Robust face detection using discriminating feature analysis and bayes classifier

Document Type

Conference Proceeding

Publication Date

10-18-2005

Abstract

This paper presents a novel face detection method, which integrates the discriminating feature analysis of the input image, the statistical modeling of face and nonface classes, and the Bayes classifier for multiple frontal face detection. First, feature analysis derives a discriminating feature vector by combining the input image, its 1-D Haar wavelet representation, and its amplitude projections. Second, statistical modeling estimates the conditional probability density functions, or PDFs, of the face and nonface classes, respectively. Finally, the Bayes classifier applies the estimated conditional PDFs to detect multiple frontal faces in an image. Experimental results using 853 images (containing a total of 970 faces) from diverse image sources show the feasibility of the proposed face detection method.

Identifier

26444487654 (Scopus)

Publication Title

Proceedings of SPIE the International Society for Optical Engineering

External Full Text Location

https://doi.org/10.1117/12.601933

ISSN

0277786X

First Page

164

Last Page

172

Volume

5779

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