The DOI matches its Crossref record; 107 of the 111 references it cites were found in a registry or an index; none of the 124 sources its claims cite is checked for support yet.
Automatic analysis of facial expressions: the state of the art
Open accessAbstract
Humans detect and interpret faces and facial expressions in a scene with little or no effort. Still, development of an automated system that accomplishes this task is rather difficult. There are several related problems: detection of an image segment as a face, extraction of the facial expression information, and classification of the expression (e.g., in emotion categories). A system that performs these operations accurately and in real time would form a big step in achieving a human-like interaction between man and machine. The paper surveys the past work in solving these problems. The capability of the human visual system with respect to these problems is discussed, too. It is meant to serve as an ultimate goal and a guide for determining recommendations for development of an automatic facial expression analyzer.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This survey reviews the state of the art (through 2000) in automatic facial expression analysis, decomposing the problem into face detection, facial expression data extraction, and facial expression classification. It compares surveyed systems against an "ideal analyzer" modeled on the human visual system and concludes that none of the three subproblems has been solved in the general case.
Key findings
- The paper identifies three related subproblems — face detection, facial expression information extraction, and facial expression classification — and argues an ideal system must automate all three, ideally in real time for HCI use.
- Early systems (1991–1995) achieved correct recognition rates of 70–92 percent when recognizing 3–8 emotion categories; none performed quantified expression classification in terms of facial actions, and only Moses et al. [57] performed in real time.
- Among the surveyed recent systems, reported recognition rates include 92 percent (Yoneyama et al., four expressions), 89 percent for familiar subjects and 73 percent for unknown persons (Hong et al.), 84.5 percent (Huang and Huang), 92 percent generalization / 75 percent for a novel subject (Lyons et al.), 90.1 percent (Zhang et al.), 92 percent upper-face AUs and 86 percent lower-face AUs (Pantic and Rothkrantz), 92/88/83 percent for eyebrow/eye/nose-mouth regions (Cohn et al.), 98 percent (Essa and Pentland), 95 percent (Wang et al.), and 88 percent (Black and Yacoob).
- Most systems rely on frontal-view images of faces without facial hair or glasses, assume constant illumination and immobile subjects, and perform only singular classification into Ekman's six basic emotion categories; only two systems ([62], [106]) perform quantified classification into multiple emotion categories, and none quantifies facial action codes or distinguishes all 44 FACS AUs.
Methods
- Design: a survey/comparative review of vision-based automatic facial expression analysis, covering work from the mid-1970s onward with emphasis on the late 1990s; systems are tabulated (Tables 2–8) against properties of an "ideal analyzer" (Table 1).
- Scope: selectively discusses systems addressing face detection, facial expression data extraction (template-based vs. feature-based; holistic, analytic, and hybrid face representations), and classification (template-, neural-network-, and rule-based methods) from both static images and image sequences.
- Analysis: qualitative comparison of reported techniques, assumptions, and recognition rates; the authors deliberately refrain from ranking systems, arguing that no single common testing database exists.
Limitations
- Stated by the paper: no single well-defined, commonly used testing database exists, so the authors deliberately do not rank systems objectively; several surveyed methods were tested only on the images used for training, so their person-independence is uncertain; most systems assume frontal views, constant illumination, no facial hair or glasses, and immobile subjects, which is unrealistic for the target application domains; only two systems handle automatic face detection in an arbitrary scene and only three deal to some extent with rigid head motions; only Essa and Pentland's method handles faces with facial hair and/or eyeglasses; no system performs quantification of facial action codes or distinguishes all 44 FACS AUs.
- Evident from the text: the survey is selective rather than exhaustive, so coverage of the field is incomplete by the authors' own statement; the "ideal analyzer" is a normative construct rather than an implemented benchmark, and the comparison is largely qualitative with heterogeneous, non-comparable evaluation protocols across systems.
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Claims the paper makes
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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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesNone of the sources cited for this claim have been checked yet.
