Computationally efficient face detection

Abstract

This paper describes an algorithm for finding faces within an image. The basis of the algorithm is to run an observa- tion window at all possible positions, scales and orientation within the image. A non-linear support vector machine is used to determine whether or not a face is contained within the observation window. The non-linear support vector ma- chine operates by comparing the input patch to a set of sup- port vectors (which can be thought of as face and anti-face templates). Each support vector is scored by some non- linear function against the observation window and if the resulting sum is over some threshold a face is indicated. Be- cause of the huge search space that is considered, it is im- perative to investigate ways to speed up the support vector machine. Within this paper we suggest a method of speeding up the non-linear support vector machine. A set of reduced set vectors (RV's) are calculated from the support vectors. By considering the RV's sequentially, and if at any point a face is deemed too unlikely to cease the sequential evalua- tion, obviating the need to evaluate the remaining RV's. The idea being that we only need to apply a subset of the RV's to eliminate things that are obviously not a face (thus reducing the computation). The key then is to explore the RV's in the right order and a method for this is proposed.