Incremental Linear Discriminant Analysis for Face Recognition

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Incremental Linear Discriminant Analysis for Face Recognition
Abstract--Dimensionality reduction methods have been successfully employed for face recognition. Among the various dimensionality reduction algorithms, linear (Fisher) discriminant analysis (LDA) is one of the popular supervised dimensionality reduction methods, and many LDA-based face recognition algorithms/systems have been reported in the last decade. However, the LDA-based face recognition systems suffer from the scalability problem. To overcome this limitation, an incremental approach is a natural solution. The main difficulty in developing the incremental LDA (ILDA) is to handle the inverse of the within-class scatter matrix. In this paper, based on the generalized singular value decomposition LDA (LDA/GSVD), we develop a new ILDA algorithm called GSVD-ILDA. Different from the existing techniques in which the new projection matrix is found in a restricted subspace, the proposed GSVD-ILDA determines the projection matrix in full space. Extensive experiments are performed to compar...
Haitao Zhao, Pong Chi Yuen
Added 29 Dec 2010
Updated 29 Dec 2010
Type Journal
Year 2008
Where TSMC
Authors Haitao Zhao, Pong Chi Yuen
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