Face Detection in Still Gray Images

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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 classifier. In this context we compare different 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 classifiers. On the first level, component classifiers independently detect components of a face, such as the eyes, the nose, and the mouth. On the second level, a single classifier checks if the geometrical configuration of the detected components in the image matches a geometrical model of a face.

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