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Invariant pattern recognition using contourlets and AdaBoost

G.Y. Chen 1 Balázs Kégl 2, 3, 4
3 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
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.
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Submitted on : Friday, October 2, 2009 - 4:32:13 PM
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G.Y. Chen, Balázs Kégl. Invariant pattern recognition using contourlets and AdaBoost. Pattern Recognition, Elsevier, 2010, 43, pp.579-583. ⟨10.1016/j.patcog.2009.08.020⟩. ⟨in2p3-00421717⟩



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