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，匹配置信度指给定匹配决策正确的置信程度。由于许多生物特征系统运行于关键决策过程（如法医调查）中，准确可靠地表述匹配置信度变得至关重要。已有关于生物特征置信度估计的研究能够较好地区分高置信度与低置信度，但缺乏可解释性，因此无法提供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，可准确反映分数源于同一身份样本的概率。我们证明所提方法能提供最优匹配置信度。与其他方法不同，该方法还能以联合PIC分数形式最优组合多个样本，进一步提升识别与置信度估计性能。实验部分，我们在四个公开数据库和五个最先进人脸识别系统上，将所有可用生物特征置信度估计方法与所提PIC方法进行对比。结果表明，PIC的概率解释精度显著优于同类方法，且在多重生物特征识别中具有高效性。代码已公开。