In the paper
“In general, three types of face representation are mainly used in facial expression analysis: holistic (e.g., isodensity maps [38]), analytic (e.g., deformable templates [105]), and hybrid (e.g., analytic-to-holistic approach [45]).”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.2 Facial Expression Data Extraction
What the sources say
- Not checked yetcites [38]
- Not checked yetcites [105] source
- Not checked yetcites [45]
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: passesNone of the sources cited for this claim have been checked yet.
In the paper
“For most of the work in automatic facial expression analysis, the conditions under which an image is obtained are controlled.”
§ 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: 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 neural-network-based classification methods utilized by the surveyed systems perform facial expression classification into a single category.”
§ 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: passesIn the paper
“Also, successfulness of the method is strongly constrained (Section 4.1.1).”
§ 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:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn the paper
“A shortcoming of the proposed face representation is that the facial appearance changes encountered in a horizontal direction cannot be modeled.”
§ 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: unclear✓ Based: passesIn the paper
“As a consequence, their method is not sensitive to unilateral appearance changes of the left eye.”
§ 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 [59] source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn the paper
“The method will not work out if the teeth are visible, i.e., if there is no dark region between the lips.”
§ 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 calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn the paper
“Hence, the method will fail to recognize any facial appearance change that involves a horizontal movement of the facial features.”
§ 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: 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
“Only one of the surveyed methods for automatic facial expression analysis from image sequences utilizes an analytic face representation (Table 3, Table 5) and applies a feature-based method for facial expression information extraction.”
§ 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 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 rule-based classification methods, utilized by the surveyed systems, classify the examined facial expression into the basic emotion categories based on the previously encoded facial actions (Table 7, Table 8).”
§ 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: unclear✓ Based: passesNone of the sources cited for this claim have been checked yet.
In the paper
“only two systems deal with the automatic face detection in an arbitrary scene ( [30], [24]).”
§ Automatic Analysis of Facial Expressions: The State of the Art > 5.1 Detection of the Face and Its Features
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesNone of the sources cited for this claim have been checked yet.
In the paper
“Only two of the surveyed systems process images acquired by a mounted camera ( [59], [62])”
§ Automatic Analysis of Facial Expressions: The State of the Art > 5.1 Detection of the Face and Its Features
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
“Just one of the surveyed methods for automatic facial expression analysis from image sequences applies a rulebased approach to expression classification.”
§ 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: 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
“Also, none of the surveyed systems can distinguish all 44 AUs defined in FACS.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 5.2 Facial Expression Classification
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ 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
“Yet, none of these systems performs quantification of the facial action codes.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 5.2 Facial Expression Classification
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ 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
“Four of the surveyed systems perform facial action coding from an input image or an image sequence (Table 7).”
§ Automatic Analysis of Facial Expressions: The State of the Art > 5.2 Facial Expression Classification
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
“Just one of the surveyed methods for automatic facial expression analysis from static images applies a rule-based approach to expression classification.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.3.3 Expression Classification from Static Images: Rule-Based Methods
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn the paper
“Yet, only the method proposed by Essa and Pentland [24] deals with the facial images of faces with facial hair and/or eyeglasses.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 5.1 Detection of the Face and Its Features
What the sources say
- Not checked yetcites [24] source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesNone of the sources cited for this claim have been checked yet.
In the paper
“Only three of the surveyed systems deal to some extent with rigid head motions ([2], [18], [30]).”
§ Automatic Analysis of Facial Expressions: The State of the Art > 5.1 Detection of the Face and Its Features
What the sources say
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn the paper
“ªLip bitingº is sometimes mistakenly identified as a smile [2].”
§ 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
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn the paper
“The proposed method has been unsuccessfully tested for image sequences of unknown 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 [41]
Strength:plain· not yet calibratedSingle caseTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn the paper
“In general, the existing expression analyzers perform a singular classification of the examined expression into one of the basic emotion categories proposed by Ekman and Friesen [20].”
- Also stated in: § 6 CONCLUSION · plain
“Also, all of the proposed approaches to automatic expression analysis perform only facial expression classification into the basic emotion categories defined by Ekman and Friesen [20].”
§ Automatic Analysis of Facial Expressions: The State of the Art > 5.2 Facial Expression Classification
What the sources say
- Not checked yetcites [20] source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Also stated in: § 6 CONCLUSION · plain
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 addition, in many instances strong assumptions are made to make the problem of facial expression analysis more tractable (Table 6).”
