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This paper presents an algorithm to extract the region of interest (ROI) from the palm print image of the Hong Kong PolyU large-scale palm print database (version 2). Competitive coding method is used for feature extraction. Coding based methods are among the most promising palm print recognition methods because of their small feature size, fast matching speed, and high verification accuracy. Competitive Coding Scheme (CCS), first convolves the palm print image from real part of six Gabor filters with different orientations and then encodes the dominant orientation into its bit wise representation. Palm print image is decomposed into two levels using discrete wavelet transform. Approximation details give compress and denoised image of the original image. The competitive scheme is applied in the approximation details of decomposed image for feature extraction. KNN Classifier is used for palm print verification. In this experiment, Genuine Acceptance Rate is calculated for four different wavelet filters (Db1, Db4, Sym4, and Coif4) with different values of K (number of nearest neighbors).