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In the context of biometrics, matching confidence refers to the confidencethat a given matching decision is correct. Since many biometric systems operatein critical decision-making processes, such as in forensics investigations,accurately and reliably stating the matching confidence becomes of highimportance. Previous works on biometric confidence estimation can welldifferentiate between high and low confidence, but lack interpretability.Therefore, they do not provide accurate probabilistic estimates of thecorrectness of a decision. In this work, we propose a probabilisticinterpretable comparison (PIC) score that accurately reflects the probabilitythat the score originates from samples of the same identity. We prove that theproposed approach provides optimal matching confidence. Contrary to otherapproaches, it can also optimally combine multiple samples in a joint PIC scorewhich further increases the recognition and confidence estimation performance.In the experiments, the proposed PIC approach is compared against all biometricconfidence estimation methods available on four publicly available databasesand five state-of-the-art face recognition systems. The results demonstratethat PIC has a significantly more accurate probabilistic interpretation thansimilar approaches and is highly effective for multi-biometric recognition. Thecode is publicly-available.

PIC-Score: Probabilistic Interpretable Comparison Score for Optimal Matching Confidence in Single- and Multi-Biometric (Face) Recognition