Invariant pattern recognition using contourlets and AdaBoost

Authors:
G. Y. Chen;B. Kégl
Affiliations:
Department of Mathematics and Statistics, Concordia University, Montreal, Quebec, Canada H3G 1M8;Linear Accelerator Laboratory, University of Paris Sud, Batiment 200, 91898 Orsay, France
Venue:
Pattern Recognition
Year:
2010

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Abstract

In this paper, we propose new methods for palmprint classification and handwritten numeral recognition 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 and handwritten numeral 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 handwritten numeral recognition, and better classification rates are reported when compared with other existing classification methods.