Multimodal Belief Fusion for Face and Ear Biometrics

Dakshina Ranjan Kisku

doi:10.4236/iim.2009.13024

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 two multimodal databases, namely, IIT Kanpur database and virtual database. Former contains face and ear images of 400 individuals while later consist of both images of 17 subjects taken from BANCA face database and TUM ear database. It is found that use of Gabor wavelet filters along with GMM and DS theory can provide robust and efficient multimodal fusion strategy.

Highlights

Recent advancements of biometrics security artifacts for identity verification and access control have increased the possibility of using identification system based on multiple biometrics identifiers [1,2,3]
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
The proposed multimodal biometrics system is tested on the two multimodal databases, namely, IIT Kanpur multimodal database of face and ear images and virtual multimodal database consisting of face images taken from BANCA face database and ear images taken from TUM ear database

Summary

Introduction

Recent advancements of biometrics security artifacts for identity verification and access control have increased the possibility of using identification system based on multiple biometrics identifiers [1,2,3]. A multimodal biometric system [1,3] integrates multiple source of information obtained from different biometric cues. It takes advantage of the positive constraints and capabilities from individual biometric matchers by validating its pros and cons independently. There exist multimodal biometrics system with various levels of fusion, namely, sensor level, feature level, matching score level, decision level and rank level. A fusion approach of face [4] and ear [5] biometrics using Dempster-Shafer decision theory [7] is proposed. Fusion of face and ear biometrics has not been studied in details except the work presented in [6]. The proposed technique uses Gabor wavelet filters [8]

Methods

Results

Conclusion