Multi-faceted and Multi-algorithmic Framework (MFMA) for Finger Knuckle Biometrics

K. Usha (), T. Thenmozhi () and M. Ezhilalarasan ()
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K. Usha: Vellore Institute of Technology, School of Computer Science and Engineering
T. Thenmozhi: Vellore Institute of Technology, School of Computer Science and Engineering
M. Ezhilalarasan: Pondicherry Engineering College, Department of Information Technology

A chapter in New Trends in Computational Vision and Bio-inspired Computing, 2020, pp 1681-1699 from Springer

Abstract: Abstract Reliable personal authentication system is essential for social, financial and political structures of today’s human life style. The advent of biometric technology has revolutionized personal authentication system to meet the current requirements through biometric modalities in a reliable, accurate, rapid and user-friendly way. However, there exist a number of unresolved issues for the biometric systems related to data, system design and algorithms. This work focuses on exploring features from dorsal side of the hand region known as finger knuckle surface for reliable personal authentication. This paper illustrates design and development of an integrated finger knuckle biometric framework using multiple units of finger knuckle surface and multi-algorithmic parameters for robust and accurate personal identification. This novel integrated approach known as Multi-Faceted and Multi-Algorithmic Framework (MFMA) for authentication using finger knuckle surface. This MFMA framework simultaneously acquires multiple instances of finger back knuckle surface, extracts multiple features using three different categories of algorithms, viz., angular geometric analysis, transform based texture analysis, statistical analysis and integrates the information derived from multiple algorithms using decision level fusion implemented based on Bayesian approach.

Keywords: Finger knuckle surface; Angular geometric analysis; Transform based texture analysis; Statistical analysis; Bayesian approach (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-41862-5_172

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DOI: 10.1007/978-3-030-41862-5_172

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