Chen, G. Y.; Kégl, B. Invariant pattern recognition using contourlets and adaboost. (English) Zbl 1187.68435 Pattern Recognition 43, No. 3, 579-583 (2010). Summary: 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. MSC: 68T10 Pattern recognition, speech recognition Keywords:palmprint classification; wavelets; contourlets; feature extraction; adaboost Software:AdaBoost.MH; PolyU palmprint; NSCT toolbox PDFBibTeX XMLCite \textit{G. Y. Chen} and \textit{B. Kégl}, Pattern Recognition 43, No. 3, 579--583 (2010; Zbl 1187.68435) Full Text: DOI References: [1] Bui, T. D.; Chen, G. Y.; Feng, L., An orthonormal-shell-Fourier descriptor for rapid matching of patterns in image database, International Journal of Pattern Recognition and Artificial Intelligence, 15, 8, 1213-1229 (2001) [2] Chen, G. Y.; Bui, T. D., Invariant Fourier-wavelet descriptor for pattern recognition, Pattern Recognition, 32, 7, 1083-1088 (1999) [3] Wunsch, P.; Laine, A. F., Wavelet descriptors for multiresolution recognition of handprinted characters, Pattern Recognition, 28, 8, 1237-1249 (1995) [4] Chen, G. Y.; Bui, T. 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