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Abstract : |
This publication can be retrieved by anonymous ftp to publications.ai.mit.edu. The pathname for this publication is: ai-publications/1500-1999/AIM-1687.ps.Z We present a trainable system for detecting frontal and near-frontal views of faces in still gray images using Support Vector Machines (SVMs). We rst consider the problem of detecting the whole face pattern by a single SVM classier. In this context we compare dierent types of image features, present and evaluate a new method for reducing the number features and discuss practical issues concerning the parameterization of SVMs and the selection of training data. The second part of the paper describes a component-based method for face detection consisting of a two-level hierarchy of SVM classiers. On the rst level, component classi ers independently detect components of a face, such as the eyes, the nose, and the mouth. On the second level, a single classier checks if the geometrical conguration of the detected components in the image matches a geometrical model of a face., |
