Feature Level Fusion of Face and Palmprint Biometrics by Isomorphic Graph-based Improved K-Medoids Partitioning

TL;DR

This paper introduces a novel feature level fusion method for face and palmprint biometrics using an improved K-medoids clustering and isomorphic graph matching, enhancing biometric recognition accuracy.

Contribution

The paper proposes a new fusion approach combining improved K-medoids clustering with isomorphic graph matching for face and palmprint biometrics.

Findings

Increased biometric recognition accuracy.

Effective fusion of face and palmprint features.

Robustness demonstrated through extensive evaluation.

Abstract

This paper presents a feature level fusion approach which uses the improved K-medoids clustering algorithm and isomorphic graph for face and palmprint biometrics. Partitioning around medoids (PAM) algorithm is used to partition the set of n invariant feature points of the face and palmprint images into k clusters. By partitioning the face and palmprint images with scale invariant features SIFT points, a number of clusters is formed on both the images. Then on each cluster, an isomorphic graph is drawn. In the next step, the most probable pair of graphs is searched using iterative relaxation algorithm from all possible isomorphic graphs for a pair of corresponding face and palmprint images. Finally, graphs are fused by pairing the isomorphic graphs into augmented groups in terms of addition of invariant SIFT points and in terms of combining pair of keypoint descriptors by concatenation rule. Experimental results obtained from the extensive evaluation show that the proposed feature level fusion with the improved K-medoids partitioning algorithm increases the performance of the system with utmost level of accuracy.