Single Sample Face Recognition Using Multicross Pattern and Learning Discriminative Binary Features
Nemat Saeidi, Hossein Karshenas, Hossein Mahvash Mohammadi · Journal of Applied Security Research · 2019
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.