Quasi-convex Optimization of Metrics in Biometric Score Fusion

In this paper, we address the problem of score fusion in biometric authentication. Single valued metrics related to the receiver operating characteristics (ROC) curve, such as Equal Error Rate (EER) and False Rejection Rate (FRR) when False Acceptance Rate equals zero, are extensively used for evaluating biometric authentication performances. Various requirements and preferences, for example, lower EER, or smaller FRR, may be imposed on biometric authentication systems in different application scenarios. We propose a novel method of score fusion based on quasi-convex optimization to directly improve biometric authentication metrics. Experiments based on a face recognition system demonstrate the effectiveness of the proposed method.