Orthogonal Complete Discriminant Locality Preserving Projections for Face Recognition

Authors:
Gui-Fu Lu;Zhong Lin;Zhong Jin
Affiliations:
School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China 210094 and School of Computer Science and Information, Anhui Polytechnic University, Wuhu, C ...;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China 210094;School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China 210094
Venue:
Neural Processing Letters
Year:
2011

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Abstract

In this paper, we propose a novel orthogonal complete discriminant locality preserving projections for facial feature extraction and recognition (OCDLPP). All training samples are projected into the range of a so-called locality preserving total scatterto reduce dimensionality without loss of discriminative information. The transformation matrix of OCDLPP is orthogonal and is found simultaneously using QR decomposition technique. Moreover, a feasible and effective procedure is proposed to alleviate the computational burden of high dimensional matrix for typical face image data. Experiments results on the ORL, Yale, FERET and PIE face databases show the effectiveness of the proposed OCDLPP.