Facial Expression Recognition Based on SSVM Algorithm and Multi-source Texture Feature Fusion Using KECA

摘要

Automatic expression recognition of human faces has been an active research area for decades. In this work, to improve the facial expression recognition effect, a new method based on SSVM algorithm and multi-source texture feature fusion using KECA is proposed. Multi-source texture features are introduced to describe the facial expression, containing GMCL, Gabor feature, and HOG feature. The results indicate that multi-source texture features are conducive to improve the recognition effect and make up for the deficiency of single texture feature in facial expression description. In addition, KECA and SSVM algorithms show better performance than traditional methods in feature extraction and classification. To further verify the effectiveness of the proposed method, three sets of comparative experiments are carried out: PCA+SVM (based on Gabor feature), PCA+SVM (based on GMCL+Gabor+HOG feature), KECA+SVM (based on GMCL+Gabor +HOG feature). The results of JAFFE database indicate that the accuracy of proposed method, equal to 93.04%, is at least 2.61% higher than conventional method. The results of JAFFE database demonstrate the validity of proposed method.