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Wavelet Energy Feature Extraction and Matching for Palmprint Recognition

Xiang-Qian Wu1, Kuan-Quan Wang1, and David Zhang2   

  1. 1School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, P.R. China
    2Biometrics Research Centre, Department of Computing, Hong Kong Polytechnic University, Hong Kong Special Administrative Region, P.R. China
  • Received:2003-12-10 Revised:2004-08-10 Online:2005-05-10 Published:2005-05-10

According to the fact that the basic features of apalmprint, including principal lines, wrinkles and ridges, havedifferent resolutions, in this paper we analyze palmprints using amulti-resolution method and define a novel palmprint feature, whichcalled 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 paperalso analyses the discriminabilities of each level WEF and, according to these discriminabilities, chooses a suitable weight for each levelto compute the weighted city block distance for recognition. Theexperimental results show that the order of the discriminabilities ofeach level WEF, from strong to weak, is the 4th, 3rd, 5th, 2nd and 1stlevel. It also shows that WEF is robust to some extent in rotation andtranslation of the images. Accuracies of 99.24% and 99.45% have beenobtained in palmprint verification and palmprint identification,respectively. These results demonstrate the power of the proposedapproach.

Key words: Formal semantics; π-calculus; logic programming; distributed AI;



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