Title :
Large margin null space discriminant analysis with applications to face recognition
Author :
Xiaobo Chen ; Jian Yang ; Wankou Yang
Author_Institution :
Sch. of Comput. Sci. & Technol., Nanjing Univ. of Sci. & Technol., Nanjing, China
Abstract :
In this paper, we proposed a novel method for feature extraction, namely, large margin null space discriminant analysis (LMNDA). Instead of maximizing the average between-class distances as in original NDA, LMNDA directly maximizes the minimum distance between each class center and the total class center after feature extraction which leads to a nonconvex quadratic programming problem. An efficient iteration algorithm based on constrained concave-convex procedure (CCCP) is developed to solve the resulting model. Experiments on the Yale and ORL face databases show that LMNDA works well and leads to good recognition performance.
Keywords :
concave programming; face recognition; feature extraction; iterative methods; quadratic programming; statistical analysis; visual databases; CCCP; LMNDA; ORL face database; Yale database; constrained concave-convex procedure; feature extraction; iteration algorithm; large margin null space discriminant analysis; nonconvex quadratic programming problem; Databases; Face; Face recognition; Feature extraction; Null space; Principal component analysis;
Conference_Titel :
Pattern Recognition (ICPR), 2012 21st International Conference on
Conference_Location :
Tsukuba
Print_ISBN :
978-1-4673-2216-4