Robust Biometric Score Fusion by Naive Likelihood Ratio via Receiver Operating Characteristics

This paper presents a novel method of fusing multiple biometrics on the matching score level. We estimate the likelihood ratios of the fused biometric scores, via individual receiver operating characteristics (ROC) which construct the Naive Bayes classifier. Using a limited number of operation points on the ROC, we are able to realize reliable and robust estimation of the Naive Bayes probability without explicit estimation of the genuine and impostor score distributions. Different from previous work, the method takes into consideration a particular characteristic of the matching score: its quantitative value is already an indication of the sample's likelihood of being genuine. This characteristic is integrated into the proposed method to improve the fusion performance while reducing the inherent algorithmic complexity. We demonstrate by experiments that the proposed method is reliable and robust, suitable for a wide range of matching score distributions in realistic data and public databases.