DocumentCode :
2438576
Title :
Real-time facial expression recognition based on features´ positions and dimensions
Author :
Sako, Hiroshi ; Smith, Anthony V W
Author_Institution :
Hitachi Central Res. Lab., Tokyo, Japan
Volume :
3
fYear :
1996
fDate :
25-29 Aug 1996
Firstpage :
643
Abstract :
This paper describes a method of real-time facial expression recognition which is based on automatic measurement of the facial features´ dimension and the positional relationship between them. The method is composed of two parts, the facial feature extraction using matching techniques and the facial expression recognition using statistics of position and dimension of the features. The method is implemented in an experimental hardware system and the performance is evaluated. The extraction rates of the facial-area, the mouth and the eyes are about 100%, 96% and 90%, respectively, and the recognition rates of facial expression such as normal, angry, surprise, smile and sad expression are 54%, 89%, 86%, 53% and 71%, respectively, for a specific person. The whole processing speed is about 15 frames/second. Finally, we touch on some applications such as man-machine interface, automatic generation of facial graphic animation and sign language translation using facial expression recognition techniques
Keywords :
computer vision; face recognition; feature extraction; image colour analysis; image matching; real-time systems; statistical analysis; colour matching; computer vision; facial expression recognition; facial feature extraction; feature dimensions; feature positions; image matching; real-time systems; statistical analysis; Eyes; Face recognition; Facial animation; Facial features; Graphics; Hardware; Mouth; Position measurement; Statistics; User interfaces;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 1996., Proceedings of the 13th International Conference on
Conference_Location :
Vienna
ISSN :
1051-4651
Print_ISBN :
0-8186-7282-X
Type :
conf
DOI :
10.1109/ICPR.1996.547025
Filename :
547025
Link To Document :
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