GEFE

Abstract

Personal identification using an individual's periocular skin texture (e.g. the texture of the skin around the eye) is a promising and exciting new biometric modality [11]. For the application presented in this paper, local binary patterns (LBPs) are used to extract 1416 features from the periocular regions of images within the Face Recognition Grand Challenge (FRGC) dataset. GEFE (Genetic & Evolutionary Feature Extraction) is then used to evolve optimized subsets of the original feature set. Our results show that not only do the evolved subsets consist of approximately 50% fewer features but they also have higher recognition rates.