Wavelet Packet based feature representation and extraction for face recognition

How to find a powerful method of feature representation and extraction is constantly a key issue in 1-D or 2-D signal recognition, such as face recognition. Wavelet Packet(WP) is a potential technique in this regard. However, we face the problems on how to find the optimal WP decomposition and extract the discriminant features. In this paper, we propose a fuzzy c-means shaped membership function in the evaluation of the classification abilities of WP sub-spaces or WP coefficients for seeking the optimal WP decomposition and extracting discriminant features. The classification is performed by Enhanced Fisher Linear Discriminant Model (EFM) and a conventional linear classifier. Experiments on typical Yaleface database are carried out. Compared with the well-known Principal Component Analysis(PCA), the face recognition rate of WP based method is higher than that obtained by PCA.