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Palmprint identification based on non-separable wavelet filter banks

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4 Author(s)
Jie Wu ; Fac. of Math. & Comput. Sci., Hubei Univ., Wuhan ; Xinge You ; Yuan Yan Tang ; Yiu-ming Cheung

Creases, as a special salient feature of palmprint, are large in number and distributed at all directions. It changes slowly in a personpsilas whole life, which qualifies themselves as features in palmprint identification. In this paper, we devised a new algorithm of crease extraction by using non-separable bivariate wavelet filter banks with linear phase. Compared with the traditional wavelet, our research demonstrates that the three high frequency sub-images generated by Non-separable Discrete Wavelet Transform (NDWT) can extract more creases and no longer extensively focus on the three special directions. As a consequence, we proposed a new method by combining NDWT and Support Vector Machines (SVM) for palmprint identification. Tested by our experiment, this method achieves a satisfied identification result and computational efficiency as well.

Published in:

Pattern Recognition, 2008. ICPR 2008. 19th International Conference on

Date of Conference:

8-11 Dec. 2008