- A Wavelet-based Framework for Face...

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Unformatted text preview: A Wavelet-based Framework for Face Recognition Christophe Garcia, Giorgos Zikos, Giorgos Tziritas ICS – Foundation for Research and Technology-Hellas – FORTH P.O.Box 1385, GR 711 10 Heraklion, Crete, Greece Tel.: +30 (81) 39 17 01, Fax: +30 (81) 39 16 01 E-mail: cgarcia,gzikos,tziritas @csi.forth.gr Abstract 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 de- velopping. Detecting and recognizing human faces auto- matically 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 Bhattachar- rya distance is used in order to classify the face feature vectors into person classes. 1 Introduction Face recognition is becoming a very promising tool for automatic multimedia content analysis and for a content- based indexing video retrievial system. Such a system is currently developped within the Esprit project DiVAN ([5]) which aims at building and evaluating a distributed audio-visual archives network providing a community of users with facilities to store video raw material, and access it in a coherent way, on top of high-speed wide area communication networks. The video raw data is first automatically segmented into shots and from the content-related image segments, salient features such as region shape, intensity, color, texture and motion descrip- tors are extracted and used for indexing and retrieving information. In order to allow queries at a higher semantic level, some particular pictorial objects have to be detected and exploited for indexing. We focus on human faces de- tection and recognition, given that such data are of great interest for users queries. In recent years, considerable progress has been made on the problem of face detection and face recognition, especially under stable conditions such as small varia- tions in lighting, facial expression and pose. A good survey may be found in [16]. These methods can be roughly divided into two different groups: geometrical features matching and template matching. In the first case, some geometrical measures about distinctive fa- cial features such as eyes, mouth, nose and chin are extracted ([2]). In the second case, the face image, rep- resented as a two-dimensional array of intensity values, is compared to a single or several templates representing a whole face. The earliest methods for template match- ing are correlation-based, thus computationally very ex- pensive and require great amount of storage and since a few years, the Principal Components Analysis (PCA)...
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