Abstract

We present a component-based, trainable system for detecting

frontal and near-frontal views of faces in still gray

images. The system consists of a two-level hierarchy of Support

Vector Machine (SVM) classifiers. On the first level,

component classifiers independently detect components of

a face. 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. We propose

a method for automatically learning components by using

3-D head models. This approach has the advantage that

no manual interaction is required for choosing and extracting

components. Experiments show that the componentbased

system is significantly more robust against rotations

in depth than a comparable system trained on whole face

patterns.