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.
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