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:PIC-Score:单模和多模式生物识别(人脸识别)中的概率可解释比较得分,以获得最佳匹配置信度。
在生物识别的情境下,匹配置信度指的是一个给定匹配决策是正确的置信度。由于许多生物识别系统在关键的决策过程中进行运作,例如在法医调查中,准确地和可靠地陈述匹配置信度变得非常重要。先前的生物识别置信度估计方法可以很好地区分高置信度和低置信度,但缺乏可解释性。因此,他们不能提供正确的决策正确性的概率估计。在这项工作中,我们提出了一种概率可解释比较(PIC)得分,准确地反映了该得分源自相同身份样本的概率。我们证明所提出的方法提供了最佳的匹配置信度。与其他方法相反,它还可以在联合PIC分数中最优地组合多个样本,从而进一步增加识别和置信度估计的性能。在实验中,所提出的PIC方法与四个公共数据库和五个最先进的人脸识别系统上所有生物识别置信度估计方法进行了比较。结果表明,PIC比类似的方法具有更准确的概率解释,并且非常适用于多种生物识别。代码可以公开获得。