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Personal authentication by palmprint using contourlet transform and k-nearest neighbour classifier
by Hedieh Sajedi; Bashir Ghasemi Moghaddam
International Journal of Machine Intelligence and Sensory Signal Processing (IJMISSP), Vol. 1, No. 4, 2016

 

Abstract: Biometric-based personal verification is a powerful security feature. Biometric systems are used the physiological and/or behavioural characteristics in each individual for verification. Palmprint is a reliable biometric that can be used for identity verification because it is stable and unique for every individual. In this paper, a new approach for personal authentication by palmprint using contourlet transform is presented. Contourlet transform is a multiscale and directional transform that captures image curvatures and smoothness with multidirectional decomposition capability, finely. Our proposed method includes three steps, preprocessing, feature extraction, and classification. In preprocessing stage, the central part of each palmprint is extracted. In feature extraction step, at first, contourlet transform is applied to the central part of palmprint and then features are extracted from created subbands. In this method for each image, 384 features are obtained. Finally, in classification step, naïve Bayes, support vector machine (SVM) and k-nearest neighbour (k-NN) classifiers are employed. Experiments are performed on three databases and recognition accuracies of 99.41%, 92.38%, and 85.34% are obtained on PolyU, COEP and IITD databases using k-NN algorithm, respectively. Experimental results illustrate that our proposed method could be used effectively in personal authentication by palmprint images.

Online publication date: Thu, 06-Jul-2017

 

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