Environment-adaptive multi-channel biometrics

This paper looks into multi-channel/multimodal biometric systems that are adaptive to environmental variations. In this work, we introduce a general formulation that addresses the environmental robustness of multi-channel fusion in biometric systems. Based on the formulation, two audio-visual biometric systems are developed. The first relies on confidence measures derived from the environmental conditions to dynamically weight the contributions of the biometric channels; whereas the second considers the multiple channels jointly to optimally adjust the fusion parameters according to the current environmental conditions. Experimental evaluations with varying testing conditions show that both systems achieve lower recognition error rate comparing with a baseline non-environment-adaptive audio-visual system. It is further shown that incorporating joint-optimization of multi-channel fusion parameters to cater to environmental changes as in the second system consistently leads to improved recognition accuracy over other systems, and at the same time guarantees to perform no worse than any of the individual biometric channels under all environmental conditions.