Multiscale Edge Based Approach for face Identification

A novel and uniform edge based framework for both face identification and verification is presented in this paper. This paper proposes the feature extraction algorithm based on different directional information and multiscale information of face. Proposed feature extraction algorithm extract discrete wavelet transform edge features to determine facial features. DWT acts as a main contributor in efficient feature extraction which results in the high recognition rates. After generating feature vectors, distance classifier is used for feature classification step. We have evaluated performance of proposed multiscale edge based method on standard databases with variation in pose, expression, orientation, lightning, facial information. ORL, JAFFE and IIT face databases were used to generate the results. Experiments show that edge wavelet features can significantly improve system performance. DWT and edge detection based approach also causes reduction in feature vector size. Thus, the proposed method has proven to be a promising technique under arbitrary variations in illumination, poses and backgrounds with slight occlusions too.