# A Comprehensive Re-Evaluation of Biometric Modality Properties in the Modern Era

arXiv: https://arxiv.org/abs/2508.13874 · HTML: https://mlsanity.com/paper/2508.13874/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 68 references that are in the map.
- First posted on arXiv 19 Aug 2025.

## Builds on (most cited first, 8 of 8)

- [Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments](https://mlsanity.com/paper/1708.08197/) (2017)
- [ECG Biometric Recognition: Review, System Proposal, and Benchmark Evaluation](https://mlsanity.com/paper/2204.03992/) (2022)
- [Some Research Problems in Biometrics: The Future Beckons](https://mlsanity.com/paper/1905.04717/) (2019)
- [A Latent Fingerprint in the Wild Database](https://mlsanity.com/paper/2304.00979/) (2023)
- [Comprehensive Survey: Biometric User Authentication Application, Evaluation, and Discussion](https://mlsanity.com/paper/2311.13416/) (2023)
- [On the Reliability of Biometric Datasets: How Much Test Data Ensures Reliability?](https://mlsanity.com/paper/2501.06504/) (2025)
- [Statistical Methods for Assessing Differences in False Non-Match Rates Across Demographic Groups](https://mlsanity.com/paper/2208.10948/) (2022)
- [Damaged Fingerprint Recognition by Convolutional Long Short-Term Memory Networks for Forensic Purposes](https://mlsanity.com/paper/2012.15041/) (2020)

## Built on by (most cited first, 2 of 2)

- [SoK: Security Evaluation of Wi-Fi CSI Biometrics: Attacks, Metrics, and Open Challenges](https://mlsanity.com/paper/2511.11381/) (2025)
- [Efficient Deep Learning for Biometrics: Overview, Challenges and Trends in Ear of Frugal AI](https://mlsanity.com/paper/2602.08809/) (2026)

## Abstract (arXiv)

The rapid advancement of authentication systems and their increasing reliance on biometrics for faster and more accurate user verification experience, highlight the critical need for a reliable framework to evaluate the suitability of biometric modalities for specific applications. Currently, the most widely known evaluation framework is a comparative table from 1998, which no longer adequately captures recent technological developments or emerging vulnerabilities in biometric systems. To address these challenges, this work revisits the evaluation of biometric modalities through an expert survey involving 24 biometric specialists. The findings indicate substantial shifts in property ratings across modalities. For example, face recognition, shows improved ratings due to technological progress, while fingerprint, shows decreased reliability because of emerging vulnerabilities and attacks. Further analysis of expert agreement levels across rated properties highlighted the consistency of the provided evaluations and ensured the reliability of the ratings. Finally, expert assessments are compared with dataset-level uncertainty across 55 biometric datasets, revealing strong alignment in most modalities and underscoring the importance of integrating empirical evidence with expert insight. Moreover, the identified expert disagreements reveal key open challenges and help guide future research toward resolving them.