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

arXiv: https://arxiv.org/abs/2211.12483 · HTML: https://mlsanity.com/paper/2211.12483/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 31 references that are in the map.
- First posted on arXiv 22 Nov 2022.

## Builds on (most cited first, 11 of 11)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [MagFace: A Universal Representation for Face Recognition and Quality Assessment](https://mlsanity.com/paper/2103.06627/) (2021)
- [Deep Face Recognition: A Survey](https://mlsanity.com/paper/1804.06655/) (2018)
- [Probabilistic Face Embeddings](https://mlsanity.com/paper/1904.09658/) (2019)
- [SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness](https://mlsanity.com/paper/2003.09373/) (2020)
- [Post-Comparison Mitigation of Demographic Bias in Face Recognition Using Fair Score Normalization](https://mlsanity.com/paper/2002.03592/) (2020)
- [QMagFace: Simple and Accurate Quality-Aware Face Recognition](https://mlsanity.com/paper/2111.13475/) (2021)
- [A Comprehensive Overview of Biometric Fusion](https://mlsanity.com/paper/1902.02919/) (2019)
- [Causality-Inspired Taxonomy for Explainable Artificial Intelligence](https://mlsanity.com/paper/2208.09500/) (2022)
- [Stating Comparison Score Uncertainty and Verification Decision Confidence Towards Transparent Face Recognition](https://mlsanity.com/paper/2210.10354/) (2022)

## Built on by (most cited first, 4 of 4)

- [IFViT: Interpretable Fixed-Length Representation for Fingerprint Matching via Vision Transformer](https://mlsanity.com/paper/2404.08237/) (2024)
- [Unveiling the Two-Faced Truth: Disentangling Morphed Identities for Face Morphing Detection](https://mlsanity.com/paper/2306.03002/) (2023)
- [From Pixels to Words: Leveraging Explainability in Face Recognition through Interactive Natural Language Processing](https://mlsanity.com/paper/2409.16089/) (2024)
- [Intelligent Mobile AI-Generated Content Services via Interactive Prompt Engineering and Dynamic Service Provisioning](https://mlsanity.com/paper/2502.11386/) (2025)

## Abstract (arXiv)

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.