Multibiometrics Belief Fusion

TL;DR

This paper introduces a multimodal biometric system combining face and ear recognition using Gabor features, GMM for feature modeling, and Dempster-Shafer theory for belief fusion, demonstrating robustness on a large database.

Contribution

It presents a novel fusion approach integrating Gabor features, GMM, and Dempster-Shafer theory for multimodal biometrics, enhancing robustness and efficiency.

Findings

Effective fusion of face and ear biometrics using Gabor features.

Improved recognition accuracy with GMM and Dempster-Shafer fusion.

Robust performance demonstrated on a 400-individual database.

Abstract

This paper proposes a multimodal biometric system through Gaussian Mixture Model (GMM) for face and ear biometrics with belief fusion of the estimated scores characterized by Gabor responses and the proposed fusion is accomplished by Dempster-Shafer (DS) decision theory. Face and ear images are convolved with Gabor wavelet filters to extracts spatially enhanced Gabor facial features and Gabor ear features. Further, GMM is applied to the high-dimensional Gabor face and Gabor ear responses separately for quantitive measurements. Expectation Maximization (EM) algorithm is used to estimate density parameters in GMM. This produces two sets of feature vectors which are then fused using Dempster-Shafer theory. Experiments are conducted on multimodal database containing face and ear images of 400 individuals. It is found that use of Gabor wavelet filters along with GMM and DS theory can provide robust and efficient multimodal fusion strategy.