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Proceedings Paper

Comparative study of palm print authentication system using geometric features
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Paper Abstract

Biometrics, particularly palm print authentication has been a stimulating research area due to its abundance of features. Stable features and effective matching are the most crucial steps for an authentication system. In conventional palm print authentication systems, matching is based on flexion creases, friction ridges, and minutiae points. Currently, contactless palm print imaging is an emerging technology. However, they tend to involve fluctuations in the image quality and texture loss due to factors such as varying illumination conditions, occlusions, noise, pose, and ghosting. These variations decrease the performance of the authentication systems. Furthermore, real-time palm print authentication in large databases continue to be a challenging task. In order to effectively solve these problems, features which are invariant to these anomalies are required. This paper proposes a robust palm print matching framework by making a comparative study of different local geometric features such as Difference-of-Gaussian, Hessian, Hessian-Laplace, Harris-Laplace, and Multiscale Harris for feature detection. These detectors are coupled with Scale Invariant Feature Transformation (SIFT) descriptor to describe the identified features. Additionally, a two-stage refinement process is carried out to obtain the best stable matches. Computer simulations demonstrate that the accuracy of the system has increased effectively with an EER of 0.86% when Harris-Laplace detector is used on IITD database.

Paper Details

Date Published: 10 May 2017
PDF: 9 pages
Proc. SPIE 10221, Mobile Multimedia/Image Processing, Security, and Applications 2017, 102210M (10 May 2017); doi: 10.1117/12.2262309
Show Author Affiliations
Shreyas Kamath K. M., Tufts Univ. (United States)
Srijith Rajeev, Tufts Univ. (United States)
Karen Panetta, Tufts Univ. (United States)
Sos S. Agaian, The Univ. of Texas at San Antonio (United States)
City Univ. of New York (United States)

Published in SPIE Proceedings Vol. 10221:
Mobile Multimedia/Image Processing, Security, and Applications 2017
Sos S. Agaian; Sabah A. Jassim, Editor(s)

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