Person authentication using face, teeth and voice modalities for mobile device security

In this paper, we propose an enhanced multimodal personal authentication system for mobile device security. The proposed approach fuses information obtained from face, teeth and voice modalities to improve performance. To integrate three modalities, we employ various fusion techniques such as the weighted-summation rule, K-NN, Fisher and Gaussian classifiers, and we then evaluate the authentication performance of the proposed system. The performance is evaluated on a database consisting of 1000 biometric traits that correspond to the face, teeth and voice modalities of 50 persons, i.e., 20 biometric traits per individual, in which these biometric traits are simultaneously collected by a smart-phone device. The experiment results integrating the three modalities showed the error rates of 1.64%, 4.70%, 3.06% and 1.98% for the weighted-summation rule, K-NN, Fisher and Gaussian classifier, respectively, and that the weight-summation rule outperformed the other classification approaches. In contrast, the error rates regarding a single modality were 5.09%, 7.75% and 8.98% for face, teeth, and voice modalities, respectively. From these results, we confirmed that the proposed method achieved a significant performance improvement over the methods using a single modality, and the results showed that the proposed method was very effective through various fusion experiments.