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Palmprint authentication using symbolic aggregate approximation features with improved enhancement techniques

Author(s)
S. Palanikumar Department of Computer Science and Business System, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College,Avadi, Chennai, Tamil Nadu,India
M. P. Flower Queen Department of Electrical and Electronics Engineering, SRM TRP Engineering College,Trichy, Tamil Nadu,India
S. Mathupriya Department of Computer Science and Engineering, Sri Sairam Institute of Technology,Chennai, Tamil Nadu,India
K. Tharageswari Department of Computer Science and Engineering, Faculty of Engineering, Karpagam Academy of Higher Education,Coimbatore, Tamil Nadu,India
Abstract
The palmprint enhancement is a pre-processing stage of palmprint authentication. The accuracy of the recognition rate can be improved by incorporating effective palmprint enhancement methods. So far, only a little work has been done in palmprint enhancement. Little attention is given to incorporate the enhancement techniques in palmprint authentication system to achieve performance improvement. This work presents robust enhancement methods which provide better performance and accuracy. Palmprint enhancement using curvelet and Recursive Histogram Equalization (RHE) overcomes the drawbacks of the existing systems and makes palmprint recognition simpler and more accurate with enhanced palm image as input. Palmprint recognition system uses the Symbolic Aggregate Approximation (SAX) features from the enhanced palmprint image. The recognition rate is optimum when both curvelet and RHE methods are used for enhancement of palmprint. Here we compared different types of enhancement methods and we have obtained maximum recognition rate with a combination of recursive histogram equalization and curvelet transform. The performance of the palmprint authentication is measured by False Acceptance Rate (FAR), False Rejection Rate (FRR) and Total Success Rate (TSR). The comparison of recognition rate with enhancement is done using minimum distance classifier, Support Vector Machine, Random Forest and Bayesian. Random Forest classifier provides better results.
Publication Details
Page(s) 401-411
DOI DOI not available
Published Journal: ARPN Journal of Engineering and Applied Sciences, Volume: 18, Issue: 4, Year: 2023

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Keywords
improved enhancement techniques, palmprint authentication, symbolic aggregate approximation features
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