Local Discriminant Wavelet Packet Coordinates for Face Recognition

Face recognition is a challenging problem due to variations in pose, illumination, and expression. Techniques that can provide effective feature representation with enhanced discriminability are crucial. Wavelets have played an important role in image processing for its ability to capture localized spatial-frequency information of images. In this paper, we propose a novel local discriminant coordinates method based on wavelet packet for face recognition to compensate for these variations. Traditional wavelet-based methods for face recognition select or operate on the most discriminant subband, and neglect the scattered characteristic of discriminant features. The proposed method selects the most discriminant coordinates uniformly from all spatial frequency subbands to overcome the deficiency of traditional wavelet-based methods. To measure the discriminability of coordinates, a new dilation invariant entropy and a maximum a posterior logistic model are put forward. Moreover, a new triangle square ratio criterion is used to improve classification using the Euclidean distance and the cosine criterion. Experimental results show that the proposed method is robust for face recognition under variations in illumination, pose and expression.