Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
531760 | Pattern Recognition | 2007 | 13 Pages |
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.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Berk Gökberk, M. Okan İrfanoğlu, Lale Akarun, Ethem Alpaydın,