Robust Algorithms for a Multimodal Biometric System Using Palmprint and Speech

Abstract

In this paper, we propose a person verification scheme using a novel combination of palmprint and speech. The crucial aspect of biometric based verification lies in its use of features in verification. Thus, in this paper, we propose two novel feature extraction methods for palmprint verification. The proposed methods are based on the Gaussian mixture model followed by subspace based approaches such as ICA I and ICA II, called independent component analysis I mixture model (ICA I MM) and independent component analysis II mixture model (ICA II MM) for palmprint verification. The speech verification system uses sphericity based measurement for person verification. The proposed multimodal biometric system uses a fairly large database of 150 users. The robustness of the proposed methods is validated by introducing six different types of noises on palmprint images and Gaussian white noise with two different signal-to-noise ratios on speech samples. Then, fusion of palmprint and speech are carried out at match score level using the weighted SUM rule for both clean and noisy conditions. Finally, we compare the results of combined biometric with the results of individual biometric and also the results of the proposed methods against conventional (without mixture model) subspace methods.