Combining 1D and 2D linear discriminant analysis for palmprint recognition

Jian Zhang; Hongbing Ji; Lei Wang; Lin Lin

doi:10.1117/12.903025

2 December 2011 Combining 1D and 2D linear discriminant analysis for palmprint recognition

Jian Zhang, Hongbing Ji, Lei Wang, Lin Lin

Proceedings Volume 8004, MIPPR 2011: Pattern Recognition and Computer Vision; 80041G (2011) https://doi.org/10.1117/12.903025
Event: Seventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2011), 2011, Guilin, China

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.

Citation Download Citation

Jian Zhang, Hongbing Ji, Lei Wang, and Lin Lin "Combining 1D and 2D linear discriminant analysis for palmprint recognition", Proc. SPIE 8004, MIPPR 2011: Pattern Recognition and Computer Vision, 80041G (2 December 2011); https://doi.org/10.1117/12.903025

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KEYWORDS

Databases

Matrices

Feature extraction

Principal component analysis

Algorithm development

Biometrics

Electronics engineering

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