Title :
Component-based face detection
Author :
Heisele, Bernd ; Serre, Thomas ; Pontil, Massimiliano ; Poggio, Tomaso
Author_Institution :
tCenter for Biol. & Computational Learning, MIT, Cambridge, MA, USA
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 component-based system is significantly more robust against rotations in depth than a comparable system trained on whole face patterns.
Keywords :
computer vision; face recognition; learning automata; 3-D head models; component-based face detection; geometrical configuration; still gray images; support vector machine classifiers; trainable system; two-level hierarchy; Eyes; Face detection; Face recognition; Feature extraction; Neural networks; Object detection; Robustness; Solid modeling; Support vector machine classification; Support vector machines;
Conference_Titel :
Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on
Print_ISBN :
0-7695-1272-0
DOI :
10.1109/CVPR.2001.990537