Novel mixture model–based approaches for person verification using multimodal biometrics

Abstract

With growing concerns about security, the world over, biometric-based person verification is gaining more and more attention. Recently, multimodal biometric has attracted increasing focus among researchers as this overwhelms many limitations of unimodal biometric systems and hence more reliable. In this paper, we propose four different feature extraction techniques namely Principle Component Analysis Mixture Model (PCA MM), Singular Value Decomposition Mixture Model (SVD MM), Independent Component Analysis I Mixture Model (ICA I MM), and Independent Component Analysis II Mixture Model (ICA II MM) to design a multimodal biometric system at feature level. The proposed methods begin with modeling the multimodal biometrics data using Gaussian Mixture Model followed by a subspace methods like PCA, SVD, ICA I, and ICA II. Extensive experiments are carried out to observe the verification performance of the proposed methods at feature and match score level on large dataset of 150 users. We compare the results of the combined biometric with the results of individual biometric and also results of the proposed schemes against conventional (without mixture model) subspace approaches. The experimental results demonstrate the effectiveness of the proposed methods in designing a robust multimodal biometric system for accurate person verification.