Processing of facial information by computer

Hasegawa, Osamu; Morishima, Shigeo; Kaneko, Masahide

doi:10.1002/(sici)1520-6440(199810)81:10<36::aid-ecjc5>3.0.co;2-p

Cited by 7 publications

(3 citation statements)

References 20 publications

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“…3) Search for a Kohonen layer unit to minimize d n,m , which is referred to as a winner unit. 4) Update weights w i n,m (t) and w j n,m (t) of a Kohonen layer unit contained in the neighborhood region of the winner unit N c (t) using (4) and (5), where α(t), β(t) are learning coefficients, and t j (t) is a teaching signal to the Grossberg layer unit j.…”

Section: B Counter Propagation Network (Cpn)mentioning

confidence: 99%

Extraction of Subject-Specific Facial Expression Categories and Generation of Facial Expression Feature Space using Self-Mapping

Ishii¹,

Sato²,

Madokoro³

et al. 2008

JMM

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This paper proposes a generation method of a subject-specific Facial Expression Map (FEMap) using the Self-Organizing Maps (SOM) of unsupervised learning and Counter Propagation Networks (CPN) of supervised learning together. The proposed method consists of two steps. In the first step, the topological change of a face pattern in the expressional process of facial expression is learned hierarchically using the SOM of a narrow mapping space, and the number of subject-specific facial expression categories and the representative images of each category are extracted. Psychological significance based on the neutral and six basic emotions (anger, sadness, disgust, happiness, surprise, and fear) is assigned to each extracted category. In the latter step, the categories and the representative images described above are learned using the CPN of a large mapping space, and a category map that expresses the topological characteristics of facial expression is generated. This paper defines this category map as an FEMap. Experimental results for six subjects show that the proposed method can generate a subject-specific FEMap based on the topological characteristics of facial expression appearing on face images.

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Section: B Counter Propagation Network (Cpn)mentioning

confidence: 99%

Extraction of Subject-Specific Facial Expression Categories and Generation of Facial Expression Feature Space using Self-Mapping

Ishii¹,

Sato²,

Madokoro³

et al. 2008

JMM

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“…In respect of facial expression caused by emotion, several methods [14][15][16] which automatically recognize or estimate a subject's emotional condition from facial expression images, have already been studied. Mase [17] explained that a process for recognizing emotions from facial expression images comprises the following five phases:…”

Section: Introductionmentioning

confidence: 99%

A Study of Facial Muscular Activities in Drowsy Expression

Ishida

Ichimura

Kamijo

2010

Kansei Engineering International Journal

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This paper describes facial muscular activities in drowsy expression. We measured facial images and facial electromyograms (F-EMGs) in a tracking task conducted with 17 participants. We investigated the mechanism of drowsy expression through analysis of the F-EMGs. Contraction of the frontalis muscle and relaxation of the zygomaticus major muscle were found to be the two common features of muscular activities in drowsy expression. The former muscular activity appeared in a medium drowsy condition, but not in arousal or dozing states. This result indicates a driver's resistance against drowsiness before reaching a dozing state, in that contraction of the frontalis muscle causes the driver's eyebrows to rise. The latter muscular activity became more obvious with an increase in the driver's drowsiness. This result indicates a decline in the driver's physical activity, in that relaxation of the zygomaticus major muscle causes the driver's cheeks to slack. Detecting these two features is considered to be useful in estimating a driver's drowsiness level by means of facial image processing. The activities of the orbicularis oris muscle, mentalis muscle, and masseter muscle, which causes the driver's "lips part (slightly open mouth)", showed a variety of tendencies in respect of the drowsiness levels among different individuals. Consideration of the difference in muscular activities among individuals is necessary when the "lips part" feature is applied to estimation of a driver's drowsiness level through facial image processing.

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“…Furthermore, the face is unique to every individual, and is the important element to specify the individual. So, researches on face recognition and face identification aiming at the application to a criminal investigation, security, etc are popular recently [1] [2]. These researches perform similarity decisions by comparing composition elements, such as an eye and a nose, of a face with those of another face, and take up the face with the nearest distance as the candidate for recognition or identification.…”

mentioning

confidence: 99%

A Study on Face Similarity Using Knowledge Processing Methods

Takahasi

Ueda

Miyahara

et al. 2005

TENCON 2005 - 2005 IEEE Region 10 Conference

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The human face plays an important role in social interaction, such as communications, identifications, and security. Thus, facial information processing becomes an active subfield of the pattern recognition area. Facial recognition/identification algorithms usually calculate the similarity between a face and faces stored in databases and determine if they match or not. In those algorithms, the measurement of similarity is important. This paper presents knowledge about face similarity acquired by knowledge information processing methods and discusses the results. In the experiments, three kinds of questionnaires are used to obtain training examples, and these examples are processed by decision tree system C4.5 and inductive logic programming system Progol.. I.

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Processing of facial information by computer

Cited by 7 publications

References 20 publications

Extraction of Subject-Specific Facial Expression Categories and Generation of Facial Expression Feature Space using Self-Mapping

Extraction of Subject-Specific Facial Expression Categories and Generation of Facial Expression Feature Space using Self-Mapping

A Study of Facial Muscular Activities in Drowsy Expression

A Study on Face Similarity Using Knowledge Processing Methods

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