Face Detection - Efficient and Rank Deficient

This paper proposes a method for computing fast approximations to support vector decision functions in the field of object detection. In the present approach we are building on an existing algorithm where the set of support vectors is replaced by a smaller, so-called reduced set of synthesized input space points. In contrast to the existing method that finds the reduced set via unconstrained optimization, we impose a structural constraint on the synthetic points such that the resulting approximations can be evaluated via separable filters. For applications that require scanning large images, this decreases the computational complexity by a significant amount. Experimental results show that in face detection, rank deficient approximations are 4 to 6 times faster than unconstrained reduced set systems.

• Type: Conference
• Year: 2004
• Where: NIPS
• Authors: Wolf Kienzle, Gökhan H. Bakir, Matthias O. Franz, Bernhard Schölkopf
• Keywords: Approximations, NIPS 2004, NIPS 2007, Reduced Set, Support Vectors