Feature level fusion of face and palmprint biometrics

Authors:
Dakshina Ranjan Kisku;Phalguni Gupta;Jamuna Kanta Sing
Affiliations:
Department of Computer Science and Engineering, Durgapur, India;Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, Kanpur, India;Department of Computer Science and Engineering, Jadavpur University, Kolkata, India
Venue:
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Year:
2010

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Abstract

This paper presents a feature level fusion of face and palmprint biometrics. It uses the improved K-medoids clustering algorithm and isomorphic graph. The performance of the system has been verified by two distance metrics namely, K-NN and normalized correlation metrics. It uses two multibiometrics databases of face and palmprint images for testing. The experimental results reveal that the feature level fusion with the improved K-medoids partitioning algorithm exhibits robust performance and increases its performance with utmost level of accuracy.