Multimedia · doi:10.1145/1631272.1631358 · Cited by 116
“In the past two decades, much effort has been paid on 2D expression recognition [3][7].”
BackgroundLocal Directional Texture Pattern image descriptor
Rivera, A. R., Castillo, J. R., & Chae, O. (2014) · Pattern Recognition Letters · doi:10.1016/j.patrec.2014.08.012 · Cited by 117
“The performance of the appearance-based methods is excellent in constrained environment but their performance degrade in environmental variation [6].”
MethodA dependence maximization view of clustering
Song, L., Smola, A. J., Gretton, A., & Borgwardt, K. M. (2007) · International Conference on Machine Learning · doi:10.1145/1273496.1273599 · Cited by 116
“…a Gaussian kernel exp(-sI x - x ' I2) with s = d-1 (d:dimension of the data) for the kernel matrix K. Hierarchical Facial Expression Clustering Computer recognition of facial expressions is an important component in automated understanding of human communication (Pantic & Rothkrantz, 2000).”
BackgroundA Multimodal Emotion Detection System during Human–Robot Interaction
Alonso-Martín, F., Malfáz, M., Sequeira, J. S., Gorostiza, J. F., & Salichs, M. A. (2013) · Sensors · doi:10.3390/s131115549 · Cited by 117
“In [70], an extensive review of different techniques and research related to the visual emotion detection process is presented.”
BackgroundMultimodal Human Computer Interaction: A Survey
Jaimes, A., & Sebe, N. (2005) · Lecture notes in computer science · doi:10.1007/11573425_1 · Cited by 115
“Most facial expression recognition research (see [131] and [47] for two comprehensive reviews) has been inspired by the work of Ekman [43] on coding facial expressions based on the basic movements of facial features called action units (AUs).”
BackgroundFacial expression recognition using Local Directional Pattern (LDP)
Jabid, T., Kabir, M. H., & Chae, O. (2010) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2010.5652374 · Cited by 114
“Performance of the appearance based method is excellent in constrained environment but its performance degrades in environmental variation [10].”
MethodAutomatic Depression Scale Prediction using Facial Expression Dynamics and Regression
Jan, A., Meng, H., Gaus, Y. F. A., Zhang, F., & Turabzadeh, S. (2014) · International Workshop on Audio/Visual Emotion Challenge (AVEC) · doi:10.1145/2661806.2661812 · Cited by 114
“Initial work aims at describing expressions using static features extracted from still face images [21] [24].”
BackgroundReal time facial expression recognition in video using support vector machines
Michel, P., & el Kaliouby, R. (2003) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/958468.958479 · Cited by 114
Sajjad, M., Nasir, M., Ullah, F. U. M., Muhammad, K., Sangaiah, A. K., & Baik, S. W. (2018) · Information Sciences · doi:10.1016/j.ins.2018.07.027 · Cited by 113
Development and Progress in Sensors and Technologies for Human Emotion Recognition
Pal, S., Mukhopadhyay, S. C., & Suryadevara, N. K. (2021) · Sensors · doi:10.3390/s21165554 · Cited by 115
“Sensors are integral parts for sensing various emotions from human beings and transfer them into the database for further processing and results, often happens autonomously [40].”
BackgroundGeneral and Interval Type-2 Fuzzy Face-Space Approach to Emotion Recognition
Halder, A., Konar, A., Mandal, R., Chakraborty, A., Bhowmik, P., Pal, N. R., & Nagar, A. K. (2013) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmca.2012.2207107 · Cited by 111
“Metallinou et al. [71] employed 2168-2216/$31.00 © 2013 IEEE content-sensitive learning for audio-visual emotion recognition.”
InfluentialMULTIMODAL EMOTION RECOGNITION
Sebe, N., Cohen, I., & Huang, T. S. (2005) · Handbook of Pattern Recognition and Computer Vision · doi:10.1142/9789812775320_0021 · Cited by 201
Supervised Committee of Convolutional Neural Networks in Automated Facial Expression Analysis
Pons, G., & Masip, D. (2017) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2017.2753235 · Cited by 110
“Emotion classification algorithms from facial expressions can rely on static images [11], [12] or video sequences [13].”
BackgroundFacial expression recognition using Support Vector Machines
Abdulrahman, M., & Eleyan, A. (2015) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2015.7129813 · Cited by 108
Tang, H., & Huang, T. S. (2008) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.2008.4813304 · Cited by 108
(2019) · doi:10.1109/wacv.2019.00178 · Cited by 151
ERNN: A Biologically Inspired Feedforward Neural Network to Discriminate Emotion From EEG Signal
Khosrowabadi, R., Quek, C. H., Ang, K. K., & Wahab, A. (2013) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2280271 · Cited by 106
“features [14]–[18].”
