Face Detection using SVM Trained in Eigenfaces Space

The central problem in the case of face detectors is to build a face class model. We present a method for face class modeling in the eigenfaces space using a large-margin classifier like SVM. Two main issues are addressed: what is the required number of eigenfaces to achieve a good classification rate and how to train the SVM for a good generalization. As the experimental evidence show, generally one needs less eigenfaces than usually considered. We will present different strategies for choosing the dimensionality of the PCA space and discuss their effectiveness in the case of face-class modeling.


Published in:
4th International Conference on Audio- and Video-Based Biometric Person Authentication, Guildford, UK, 2688, 190-198
Year:
2003
Publisher:
Berlin, SPIE
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 Record created 2006-06-14, last modified 2018-03-17

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