Wavelet-based personal identification

This work presents a personal identification system based on the learning of the lifting dyadic wavelet filters. Our system consists of face learning, detection, and identification processes. In the learning process, free parameters in the lifting filters are determined so as to capture a facial part. Our face detection method is performed by applying the learned filters to each of the video frames. A person whose face is detected in a maximum number of frames is identified as a target person. In simulation, it is shown that our personal identification algorithm is fast and accurate.