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Proceedings Paper

FRIT characterized hierarchical kernel memory arrangement for multiband palmprint recognition
Author(s): Dakshina Ranjan Kisku; Phalguni Gupta; Jamuna Kanta Sing
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Paper Abstract

In this paper, we present a hierarchical kernel associative memory (H-KAM) based computational model with Finite Ridgelet Transform (FRIT) representation for multispectral palmprint recognition. To characterize a multispectral palmprint image, the Finite Ridgelet Transform is used to achieve a very compact and distinctive representation of linear singularities while it also captures the singularities along lines and edges. The proposed system makes use of Finite Ridgelet Transform to represent multispectral palmprint image and it is then modeled by Kernel Associative Memories. Finally, the recognition scheme is thoroughly tested with a benchmarking multispectral palmprint database CASIA. For recognition purpose a Bayesian classifier is used. The experimental results exhibit robustness of the proposed system under different wavelengths of palm image.

Paper Details

Date Published: 21 October 2015
PDF: 12 pages
Proc. SPIE 9652, Optics and Photonics for Counterterrorism, Crime Fighting, and Defence XI; and Optical Materials and Biomaterials in Security and Defence Systems Technology XII, 96520P (21 October 2015); doi: 10.1117/12.2190204
Show Author Affiliations
Dakshina Ranjan Kisku, National Institute of Technology, Durgapur (India)
Phalguni Gupta, National Institute of Technical Teachers' Training and Research, Kolkata (India)
Jamuna Kanta Sing, Jadavpur Univ. (India)


Published in SPIE Proceedings Vol. 9652:
Optics and Photonics for Counterterrorism, Crime Fighting, and Defence XI; and Optical Materials and Biomaterials in Security and Defence Systems Technology XII
Roberto Zamboni; Douglas Burgess; Gari Owen; François Kajzar; Attila A. Szep; Harbinder Rana, Editor(s)

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