Two-Dimensional Linear Discriminant Analysis of Principle Component Vectors for Face Recognition


IEICE TRANSACTIONS on Information and Systems   Vol.E89-D   No.7   pp.2164-2170
Publication Date: 2006/07/01
Online ISSN: 1745-1361
DOI: 10.1093/ietisy/e89-d.7.2164
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Section on Machine Vision Applications)
Category: Face, Gesture, and Action Recognition
face recognition,  two-dimensional principle component analysis (2DPCA),  two-dimensional linear discriminant analysis (2DLDA),  2DPCA+2DLDA,  

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In this paper, we proposed a new Two-Dimensional Linear Discriminant Analysis (2DLDA) method, based on Two-Dimensional Principle Component Analysis (2DPCA) concept. In particular, 2D face image matrices do not need to be previously transformed into a vector. In this way, the spatial information can be preserved. Moreover, the 2DLDA also allows avoiding the Small Sample Size (SSS) problem, thus overcoming the traditional LDA. We combine 2DPCA and our proposed 2DLDA on the Two-Dimensional Linear Discriminant Analysis of principle component vectors framework. Our framework consists of two steps: first we project an input face image into the family of projected vectors via 2DPCA-based technique, second we project from these space into the classification space via 2DLDA-based technique. This does not only allows further reducing of the dimension of feature matrix but also improving the classification accuracy. Experimental results on ORL and Yale face database showed an improvement of 2DPCA-based technique over the conventional PCA technique.