Learning the best subset of local features for face recognition
Pattern Recognition, cilt.40, sa.5, ss.1520-1532, 2007 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 40 Sayı: 5
- Basım Tarihi: 2007
- Doi Numarası: 10.1016/j.patcog.2006.09.009
- Dergi Adı: Pattern Recognition
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Sayfa Sayıları: ss.1520-1532
- Anahtar Kelimeler: Face recognition, Face representation, Feature subset selection, Gabor wavelets, Genetic algorithms
- Boğaziçi Üniversitesi Adresli: Evet
Özet
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.