Spotting segments displaying facial expression from image sequences using HMM

Otsuka, Takahiro; Ohya, Jun

doi:10.1109/afgr.1998.670988

Cited by 37 publications

(22 citation statements)

References 13 publications

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“…The approaches to facial expression recognition can be divided into two classes in many different ways. In one way, they can be classified into static-image-based approaches (e.g., [10]) and image sequence-based approaches (e.g., [2], [7]- [8], [11], [17], [20]- [22], etc). While the static-image-based approach classifies expressions based on a single image, the image sequence-based approach utilizes the motion information in an image sequence.…”

Section: Introductionmentioning

confidence: 99%

An SOM-based Automatic Facial Expression Recognition System

Su¹,

Yang²,

Lin³

et al. 2013

IJSCAI

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Recently, a number of applications of automatic facial expression recognition systems havesurfaced in many different research fields. The automatic facial expression recognition problem is a very challenging problem because it involves in three sub-problems: 1) face detection, 2) facial expression feature extraction, and 3) expression classification. This paper presents an automatic facial expression recognition system based on self-organizing feature maps, which provides an effective solution to the aforementioned three sub-problems. The performance of the proposed system was computed on twowell-known facial expression databases.The average correct recognition rates were over 90%.

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Section: Introductionmentioning

confidence: 99%

An SOM-based Automatic Facial Expression Recognition System

Su¹,

Yang²,

Lin³

et al. 2013

IJSCAI

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“…Efforts have recently turned to expression classification by image processing of video sequences [1], [7], [9], [8], [14]. Our approach is more in line with FACS [5] and Ekman's work on prototypic emotional facial expressions [6] -we perform expression classification in photographs.…”

Section: Facial Data Extractormentioning

confidence: 55%

“…In comparison to the existing approaches to automatic facial action coding [2], [1], [7], and facial expression emotional interpretation [1], [4], [8], [9], [14], [15], the system presented in this paper is fundamentally different. First it brings together diverse theories and technologies -FACS [5], studies on facial expression of emotion [6], [13], image analysis and various AI methodologies [18], [12].…”

Section: Discussionmentioning

confidence: 99%

“…FACIAL EXPRESSION CLASSIFIER Nowadays psychological theories on facial expressions of emotions are ambiguous, doubtful and most of all inadequate for interpretation of each and every facial expression able to be displayed on human face [17]. Besides, most of the explicit attempts to automate facial expression emotional interpretation use culture-specific emotion terminology such as "angry" or "sad" [1], [4], [7], [8], [9], [14], [15], instead of linguistic universals such as "he means bad" or "he feels disappointed". In order to facilitate a valuable interpretation of facial expressions in a user-defined domain, we should allow the user to define his/her own facial expression interpretation space.…”

Section: Post-processormentioning

confidence: 99%

“…Most of the existing studies on automatic facial expression analysis perform a singular classification into one of the six basic emotion categories as defined by Ekman [6] (anger, disgust, happiness, fear, surprise and sadness). Some recent examples of automatic [1], Huang et al [9], Hong et al [8], Otsuka et al [14], Edwards et al [4].…”

Section: Introductionmentioning

confidence: 99%

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Self-adaptive expert system for facial expression analysis

Pantić

Rothkrantz²

SMC 2000 Conference Proceedings. 2000 IEEE International Conference on Systems, Man and Cybernetics. 'Cybernetics Evolving to S

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Abstract--We attempt to automate facial expression recognition and introduce it into the man-machine interaction as a new modality. This will make the interaction compact and more efficient. As the first step, we developed a self-adaptive expert system that accepts the facial features contours localized in a static dual-view facial image and returns the expression interpretation label(s) used by the user. Expression identification in terms of the encountered facial actions is also displayed to the user. Reasoning with uncertainty about the extracted facial expression data is employed for facial action coding and quantification. A memory of experiences, inspired by the Shank's theory of human autobiographical memory organization and the instance-based learning, expounds the encoded facial actions in terms of the learned interpretation labels. Validation studies on the prototype suggest that the expressions' identifications and interpretations achieved are generally consistent with those defined by the users.

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