A Combined KPCA and SVM Method for Basic Emotional Expressions Recognition

Abstract

Automatic analysis of facial expression has become a popular research area because of it’s many applications in the field of computer vision. This paper presents a hybrid method based on Gabor filter, Kernel Principle Component Analysis (KPCA) and Support Vector Machine (SVM) for classification of facial expressions into six basic emotions. At first, Gabor filter bank is applied on input images. Then, the feature reduction technique of KPCA is performed on the outputs of the filter. Finally, SVM is used for classification. The proposed method is tested on the Cohen-Kanade’s facial expression images dataset. The results of the proposed method are compared to the ones of the combined Principle Component Analysis (PCA) and SVM classifier. Experimental results show the effectiveness of the proposed method. The average recognition rate of 89.9% is achieved in this work which is higher than 87.3% resulted from a common combined PCA and SVM method.