Learning the best subset of local features for face recognition

We propose a novel, local feature-based face representation method based on twostage 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. Key words: Face recognition, face representation, Gabor wavelets, feature subset selection, genetic algorithms

• Type: Journal
• Year: 2007
• Where: PR
• Authors: Berk Gökberk, M. Okan Irfanoglu, Lale Akarun, Ethem Alpaydin
• Keywords: Genetic Algorithms, Local Feature-based Face, PR 2007, Subset Selection-based Formulation