Texture and shape information fusion for facial expression and facial action unit recognition
Kotsia, I., Zafeiriou, S. P., & Pitas, I. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2007.06.026 · Cited by 106
“A survey on the research made concerning facial expression recognition can be found in [6,7].”
BackgroundMethodThe SEMAINE API: Towards a Standards-Based Framework for Building Emotion-Oriented Systems
Schröder, M. (2010) · Advances in Human-Computer Interaction · doi:10.1155/2010/319406 · Cited by 106
“Common tasks include the analysis of the user’s a ff ective state [1] from the face [2, 3], the voice [4, 5], or physiological measures [6], the evaluation of events in the world according to a ff ective criteria [7], and the generation of emotion-related system behaviour, such as facial expression…”
BackgroundDibeklioğlu, H., Salah, A. A., & Gevers, T. (2015) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2015.2394777 · Cited by 104
“the reader to two comprehensive surveys [2], [3].”
Background1 Facial Expression Recognition with Local Binary Patterns and Linear Programming1
Feng, X., Pietikäinen, M., & Hadid, A. (2008) · Cited by 103
Facial Expression Recognition using Convolutional Neural Network with Data Augmentation
Ahmed, T. U., Hossain, S., Hossain, M. S., Islam, R. U., & Andersson, K. (2019) · International Conference on Informatics, Electronics & Vision (ICIEV) · doi:10.1109/iciev.2019.8858529 · Cited by 105
“As research on facial expression recognition has been conducting for years, research progress on this topic is commendable [3] [4].”
BackgroundPhysiological and subjective responses to articulated robot motion
Kulić, D., & Croft, E. A. (2006) · Robotica · doi:10.1017/s0263574706002955 · Cited by 102
“10 Recently, research has focused on using non-verbal communication, such as eye-gaze, 2,3 facial expressions, 11,12 and physiological signals 10,13–16 for human–robot and human– computer interactions.”
BackgroundFoundations of human computing
Cohn, J. F. (2006) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/1180995.1181043 · Cited by 106
“Facial expression has been a subject of keen study in behavioral science for more than a hundred years[4, 5], and within the past 10 years considerable progress has been made in automatic analysis of facial expression from digital video input [6-8].”
BackgroundMachine analysis of facial behaviour: naturalistic and dynamic behaviour
Pantić, M. (2009) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2009.0135 · Cited by 107
“... that can recognize deliberately produced AUs in face images and even few attempts towards recognition of spontaneously displayed AUs have been recently reported as well [4], (see also section 4). While the older methods employ simple approaches including expert rules and machine learning methods such as neural net…”
BackgroundMethodInfluentialAuthentic facial expression analysis
Sebe, N., Lew, M. S., Cohen, I., Sun, Y., Gevers, T., & Huang, T. S. (2004) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.2004.1301585 · Cited by 101
Facial expression recognition based on local binary patterns
Feng, X., Pietikäinen, M., & Hadid, A. (2007) · Pattern Recognition and Image Analysis · doi:10.1134/s1054661807040190 · Cited by 101
“Approaches to facial expression analysis from both static images and image sequences have been proposed in the literature [ 1 , 2].”
MethodInfluentialLearning Discriminative LBP-Histogram Bins for Facial Expression Recognition
Shan, C., & Gritti, T. (2008) · British Machine Vision Conference (BMVC) · doi:10.5244/c.22.27 · Cited by 101
“Machine analysis of facial expressions, enabling computers to analyze and interpret facial expressions as humans do, has many applications such as human-computer interaction and computer animation; so it has attracted much attention in last two decades [ 9 , 3].”
BackgroundAn efficient multimodal 2D + 3D feature-based approach to automatic facial expression recognition
Li, H., Ding, H., Huang, D., Wang, Y., Zhao, X., Morvan, J., & Chen, L. (2015) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2015.07.005 · Cited by 99
Visual learning of texture descriptors for facial expression recognition in thermal imagery
Hernández, B., Olague, G., Hammoud, R., Trujillo, L., & Romero, E. (2007) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2006.08.012 · Cited by 97
“The FER problem has also received much research attention, as can be noted by survey by Pantic and Rothkrantz [21], and more recently by Fasel and Luettin [7].”
