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. © 2010 Springer-Verlag Berlin Heidelberg.
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CITATION STYLE
Kisku, D. R., Gupta, P., & Sing, J. K. (2010). Feature level fusion of face and palmprint biometrics. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6218 LNCS, pp. 512–521). https://doi.org/10.1007/978-3-642-14980-1_50
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