Feature Level Fusion of Biometrics Cues: Human Identification with Doddingtons Caricature

TL;DR

This paper introduces a multimodal biometric system combining fingerprint and ear features using SIFT descriptors and K-medoids clustering, enhanced by Doddington's user-dependent weighting, demonstrating robust identification performance.

Contribution

It proposes a novel feature fusion approach at the feature level for multimodal biometrics, integrating clustering and user-dependent weighting for improved accuracy.

Findings

Robust performance demonstrated on biometric datasets

Effective feature reduction via K-medoids clustering

Enhanced matching accuracy with Doddington weighting

Abstract

This paper presents a multimodal biometric system of fingerprint and ear biometrics. Scale Invariant Feature Transform (SIFT) descriptor based feature sets extracted from fingerprint and ear are fused. The fused set is encoded by K-medoids partitioning approach with less number of feature points in the set. K-medoids partition the whole dataset into clusters to minimize the error between data points belonging to the clusters and its center. Reduced feature set is used to match between two biometric sets. Matching scores are generated using wolf-lamb user-dependent feature weighting scheme introduced by Doddington. The technique is tested to exhibit its robust performance.