Feature Selection for Face Detection

dc.creator	Serre, Thomas
dc.creator	Heisele, Bernd
dc.creator	Mukherjee, Sayan
dc.creator	Poggio, Tomaso
dc.date	2004-10-20T21:03:34Z
dc.date	2004-10-20T21:03:34Z
dc.date	2000-09-01
dc.date.accessioned	2013-10-09T02:48:37Z
dc.date.available	2013-10-09T02:48:37Z
dc.date.issued	2013-10-09
dc.identifier	AIM-1697
dc.identifier	CBCL-192
dc.identifier	http://hdl.handle.net/1721.1/7232
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721
dc.description	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.
dc.format	7211022 bytes
dc.format	1034240 bytes
dc.format	application/postscript
dc.format	application/pdf
dc.language	en_US
dc.relation	AIM-1697
dc.relation	CBCL-192
dc.title	Feature Selection for Face Detection

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