Fuzzy Rule Based Quality Measures for Adaptive Multimodal Biometric Fusion at Operation Time

Abstract

Sample quality variation at operation time is one of the major concerns of real time biometric authentication and surveillance systems. Quality deviations of samples affect the performance of many benchmark biometric trait recognition systems. Moreover, large variation between enrolled and probe samples is very uncertain since it may arise at operation time for various reasons. In this paper, a novel adaptive multimodal biometric system is presented that can adapt the uncertainty of the quality degradation during operation. Fuzzy rule based method is applied for the first time to calculate the quality score of template-probe pairs dynamically. Feature extraction is accomplished using a novel shift invariant multi-resolution fusion approach. Finally, face and ear modalities are fused adaptively at rank level based on the quality scores. Proposed method relies more on good quality samples and disregards misclassification of poor quality samples. Experimental results demonstrate significant performance improvement of the proposed adaptive multimodal approach over baseline i.e. non-adaptive method.