Learning the best subset of local features for face recognition

PATTERN RECOGNITION

卷:40

Article

Gokberk, Berk ; Irfanoglu, M. Okan ; Akarun, Lale ; Alpaydin, Ethem

关键词: face recognition;    face representation;    Gabor wavelets;    feature subset selection;    genetic algorithms;   

DOI  :  10.1016/j.patcog.2006.09.009

来源: Elsevier

【 摘 要 】

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. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

【 授权许可】

Free