  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.

Computer Vision and Pattern Recognition