Component-Based Face Detection

Abstract

We present a component-based, trainable system for de-tecting frontal and near-frontal views of faces in still gray images. The system consists of a two-level hierarchy of Sup-port Vector Machine (SVM) classifers. On the first level, component classifers independently detect components of a face. On the second level, a single classifier checks i f the geometrical configuration of the detected components in the image matches a geometrical model of a face. We propose a method for automatically learning components by using 3- 0 head models. This approach has the advantage that no manual interaction is required f o r choosing and extract-ing components. Experiments show that the component-based system is signij5cantly more robust against rotations in depth than a comparable system trained on whole face patterns. 1.