§ Automatic Analysis of Facial Expressions: The State of the Art > 5.1 Detection of the Face and Its Features
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
“However, in many instances, the systems do not utilize a camera setting that will ascertain the correctness of that assumption.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 5.1 Detection of the Face and Its Features
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 currently existing systems for facial expression analysis assume that the presence of a face in the scene is ensured.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 5.1 Detection of the Face and Its Features
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesNone of the sources cited for this claim have been checked yet.
In the paper
“Only two of the surveyed systems ( [62], [106]) perform quantified facial expression classification into multiple basic emotion categories.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 5.2 Facial Expression Classification
What the sources say
- Not checked yetcites [62] source
- Not checked yetcites [106]
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ 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
“Furthermore, some of the surveyed methods have been tested only on the set of images used for training.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 6 CONCLUSION
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ 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 surveyed systems, however, are based on frontal view images of faces without facial hair and glasses what is unrealistic to expect in these application domains.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 6 CONCLUSION
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ 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
“Among the problems, facial expression classification has been studied most, due to its utility in application domains of human behavior interpretation and HCI.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 6 CONCLUSION
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn the paper
“The presence of the features and their geometrical relationship with each other appears to be more important than the details of the features [5].”
§ Automatic Analysis of Facial Expressions: The State of the Art > 2.1 Face Detection
What the sources say
- Not checked yetcites [5] source
Strength:hedged· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn the paper
“Considerable research in social psychology has also shown that facial expressions help coordinate conversation [4], [82], and have considerably more effect on whether a listener feels liked or disliked than the speaker's spoken words [55].”
§ Automatic Analysis of Facial Expressions: The State of the Art > 1 INTRODUCTION
What the sources say
- Not checked yetcites [55] source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesNone of the sources cited for this claim have been checked yet.
In the paper
“The experiments suggest that the visual properties of the face, regarding the information about the shown facial expression, could be made clear by describing the movements of points belonging to the facial features (eyebrows, eyes, and mouth) and then by analyzing the relationships between those movements.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 2.2 Facial Expression Data Extraction
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA 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
“This implies that the facial expressions form the major modality in human communication.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 1 INTRODUCTION
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesNone of the sources cited for this claim have been checked yet.
In the paper
“Considerable research in social psychology has also shown that facial expressions help coordinate conversation [4], [82], and have considerably more effect on whether a listener feels liked or disliked than the speaker's spoken words [55].”
§ Automatic Analysis of Facial Expressions: The State of the Art > 1 INTRODUCTION
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesNone of the sources cited for this claim have been checked yet.
In the paper
“Finally, there is now a growing psychological research that argues that timing of facial expressions is a critical factor in the interpretation of expressions [1], [5], [34].”
§ Automatic Analysis of Facial Expressions: The State of the Art > 2.3 Facial Expression Classification
What the sources say
Strength:plain· not yet calibratedCausal:correlational· 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
“There is no uniquely defined description either in terms of facial actions or in terms of some other universally defined facial codes.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 2.3 Facial Expression Classification
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ 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
“Also, it is definitely not the case that each and every facial expression able to be displayed on the face can be classified into one and only one emotion class.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 3 AN IDEAL SYSTEM FOR FACIAL EXPRESSION ANALYSIS
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“This ratio varies depending on several factors: the familiarity with the face, the familiarity with the personality of the observed person, the general experience with different types of expressions, the attention given to the face and the nonvisual cues (e.g., the context in which an expression appears).”
§ Automatic Analysis of Facial Expressions: The State of the Art > 2.3 Facial Expression Classification
What the sources say
- Not checked yetcites [1] source
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn the paper
“Second, it is important to realize that the interpretation of the body language is situation-dependent [75].”
§ Automatic Analysis of Facial Expressions: The State of the Art > 2.3 Facial Expression Classification
What the sources say
- Not checked yetcites [75] source
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn the paper
“The face model used by Wang et al. represents a way of improving the labeled-graph-based models (e.g., [30]) to include intensity measurement of the encountered facial expressions based on the information stored in the links between the nodes.”