BackgroundSpontaneous Emotional Facial Expression Detection
Zeng, Z., Fu, Y., Roisman, G. I., Wen, Z., Hu, Y., & Huang, T. S. (2006) · Journal of Multimedia · doi:10.4304/jmm.1.5.1-8 · Cited by 97
Understanding Discrete Facial Expressions in Video Using an Emotion Avatar Image
Yang, S., & Bhanu, B. (2012) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2012.2192269 · Cited by 98
Junior, R. M. C., Bengoetxea, E., Isabelle Bloch, A., & Larrañaga, P. (2005) · Pattern Recognition · doi:10.1016/j.patcog.2005.05.007 · Cited by 97
Muscular Movement Model-Based Automatic 3D/4D Facial Expression Recognition
Zhen, Q., Huang, D., Wang, Y., & Chen, L. (2016) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2016.2557063 · Cited by 97
“images and videos [3]–[10].”
Facial Emotion Distribution Learning by Exploiting Low-Rank Label Correlations Locally
Jia, X., Zheng, X., Li, W., Zhang, C., & Li, Z. (2019) · 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.2019.01007 · Cited by 97
“Due to its wide range of application, such as human-computer interaction [21] and data-driven animation [19], automatic facial expression recognition has attracted significant attention in recent years.”
BackgroundFacial expression recognition using emotion avatar image
Yang, S., & Bhanu, B. (2011) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2011.5771364 · Cited by 96
“A literature review shows that early stage research on facial expression recognition focused on static images [1].”
BackgroundAudio-Visual Emotion Recognition Using Gaussian Mixture Models for Face and Voice
Metallinou, A., Lee, S., & Narayanan, S. S. (2008) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2008.40 · Cited by 97
“Human expression is communicated through various channels, producing complementary and redundant information that is used to resolve problems when one of the modalities is not properly transmitted (e.g., speech in noisy environment)[18].”
BackgroundDynamic Facial Expression Recognition Using Longitudinal Facial Expression Atlases
Guo, Y., Zhao, G., & Pietikäinen, M. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33709-3_45 · Cited by 96
“Over the last decade, facial expression recognition based on the analysis of static images has received lots of attentions [1,2,3].”
BackgroundGenerating Facial Expressions with Deep Belief Nets
Joshua, M., Geoffrey, E., Javier, R. A. V., & Adam, K. (2008) · doi:10.5772/6167 · Cited by 95
“Additionally, a mixture of spontaneous and posed facial actions were collected from subjects in the MMI database (Pantic & Rothcrantz, 2000).”
MethodFacial expression recognition on multiple manifolds
Xiao, R., Zhao, Q., Zhang, D. D., & Shi, P. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.07.017 · Cited by 96
Driver fatigue recognition based on facial expression analysis using local binary patterns
Zhang, Y., & Hua, C. (2015) · Optik · doi:10.1016/j.ijleo.2015.08.185 · Cited by 96
Facial emotion recognition using empirical mode decomposition
Ali, H., Hariharan, M., Yaacob, S., & Adom, A. H. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.08.049 · Cited by 96
Boosting encoded dynamic features for facial expression recognition
Yang, P., Liu, Q., & Metaxas, D. (2008) · Pattern Recognition Letters · doi:10.1016/j.patrec.2008.03.014 · Cited by 94
Comparative evaluation of 3D vs. 2D modality for automatic detection of facial action units
Savran, A., Sankur, B., & Taha Bilge, M. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2011.07.022 · Cited by 111
Meng, H., Bianchi‐Berthouze, N., Deng, Y. S., Cheng, J., & Cosmas, J. (2015) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2015.2418092 · Cited by 94
“Work has focused on most modalities that people and animals consciously or unconsciously use to communicate or detect emotions: vocal (see [6], [7]), facial (see [8]–[10]), body expressions (see [11]), touch behaviors (see [12]), physiological (see [13], [14]) and neurological activation patterns (see [15]–[17]), or m…”
BackgroundGabor wavelet transform based facial expression recognition using PCA and LBP
Abdulrahman, M., Gwadabe, T., Abdu, F. J., & Eleyan, A. (2014) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2014.6830717 · Cited by 91
“( ) ( ) ( ), , , , , ,m n x y I x y x yϕ ψ ϖ θ= ∗ (3) In practice usually the magnitude of the convolution output Gabor feature vector representation is used for FER, because they do vary slowly with the displacement while the phases are very sensitive under small displacement [18].”
InfluentialNuovo, A. G. D., Conti, D., Trubia, G., Buono, S., & Nuovo, S. D. (2018) · Robotics · doi:10.3390/robotics7020025 · Cited by 91
“Human face localization plays an essential role in a countless number of applications such as human-computer and human–robot interaction systems [27,31], pilot/driver attention levels [32,33], video surveillance [34], face recognition [35], and facial expression analysis [36,37].”
