Robust authentication using Likelihood Ratio and GMM for the fusion of voice and face

Abstract

With the increased use of biometrics for identity verification, there have been a similar increase in the use of multimodal fusion to overcome the limitations of unimodal biometric systems. While there are several types of fusion (e.g. decision level, score level, feature level, sensor level), research has shown that score level fusion is the most effective in delivering increased accuracy. Recently a promising framework for optimal combination of match scores based on the Likelihood Ratio-LR-test is proposed where the distributions of genuine and impostor match scores are modelled as finite Gaussian mixture model. In this paper, we examine the performance of combining face and voice biometrics at the score level using the LR classifier. Our experiments on the publicly available scores of the XM2VTS Benchmark database show a consistent improvement in performance compared to the famous efficient sum rule preceded by Min-Max, z-score and tanh score normalization techniques.