Efficient face detection by a cascaded support–vector machine expansion

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.