DocumentCode :
1848678
Title :
Palmprint Recognition Using Kernel Spectral Regression Discriminant Analysis and HOG Representation
Author :
Jia, Wei ; Gui, Jie ; Hu, Rong-Xiang ; Lei, Ying-Ke
Author_Institution :
Hefei Inst. of Intell. Machines, Chinese Acad. of Sci., Hefei, China
fYear :
2010
fDate :
22-22 Aug. 2010
Firstpage :
1
Lastpage :
6
Abstract :
In this paper, we propose a new appearance based approach for palmprint recognition, which combines Kernel Spectral Regression Discriminant Analysis (KSRDA) method and HOG representation. KSRDA is the kernel version of SRDA which has lower computation complexity than Linear Discriminant Analysis (LDA). Meanwhile, HOG representation isn´t sensitive to changes of illumination, and has the robustness against deformations because slight translations and rotations make small histogram value changes. As a result, the proposed approach can achieve promising recognition rate. The results of experiments conducted on Hong Kong Polytechnic University Palmprint Database II and the blue band of Hong Kong Polytechnic University Multispectral Palmprint Database demonstrate the effectiveness of proposed approach.
Keywords :
biometrics (access control); image recognition; regression analysis; visual databases; HOG representation; KSRDA; LDA; Palmprint recognition; kernel spectral regression discriminant analysis; linear discriminant analysis; multispectral palmprint database; Databases; Histograms; Kernel; Lighting; Pixel; Principal component analysis; Robustness;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Emerging Techniques and Challenges for Hand-Based Biometrics (ETCHB), 2010 International Workshop on
Conference_Location :
Istanbul
Print_ISBN :
978-1-4244-7063-1
Type :
conf
DOI :
10.1109/ETCHB.2010.5559288
Filename :
5559288
Link To Document :
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