§ 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 [30] source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn the paper
“Experimentally, they found out that the first two terms of eigenvalues could represent more than 90 percent of APs variations.”
§ 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 [32]
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“The proposed method has been tested on another 90 images shown by the same subjects.The achieved correct recognition ratio was 84.5 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 [32]
Strength:plain· not yet calibratedn = 90Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesNone of the sources cited for this claim have been checked yet.
In the paper
“Recognition of nonprototypic facial expressions is feasible, however, if each neural network output unit is associated with a weight from the interval [0,1], as proposed in [106], [44], [71], [56], instead of being associated with either 0 or 1 (e.g., [42], [107]).”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.3 Facial Expression Classification
What the sources say
- Not checked yetcites [106]
- Not checked yetcites [44]
- Not checked yetcites [71] source
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA 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
“In general, it is difficult to achieve a template-based quantified recognition of a nonprototypic facial expression.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.3 Facial Expression Classification
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn 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: unclear✓ Based: passesIn the paper
“The achieved recognition rate was 90.1 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 [106]
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA 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 reason lies in the rules used for classification.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 4.3.5 Expression Classification from Image Sequences: Rule-Based Methods
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn the paper
“The performance of the network is not tested for recognition of expression of a novel subject.”
§ 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 [106]
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn the paper
“When tested on 52 frontal-view image sequences of eight people showing six distinct expressions, a correct recognition rate of 98 percent has been achieved.”
§ 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 [24] source
Strength:plain· not yet calibratedn = 8Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesNone of the sources cited for this claim have been checked yet.
In the paper
“Some experimental proofs can be found in the studies of Asian researchers (e.g., [32], [106]), which reported that their Asian subjects have difficulties to express some of the basic expressions (e.g., disgust and fear).”
§ Automatic Analysis of Facial Expressions: The State of the Art > 5.2 Facial Expression Classification
What the sources say
- Not checked yetcites [32]
- Not checked yetcites [106]
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA 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
“Therefore, classification of an expression into a single emotion category isn't realistic.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 5.2 Facial Expression Classification
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Rests on an assumption: Pure emotional expressions are seldom elicited.
- Rests on an assumption: Most of the time, people show blends of emotional expressions.
None of the sources cited for this claim have been checked yet.
In the paper
“Second, it is not at all certain that all facial expressions displayed on the face can be classified under the six basic emotion categories.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 5.2 Facial Expression Classification
What the sources say
- Not checked yetcites [33], [76], [77], and [26]
- Not checked yetcites [32], [106]
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIn the paper
“The average correct recognition rate achieved was 86 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 [61] source
Strength:plain· not yet calibratedn = 12Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA 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
“So even if an expression analyzer performs a quantified expression classification into multiple basic emotion categories, it would probably not be capable of interpreting each and every encountered expression.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 5.2 Facial Expression Classification
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA 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
“Therefore, it is not known how the method will behave in the case of an unknown expresser.”
§ 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 = 2Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA 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 hesitate in belief that those systems are personindependent what, in turn, should be a basic property of a behavioral science research tool or of an advanced HCI.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 6 CONCLUSION
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA 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
“Most of the surveyed systems, however, are based on frontal view images of faces without facial hair and glasses what is unrealistic to expect in these application domains.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 6 CONCLUSION
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA 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
“All the discussed problems are intriguing and none has been solved, in the general case.”
§ Automatic Analysis of Facial Expressions: The State of the Art > 6 CONCLUSION
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes? Based: unclear
- 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
Cites
Kass, M., Witkin, A., & Terzopoulos, D. (1988) · International Journal of Computer Vision · doi:10.1007/bf00133570 · Cited by 17,330
Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (1994) · Cited by 15,411
Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
Numerical recipes in Pascal: the art of scientific computing
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Flagged first, then by citations
4 of the 111 references couldn’t be matched to a known paper.
Research works printed but not linked: 13
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Cited by
Sorted by citations
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Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions
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“Much progress has been made in the last decade, and a thorough survey of the exiting work can be found in [ 1 ,2].”
BackgroundA Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
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“There are two major approaches to automated face recognition [43].”
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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