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Face Detection in Still Gray Images

Author(s)
Heisele, Bernd; Poggio, Tomaso; Pontil, Massimiliano
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Abstract
We present a trainable system for detecting frontal and near-frontal views of faces in still gray images using Support Vector Machines (SVMs). We first consider the problem of detecting the whole face pattern by a single SVM classifer. In this context we compare different types of image features, present and evaluate a new method for reducing the number of 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 classifers. On the first level, component classifers independently detect components of a face, such as the eyes, the nose, and the mouth. On the second level, a single classifer checks if the geometrical configuration of the detected components in the image matches a geometrical model of a face.
Date issued
2000-05-01
URI
http://hdl.handle.net/1721.1/7229
Other identifiers
AIM-1687
CBCL-187
Series/Report no.
AIM-1687CBCL-187

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  • AI Memos (1959 - 2004)
  • CBCL Memos (1993 - 2004)

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