Feature Selection for Face Detection


Autoria(s): Serre, Thomas; Heisele, Bernd; Mukherjee, Sayan; Poggio, Tomaso
Data(s)

20/10/2004

20/10/2004

01/09/2000

Resumo

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.

Formato

7211022 bytes

1034240 bytes

application/postscript

application/pdf

Identificador

AIM-1697

CBCL-192

http://hdl.handle.net/1721.1/7232

Idioma(s)

en_US

Relação

AIM-1697

CBCL-192