Detecting facial expressions from face images using a genetic algorithm

Jun Ohya; Fumio Kishino

doi:10.1109/ICPR.1996.547026

Detecting facial expressions from face images using a genetic algorithm

Jun Ohya, Fumio Kishino

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

A new method to detect deformations of facial parts from a face image regardless of changes in the position and orientation of a face using the genetic algorithm is proposed. Facial expression parameters that are used to deform and position a 3D face model are assigned to the genes of an individual in a population. The face model is deformed and positioned according to the gene values of each individual and is observed by a virtual camera, and a face image is synthesized. The fitness which evaluates to what extent the real and synthesized face images are similar to each other is calculated. After this process is repeated for sufficient generations, the parameter estimation is obtained from the genes of the individual with the best fitness. Experimental results demonstrate the effectiveness of the method.

Original language	English
Title of host publication	Track C
Subtitle of host publication	Applications and Robotic Systems
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	649-653
Number of pages	5
ISBN (Print)	081867282X, 9780818672828
DOIs	https://doi.org/10.1109/ICPR.1996.547026
Publication status	Published - 1996 Jan 1
Externally published	Yes
Event	13th International Conference on Pattern Recognition, ICPR 1996 - Vienna, Austria Duration: 1996 Aug 25 → 1996 Aug 29

Publication series

Name	Proceedings - International Conference on Pattern Recognition
Volume	3
ISSN (Print)	1051-4651

Conference

Conference	13th International Conference on Pattern Recognition, ICPR 1996
Country/Territory	Austria
City	Vienna
Period	96/8/25 → 96/8/29

ASJC Scopus subject areas

Computer Vision and Pattern Recognition

Access to Document

10.1109/ICPR.1996.547026

Cite this

Detecting facial expressions from face images using a genetic algorithm. / Ohya, Jun; Kishino, Fumio.
Track C: Applications and Robotic Systems. Institute of Electrical and Electronics Engineers Inc., 1996. p. 649-653 547026 (Proceedings - International Conference on Pattern Recognition; Vol. 3).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ohya, J & Kishino, F 1996, Detecting facial expressions from face images using a genetic algorithm. in Track C: Applications and Robotic Systems., 547026, Proceedings - International Conference on Pattern Recognition, vol. 3, Institute of Electrical and Electronics Engineers Inc., pp. 649-653, 13th International Conference on Pattern Recognition, ICPR 1996, Vienna, Austria, 96/8/25. https://doi.org/10.1109/ICPR.1996.547026

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abstract = "A new method to detect deformations of facial parts from a face image regardless of changes in the position and orientation of a face using the genetic algorithm is proposed. Facial expression parameters that are used to deform and position a 3D face model are assigned to the genes of an individual in a population. The face model is deformed and positioned according to the gene values of each individual and is observed by a virtual camera, and a face image is synthesized. The fitness which evaluates to what extent the real and synthesized face images are similar to each other is calculated. After this process is repeated for sufficient generations, the parameter estimation is obtained from the genes of the individual with the best fitness. Experimental results demonstrate the effectiveness of the method.",

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AB - A new method to detect deformations of facial parts from a face image regardless of changes in the position and orientation of a face using the genetic algorithm is proposed. Facial expression parameters that are used to deform and position a 3D face model are assigned to the genes of an individual in a population. The face model is deformed and positioned according to the gene values of each individual and is observed by a virtual camera, and a face image is synthesized. The fitness which evaluates to what extent the real and synthesized face images are similar to each other is calculated. After this process is repeated for sufficient generations, the parameter estimation is obtained from the genes of the individual with the best fitness. Experimental results demonstrate the effectiveness of the method.

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Detecting facial expressions from face images using a genetic algorithm

Abstract

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Conference

ASJC Scopus subject areas

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