Efficient face detection by a cascaded support-vector machine expansion

Romdhani, S; Torr, P; Schölkopf, B; Blake, A

doi:10.1098/rspa.2004.1333

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Efficient face detection by a cascaded support-vector machine expansion

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Schölkopf, B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://royalsocietypublishing.org/doi/pdf/10.1098/rspa.2004.1333
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Citation

Romdhani, S., Torr, P., Schölkopf, B., & Blake, A. (2004). Efficient face detection by a cascaded support-vector machine expansion. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 460(2501), 3283-3297. doi:10.1098/rspa.2004.1333.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-D75D-D

Abstract

We describe a fast system for the detection and localization of human faces in images using a nonlinear ‘support-vector machine‘. We approximate the decision surface in terms of a reduced set of expansion vectors and propose a cascaded evaluation which has the property that the full support-vector expansion is only evaluated on the face-like parts of the image, while the largest part of typical images is classified using a single expansion vector (a simpler and more efficient classifier). As a result, only three reduced-set vectors are used, on average, to classify an image patch. Hence, the cascaded evaluation, presented in this paper, offers a thirtyfold speed-up over an evaluation using the full set of reduced-set vectors, which is itself already thirty times faster than classification using all the support vectors.