An MPCA/LDA Based Dimensionality Reduction Algorithm for Face Recognition. (31st August 2014)

• Record Type: Journal Article
• Authors: Huang, Jun; Su, Kehua; El-Den, Jamal; Hu, Tao; Li, Junlong
• Other Names: Chen Yi Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2014(2014)
• Publication Date: 2014-08-31
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2014/393265
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : We proposed a face recognition algorithm based on both the multilinear principal component analysis (MPCA) and linear discriminant analysis (LDA). Compared with current traditional existing face recognition methods, our approach treats face images as multidimensional tensor in order to find the optimal tensor subspace for accomplishing dimension reduction. The LDA is used to project samples to a new discriminant feature space, while the K nearest neighbor (KNN) is adopted for sample set classification. The results of our study and the developed algorithm are validated with face databases ORL, FERET, and YALE and compared with PCA, MPCA, and PCA + LDA methods, which demonstrates an improvement in face recognition accuracy.