Title :
Palmprint classification using contourlets
Author :
Chen, G.Y. ; Kégl, B.
Author_Institution :
Canadian Space Agency, St-Hubert
Abstract :
In this paper, we propose a new palmprint classification method by using the contourlet features. The contourlet transform is a new two dimensional extension of the wavelet transform using multiscale and directional filter banks. It can effectively capture smooth contours that are the dominant features in palmprint images. AdaBoost is used as a classifier in the experiments. Experimental results show that the contourlet features are very stable features for invariant palmprint classification, and better classification rates are reported when compared with other existing classification methods.
Keywords :
feature extraction; filtering theory; image classification; wavelet transforms; AdaBoost; contourlet features; contourlet transform; directional filter banks; multiscale filter banks; palmprint classification method; palmprint images; smooth contours; wavelet transform; Authentication; Biometrics; Feature extraction; Filter bank; Fourier transforms; Handwriting recognition; Neural networks; Pattern recognition; Polarization; Wavelet transforms; AdaBoost; Palmprint classification; contourlets; feature extraction; wavelets;
Conference_Titel :
Systems, Man and Cybernetics, 2007. ISIC. IEEE International Conference on
Conference_Location :
Montreal, Que.
Print_ISBN :
978-1-4244-0990-7
Electronic_ISBN :
978-1-4244-0991-4
DOI :
10.1109/ICSMC.2007.4413648