Robust Facial Expression Recognition using Gabor Feature and Bayesian Discriminating Classifier

被引：0
|
作者：
Piparsaniyan, Yamini [1 ]
Sharma, Vijay K. [1 ]
Mahapatra, K. K. [1 ]
机构：
[1] Natl Inst Technol, Dept ECE, Rourkela, Odisha, India
来源：
2014 INTERNATIONAL CONFERENCE ON COMMUNICATIONS AND SIGNAL PROCESSING (ICCSP) | 2014年
关键词：
Bayesian classification; Computer vision; Emotion recognition; Gabor features; PCA; FACE;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Automatic facial expression recognition is important for effective Human computer interaction (HCI) as well as autistic children for communication. In this paper, we propose emotion recognition using Gabor feature and simple Bayesian discriminating classifier based on principal component analysis (PCA) for emotion recognition. The multi class classification strategic has been applied based on highest value of log likelihood after training different emotions class. Facial expression images from JAFFE database have been used for training as well as testing. Very high accuracy (96.73 %) of emotion recognition has been obtained with proposed method.
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收藏
页数：4
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