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Horizontal and vertical 2DPCA based discriminant analysis for face verification using the FRGC version 2 database

NCJ Number
Advances in Biometrics Volume: 4642 Dated: 2007 Pages: 838-847
Jian Yang ; Chengjun Liu
Date Published
10 pages

This paper presents a horizontal and vertical 2D principal component analysis (2DPCA) based discriminant analysis (HVDA) method for face verification.


The HVDA method, which derives features by applying DPCA horizontally and vertically on the image matrices (2D arrays), achieves high computational efficiency compared with the traditional PCA and/or LDA based methods that operate on high dimensional image vectors (1D arrays). The HVDA method further performs discriminant analysis to enhance the discriminating power of the horizontal and vertical 2DPCA features. Finally, the HVDA method takes advantage of the color 305990 What kind of color spaces is suitable for color face recognition?