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Wavelet packet analysis for face recognition

delete2000-03-01
delete94
PRE
AI
C
Christophe García *
G
G. A. Zikos
G
Georgios Tziritas
DOI：10.1016/S0262-8856(99)00056-6delete
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Abstract

Abstract

En 中文
A novel method for recognition of frontal views of human faces under roughly constant illumination is presented. The proposed scheme is based on the analysis of a wavelet packet decomposition of the face images. Each face image is first located and then, described by a subset of band filtering images containing wavelet coefficients. From these wavelet coefficients, which characterize the face texture, we build compact and meaningful feature vectors, using simple statistical measures. Then, we show how an efficient and reliable probabilistic metric derived from the Bhattacharrya distance can be used in order to classify the face feature vectors into person classes. Experimental results are presented using images from the FERET and the FACES databases. The efficiency of the proposed approach is analyzed according to the FERET evaluation procedure and by comparing our results with those obtained using the well-known Eigenfaces method. (C) 2000 Elsevier Science B.V. All rights reserved.
Keywords:
face recognition
wavelet packet decomposition
facial features extraction
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Journal

Image and Vision Computing cover
Image and Vision Computing
IF:
4.2
Papers:
4.0K
Citations:
6.7K

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