# Comprehensive Survey: Biometric User Authentication Application, Evaluation, and Discussion

arXiv: https://arxiv.org/abs/2311.13416 · HTML: https://mlsanity.com/paper/2311.13416/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2023.

## Built on by (most cited first, 5 of 5)

- [Temporal Persistence and Intercorrelation of Embeddings Learned by an End-to-End Deep Learning Eye Movement-driven Biometrics Pipeline](https://mlsanity.com/paper/2402.16399/) (2024)
- [A Comprehensive Re-Evaluation of Biometric Modality Properties in the Modern Era](https://mlsanity.com/paper/2508.13874/) (2025)
- [Beyond Appearance: Transformer-based Person Identification from Conversational Dynamics](https://mlsanity.com/paper/2510.04753/) (2025)
- [Two-Stream Spatial-Temporal Transformer Framework for Person Identification via Natural Conversational Keypoints](https://mlsanity.com/paper/2502.20803/) (2025)
- [Transfer Learning of Keystroke Dynamics for Cross-Device User Authentication](https://mlsanity.com/paper/2608.16334/) (2026)

## Abstract (arXiv)

This paper conducts an extensive review of biometric user authentication literature, addressing three primary research questions: (1) commonly used biometric traits and their suitability for specific applications, (2) performance factors such as security, convenience, and robustness, and potential countermeasures against cyberattacks, and (3) factors affecting biometric system accuracy and po-tential improvements. Our analysis delves into physiological and behavioral traits, exploring their pros and cons. We discuss factors influencing biometric system effectiveness and highlight areas for enhancement. Our study differs from previous surveys by extensively examining biometric traits, exploring various application domains, and analyzing measures to mitigate cyberattacks. This paper aims to inform researchers and practitioners about the biometric authentication landscape and guide future advancements.