A Wavelet-based Framework for Face Recognition
Christophe García, G. A. Zikos, Giorgos Tziritas · 1998
Content-based indexing methods are of great interest for image and video retrievial in audio-visual archives, such as in the DiVAN project that we are currently developping. Detecting and recognizing human faces automatically in video data provide users with powerful tools for performing queries. In this article, a new scheme for face recognition using a wavelet packet decomposition is presented. Each face is described by a subset of band filtered images containing wavelet coefficients. These coefficients characterize the face texture and a set of simple statistical measures allows us to form compact and meaningful feature vectors. Then, an efficient and reliable probalistic metric derived from the Bhattacharrya distance is used in order to classify the face feature vectors into person classes.