Analysis of facial expressions using a three‐dimensional facial model

Choi, Changkyu; Takebe, Tsuyoshi; Harashima, Hiroshi

doi:10.1002/scj.4690231202

Cited by 7 publications

(5 citation statements)

References 4 publications

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“…In type (1) the estimation methods for expressive muscle motion typically follow methods based on detecting motion of expression muscles with optical flows [22,23] and methods that estimate AU motions of FACS to recognize expressions [24,25]. At present for type (2), most work is being carried out on the assumption that the facial characteristic points are properly extracted.…”

Section: Classification Of Expression Recognition Algorithmmentioning

confidence: 99%

Processing of facial information by computer

Hasegawa¹,

Morishima

Kaneko³

1998

Electron. Comm. Jpn. Pt. III

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Section: Classification Of Expression Recognition Algorithmmentioning

confidence: 99%

Processing of facial information by computer

Hasegawa¹,

Morishima

Kaneko³

1998

Electron. Comm. Jpn. Pt. III

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“…(1) Extraction of a facial structure (2) Detection of a facial change (3) Description of the facial change by reference to certain elements (4) Recognition of basic expression (5) Recognition of complex emotion or intention In these processes, FACS (Facial Action Coding System), as proposed by Ekman & Friesen [18] is generally used as steps (3) and (4) [19][20][21]. In FACS, the facial changes caused by emotions are divided into several basic units called AUs (Action Units) which correspond to specific facial muscular activities (cf.…”

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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“…When using geometric parameters [6,7], facial expressions are generated by using the vertex displacements of the facial model. When using physical parameters [8][9][10][11][12], facial expressions are generated by the principles of mechanics and the material properties of muscle.…”

Section: Introductionmentioning

confidence: 99%

Generating facial expressions of another person based on estimation of muscular contraction parameters from face image

Ahn¹,

Ozawa²

2007

Systems & Computers in Japan

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SUMMARYWe aim to generate facial expressions of various persons more naturally and without any markers and feature lines. We propose a new approach to estimating muscular contraction parameters for the purpose of generating facial expressions. The muscles of the face are commonly known as the muscles of facial expression. The facial expressions of a human are generated by muscle movements inside the face. If the muscle-based facial model that Waters proposed is used, facial expressions can be generated by muscular contraction parameters. In this paper, first, the facial surface feature points of the face images are detected by imageprocessing methods. Next, the muscular contraction parameters are estimated by the neutral expression and arbitrary expression displacement of the facial model wireframe fitting, based on detected facial surface feature points. Finally, the facial expression is generated by the vertex displacements of an individual facial model based on estimated muscular contraction parameters. Experimental results reveal that our approach can generate various facial expressions of the individual facial model, which corresponds to the facial expression of the actual face image. Additionally, we can generate the facial expressions of another person by using the muscular contraction parameters of that person in an individual facial model.

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Analysis of facial expressions using a three‐dimensional facial model

Cited by 7 publications

References 4 publications

Processing of facial information by computer

Processing of facial information by computer

A Study of Facial Muscular Activities in Drowsy Expression

Generating facial expressions of another person based on estimation of muscular contraction parameters from face image

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