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Pattern Recognition 40 (2007) 15201532 www.elsevier.com/locate/pr
 

Summary: Pattern Recognition 40 (2007) 1520­1532
www.elsevier.com/locate/pr
Learning the best subset of local features for face recognition
Berk Gökberk
, M. Okan Irfanoglu, Lale Akarun, Ethem Alpaydin
Department of Computer Engineering, Bogaziçi University, TR-34342, Bebek, Istanbul, Turkey
Received 28 October 2005; received in revised form 14 June 2006; accepted 6 September 2006
Abstract
We propose a novel, local feature-based face representation method based on two-stage subset selection where the first stage finds the
informative regions and the second stage finds the discriminative features in those locations. The key motivation is to learn the most
discriminative regions of a human face and the features in there for person identification, instead of assuming a priori any regions of
saliency. We use the subset selection-based formulation and compare three variants of feature selection and genetic algorithms for this
purpose. Experiments on frontal face images taken from the FERET dataset confirm the advantage of the proposed approach in terms of
high accuracy and significantly reduced dimensionality.
2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Keywords: Face recognition; Face representation; Gabor wavelets; Feature subset selection; Genetic algorithms
1. Introduction
Face recognition has proved to be a difficult problem
in computer vision. The main reason for this is that intra-
personal variations caused by facial expressions, view point

  

Source: Alpaydın, Ethem - Department of Computer Engineering, Bogaziçi University

 

Collections: Computer Technologies and Information Sciences