Partially-occluded face recognition using weighted module linear regression classification

Accuracy and speed of face recognition frameworks are two foremost concerns for practical applications in recent researches. Linear regression classification (LRC) is a very famous and powerful approach for face recognition; however, it cannot perform very well under occlusion situations. In this paper, the regression parameters of the module-LRC are analyzed when a query facial image is partially occluded. For removing contaminated modules, the weighted module linear regression classification (WMLRC) is proposed. In order to evaluate the effectiveness, AR face database is used to validate the proposed WMLRC as well as the well-known face recognition methods. Simulation results show that the proposed WMLRC method achieves the best performance for partially-occluded faces while keeping the advantage in speed over the SRC-based approaches.