BackgroundEmotion Recognition from Arbitrary View Facial Images
Zheng, W., Tang, H., Lin, Z., & Huang, T. S. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15567-3_36 · Cited by 91
“One may refer to [3][4][5][6] for a survey.”
BackgroundNouse ‘use your nose as a mouse’ perceptual vision technology for hands-free games and interfaces
Gorodnichy, D. O., & Roth, G. (2004) · Image and Vision Computing · doi:10.1016/j.imavis.2004.03.021 · Cited by 91
“40 20 10 5 Eye size 20 10 5 2 Nose size 10 5 – – FS X X X m þ [34,35] þ[36] þ FD X X m – þ [35,37] þ þ [1,2,38,39] FT X X m – þ [40] þ þ [3] FL X m – – þ[41] þ [4] FER X X m – þ þ þ [20,42] FC X X m – þ43 FM/I X X – – þ [44]”
Bourel, F., Chibelushi, C. C., & Low, A. A. (2003) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/afgr.2002.1004141 · Cited by 93
“Although the automated recognition of facial expressions has been studied with much interest in the past 10 years [6, 19, 21], it is still a challenging task for a computer program.”
BackgroundComputer analysis of face beauty: A survey
Laurentini, A., & Bottino, A. (2014) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2014.04.006 · Cited by 90
Smile detection by boosting pixel differences
Shan, C. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2161587 · Cited by 90
Classification of emotions induced by music videos and correlation with participants’ rating
Daimi, S. N., & Saha, G. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.03.050 · Cited by 90
“Emotion recognition techniques have been developed based on facial expression (Pantic & Rothkrantz, 2000), speech (Busso et al., 2004), and physiological signals like heart rate and galvanic skin response (GSR) (Lisetti & Nasoz, 2004; Picard, Vyzas, & Healey, 2001).”
BackgroundJoint Patch and Multi-label Learning for Facial Action Unit and Holistic Expression Recognition
Zhao, K., Chu, W.-S., de la Torre, F. N., Cohn, J. F., & Zhang, H. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2570550 · Cited by 91
“See [1], [6], [10], and [26] for comprehensive reviews.”
BackgroundImproved Facial Expression Recognition Based on DWT Feature for Deep CNN
Ilyas, B. R., Beladgham, M., Merit, K., & Ahmed, A. T. (2019) · Electronics · doi:10.3390/electronics8030324 · Cited by 90
Evolutionary feature synthesis for facial expression recognition
Yu, J., & Bhanu, B. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.07.026 · Cited by 90
“Next, relevant features from the face must be extracted, and finally the expression can be classified based on the extracted features (Daugman, 1997; Pantic and Rothkrantz, 2000).”
BackgroundAutomated video-based facial expression analysis of neuropsychiatric disorders
Wang, P., Barrett, F. S., Martin, E. A., Milonova, M., Gur, R. E., Gur, R. C., Kohler, C. G., & Verma, R. (2007) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2007.09.030 · Cited by 90
Höfling, T. T. A., Gerdes, A. B. M., Föhl, U., & Alpers, G. W. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.01388 · Cited by 96
“Recent advances in technology have enabled emotion researchers to obtain AU activity and consecutive emotion measurements automatically through analysis of video and photo recordings (Pantic and Rothkrantz, 2000; Cohn and Sayette, 2010).”
BackgroundInfluence of music liking on EEG based emotion recognition
Daimi, S. N., & Saha, G. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102251 · Cited by 93
Wang, J. Z., Zhao, S., Wu, C., Adams, R. B., Newman, M. G., Shafir, T., & Tsachor, R. P. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3273517 · Cited by 107
“Readers interested in traditional methodologies are advised to refer to survey articles [249], [250], [251], [252].”
BackgroundMAE-DFER: Efficient Masked Autoencoder for Self-supervised Dynamic Facial Expression Recognition
Sun, L., Lian, Z., Liu, B., & Tao, J. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612365 · Cited by 107
Meticulously detailed eye region model and its application to analysis of facial images
Moriyama, T., Takeo Kanade, A., Xiao, J., & Cohn, J. F. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.98 · Cited by 91
“The FacialActionCodingSystem(FACS[9]),thedefactostandard for coding facial muscle actions in behavioral science [10], defines many action units (AUs) for eyes [11], [ 12 ].”
BackgroundPredicting term-relevance from brain signals
Eugster, M. J. A., Ruotsalo, T., Spapé, M., Kosunen, I., Barral, O., Ravaja, N., Jacucci, G., & Samuel Kaski, A. (2014) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2600428.2609594 · Cited by 89
“Recently, sensory signals have been utilized to measure human emotion and map emotional states to predict relevance [27, 28].”
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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