Feature Selection for Face Detection
Author(s)
Serre, Thomas; Heisele, Bernd; Mukherjee, Sayan; Poggio, Tomaso
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We present a new method to select features for a face detection system using Support Vector Machines (SVMs). In the first 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 classification 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 function of the SVM.
Date issued
2000-09-01Other identifiers
AIM-1697
CBCL-192
Series/Report no.
AIM-1697CBCL-192