Title :
Nuclear Norm Based 2DPCA
Author :
Fanlong Zhang ; Jianjun Qian ; Jian Yang
Author_Institution :
Sch. of Comput. Sci. & Technol., Nanjing Univ. of Sci. & Technol., Nanjing, China
Abstract :
This paper presents a novel method, namely nuclear norm based 2DPCA (N-2DPCA), for image feature extraction. Unlike the conventional 2DPCA, N-2DPCA uses a nuclear norm based reconstruction error criterion. The criterion is minimized by converting the nuclear norm based optimization problem into a series of F-norm based optimization problems. N-2DPCA is applied to face recognition and is evaluated using the Extended Yale B and CMU PIE databases. Experimental results demonstrate that our method is more effective and robust than PCA, 2DPCA and L1-Norm based 2DPCA.
Keywords :
face recognition; matrix algebra; principal component analysis; CMU PIE database; Extended Yale B database; F-norm based optimization problems; L1-norm based 2DPCA; PCA; face recognition; nuclear norm based 2DPCA; nuclear norm based reconstruction error criterion; two-dimensional principal component analysis; Databases; Lighting; Linear programming; Optimization; Principal component analysis; Testing; Training; feature extraction; nuclear norm; principal component analysis; subspace analysis;
Conference_Titel :
Pattern Recognition (ACPR), 2013 2nd IAPR Asian Conference on
Conference_Location :
Naha
DOI :
10.1109/ACPR.2013.10
