Single Sample Face Recognition Using Multicross Pattern and Learning Discriminative Binary Features

Abstract

Binary feature descriptors, require considerable amount of information to be applicable in wide appearance variations, which contradicts the single sample per person (SSPP) problem. To address this challenge, a novel binary feature learning method called discriminative binary feature mapping is presented. Then, based on a number of precisely selected objectives, a feature mapping is learned by projecting all of the extracted vectors to a lower-dimensional feature space. The resulting feature vectors are then used to obtain a holistic face representation based on dictionary learning. Extensive experimental results show that the proposed method is able to obtain superior performance.