In the context of biometrics, matching confidence refers to the confidence that a given matching decision is correct. Since many biometric systems operate in critical decision-making processes, such as in forensics investigations, accurately and reliably stating the matching confidence becomes of high importance. Previous works on biometric confidence estimation can well differentiate between high and low confidence, but lack interpretability. Therefore, they do not provide accurate probabilistic estimates of the correctness of a decision. In this work, we propose a probabilistic interpretable comparison (PIC) score that accurately reflects the probability that the score originates from samples of the same identity. We prove that the proposed approach provides optimal matching confidence. Contrary to other approaches, it can also optimally combine multiple samples in a joint PIC score which further increases the recognition and confidence estimation performance. In the experiments, the proposed PIC approach is compared against all biometric confidence estimation methods available on four publicly available databases and five state-of-the-art face recognition systems. The results demonstrate that PIC has a significantly more accurate probabilistic interpretation than similar approaches and is highly effective for multi-biometric recognition. The code is publicly-available.

翻译：在生物特征识别领域，匹配置信度是指给定匹配决策正确的置信程度。由于许多生物识别系统运行于关键决策过程中（如法医调查），准确可靠地陈述匹配置信度显得尤为重要。现有生物特征置信度估计方法虽能有效区分高/低置信度，但缺乏可解释性，因此无法提供关于决策正确性的精确概率估计。本文提出一种概率可解释比较（PIC）分数，该分数能精确反映当前得分来自同一身份样本的概率。我们证明该方法可提供最优匹配置信度。与其他方法不同，该方法还能通过联合PIC分数对多个样本进行最优融合，进一步提升识别与置信度估计性能。实验中将所提PIC方法与四个公开数据库及五个前沿人脸识别系统上现有的所有生物特征置信度估计方法进行对比，结果表明PIC具有比同类方法显著更高的概率解释准确性，且在多生物特征识别中表现出高效性。代码已开源。