Facial expression classification using Kernel based PCA with fused DCT and GWT features

Although many approaches for facial expression recognition have been proposed in the past, most of them yielding poor recognition performance with single feature extraction method. The objective of this paper is to propose an innovative method based on fusion of local and global features for better classification rate. Gabor wavelets(GWT) are used to extract Local features and Discrete Cosine Transform (DCT) is used to extract global features from facial expression images. To reduce dimensionality of extracted features and better classification performance Kernel Principal Components Analysis (KPCA) is applied. Wavelet fusion method is used to fuse the features extracted from GWT and DCT. Finally the images are classified into 6 different basic emotions like surprise, fear, sad, joy, anger and disgust using Radial Basis Function(RBF) Neural Network classifier. The performance of the proposed method is evaluated on Cohn-Kanade database. The results of proposed algorithm exhibit high performance rate of about 99% in person dependent facial expression recognition.