Wavelet Energy Feature Extraction and Matching for Palmprint Recognition |
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Authors: | Email author" target="_blank">Xiang-Qian?WuEmail author Kuan-Quan?Wang David?Zhang |
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Affiliation: | (1) School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, P.R. China;(2) Biometrics Research Centre, Department of Computing, Hong Kong Polytechnic University, Hong Kong Special Administrative Region, P.R., China |
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Abstract: | According to the fact that the basic features of a palmprint, including principal lines, wrinkles and ridges, have different resolutions, in this paper we analyze palmprints using a multi-resolution method and define a novel palmprint feature, which called wavelet energy feature (WEF), based on the wavelet transform. WEF can reflect the wavelet energy distribution of the principal lines, wrinkles and ridges in different directions at different resolutions (scales), thus it can efficiently characterize palmprints. This paper also analyses the discriminabilities of each level WEF and, according to these discriminabilities, chooses a suitable weight for each level to compute the weighted city block distance for recognition. The experimental results show that the order of the discriminabilities of each level WEF, from strong to weak, is the 4th, 3rd, 5th, 2nd and 1st level. It also shows that WEF is robust to some extent in rotation and translation of the images. Accuracies of 99.24% and 99.45% have been obtained in palmprint verification and palmprint identification, respectively. These results demonstrate the power of the proposed approach. |
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Keywords: | biometrics palmprint recognition wavelet energy feature weighted city block distance |
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