Feature Selection for Face Detection
T. Serre, Bernd Heisele, Sayan Mukherjee, Tomaso Poggio · 2000
This publication can be retrieved by anonymous ftp to publications.ai.mit.edu. The pathname for this publication is: ai-publications/1500-1999/1697 We present a new method to select features for a face detection system using Support Vector Machines (SVMs). In the rst step we reduce the dimensionality of the input space by projecting the data into a subset of eigenvectors. The dimension of the subset is determined by a classication criterion based on minimizing a bound on the expected error probability of an SVM. In the second step we select features from the SVM feature space by removing those that have low contributions to the decision func-tion of the SVM. Copyright c