On the Improvements of Uni-modal and Bi-modal Fusions of Speaker and Face Recognition for Mobile Biometrics

The MOBIO database provides a challenging test-bed for speaker and face recognition systems because it includes voice and face samples as they would appear in forensic scenarios. In this paper, we investigate uni-modal and bi-modal multi-algorithm fusion using logistic regression. The source speaker and face recognition systems were taken from the 2013 speaker and face recognition evaluations that were held in the context of the last International Conference on Biometrics (ICB-2013). Using the unbiased MOBIO protocols, the employed evaluation measures are the equal error rate (EER), the half-total error rate (HTER) and the detection error trade-off (DET). The results show that by uni-modal algorithm fusion, the HTER's of the speaker recognition system are reduced by around 35%, and of the face recognition system by between 15% and 20%. Bi-modal fusion drastically boosts recognition by a relative gain of 65% - 70% of performance compared to the best uni-modal system.