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Proceedings Paper

Combining 1D and 2D linear discriminant analysis for palmprint recognition
Author(s): Jian Zhang; Hongbing Ji; Lei Wang; Lin Lin
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Paper Abstract

In this paper, a novel feature extraction method for palmprint recognition termed as Two-dimensional Combined Discriminant Analysis (2DCDA) is proposed. By connecting the adjacent rows of a image sequentially, the obtained new covariance matrices contain the useful information among local geometry structures in the image, which is eliminated by 2DLDA. In this way, 2DCDA combines LDA and 2DLDA for a promising recognition accuracy, but the number of coefficients of its projection matrix is lower than that of other two-dimensional methods. Experimental results on the CASIA palmprint database demonstrate the effectiveness of the proposed method.

Paper Details

Date Published: 2 December 2011
PDF: 6 pages
Proc. SPIE 8004, MIPPR 2011: Pattern Recognition and Computer Vision, 80041G (2 December 2011); doi: 10.1117/12.903025
Show Author Affiliations
Jian Zhang, Xidian Univ. (China)
Hongbing Ji, Xidian Univ. (China)
Lei Wang, Xidian Univ. (China)
Lin Lin, Xidian Univ. (China)


Published in SPIE Proceedings Vol. 8004:
MIPPR 2011: Pattern Recognition and Computer Vision
Jonathan Roberts; Jie Ma, Editor(s)

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