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Proceedings Paper

A method of recognition based on the feature layer fusion of palmprint and hand vein
Author(s): Hua Ma; Xiaoping Yang; Guangyuan Shi
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Paper Abstract

In this paper, a method of recognition of multi-modal biometrics for palmprint and hand vein based on the feature layer fusion is proposed, combined with the characteristics of an improved canonical correlation analysis (CCA) and two dimensional principal component analysis (2DPCA). After pretreatment respectively, feature vectors of palmprint and hand vein images are extracted using two dimensional principal component analysis (2DPCA),then fused in the feature level using the improved canonical correlation analysis(CCA), so identification can be done by a adjacent classifier finally. Using this method, two biometric information can be fused and the redundancy of information between features can effectively eliminated, the problem of the high-dimensional and small sample size can be overcome too. Simulation experimental results show that the proposed method in this paper can effectively improve the recognition rate of identification.

Paper Details

Date Published: 19 December 2013
PDF: 10 pages
Proc. SPIE 9045, 2013 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology, 904514 (19 December 2013); doi: 10.1117/12.2035110
Show Author Affiliations
Hua Ma, Tianjin Univ. of Technology (China)
Xiaoping Yang, Tianjin Univ. of Technology (China)
Guangyuan Shi, Tianjin Univ. of Technology (China)


Published in SPIE Proceedings Vol. 9045:
2013 International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Processing Technology
Xinggang Lin; Jesse Zheng, Editor(s)

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