Facial expression recognition based on fusion feature of PCA and LBP with SVM

Abstract

Facial expressions recognition is an important part of the study in man-machine interface. Principal component analysis (PCA) is an extraction method based on statistical features which were extracted the global grayscale features of the whole image. But the grayscale global features are environmentally sensitive. So a hybrid method of principal component analysis and local binary pattern (LBP) is introduced in this article. LBP extracts the local grayscale features of the mouth region, which contribute most to facial expression recognition, to assist the global grayscale features of facial expression recognition. The support vector machine (SVM) is used for facial expression recognition. And experiment results show that, this method can classify different expressions more effectively and can get higher recognition rate than the traditional recognition methods.