Component-based Face Detection

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Component-based Face Detection
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.
Bernd Heisele, Thomas Serre, Massimiliano Pontil,
Added 12 Oct 2009
Updated 08 Jul 2010
Type Conference
Year 2001
Where CVPR
Authors Bernd Heisele, Thomas Serre, Massimiliano Pontil, Tomaso Poggio
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