Pre-detection and dual-dictionary sparse representation based face recognition algorithm in non-sufficient training samples

摘要

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 sam-ples 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 pro-posed 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 dic-tionary 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 inter-ference.After FPPR,the sample is processed by the DD-SRC to find the most similar one in training samples.The experimen-tal 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).