Pre-detection and dual-dictionary sparse representation based face recognition algorithm in non-sufficient training samples
School of Information Science and Technology, Northwest University, Xi’an 710069, China, Jian Guang Zhao, Chao Zhang, School of Information Science and Technology, Northwest University, Xi’an 710069, China, Shunli Zhang, School of Information Science and Technology, Northwest University, Xi’an 710069, China, Tingting Lu, School of Information Science and Technology, Northwest University, Xi’an 710069, China, Weiwen Su, School of Information Science and Technology, Northwest University, Xi’an 710069, China, Jian Jia, School of Information Science and Technology, Northwest University, Xi’an 710069, China · Journal of Systems Engineering and Electronics · 2018
Face recognition based on few training samples is a challenging task. In daily applications, sufficient training samples may not be obtained and most of the gained training samples are in various illuminations and poses. Non-sufficient training samples could not effectively express various facial conditions, so the improvement of the face recognition rate under the non-sufficient training samples condition becomes a laborious mission. In our work, the facial pose pre-recognition (FPPR) model and the dual-dictionary sparse representation classification (DD-SRC) are proposed for face recognition. The FPPR model is based on the facial geometric characteristic and machine learning, dividing a testing sample into full-face and profile. Different poses in a single dictionary are influenced by each other, which leads to a low face recognition rate. The DD-SRC contains two dictionaries, full-face dictionary and profile dictionary, and is able to reduce the interference. After FPPR, the sample is processed by the DD-SRC to find the most similar one in training samples. The experimental results show the performance of the proposed algorithm on olivetti research laboratory (ORL) and face recognition technology (FERET) databases, and also reflect comparisons with SRC, linear regression classification (LRC), and two-phase test sample sparse representation (TPTSSR).