# Evaluation of direct attacks to fingerprint verification systems

arXiv: https://arxiv.org/abs/2212.07575 · HTML: https://mlsanity.com/paper/2212.07575/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 38 references that are in the map.
- First posted on arXiv 15 Dec 2022.

## Builds on (most cited first, 2 of 2)

- [A Comparative Study of Fingerprint Image-Quality Estimation Methods](https://mlsanity.com/paper/2111.07432/) (2021)
- [On the vulnerability of fingerprint verification systems to fake fingerprint attacks](https://mlsanity.com/paper/2207.04813/) (2022)

## Built on by (most cited first, 3 of 3)

- [Deep Learning based Fingerprint Presentation Attack Detection: A Comprehensive Survey](https://mlsanity.com/paper/2305.17522/) (2023)
- [Introduction to Presentation Attack Detection in Fingerprint Biometrics](https://mlsanity.com/paper/2304.06723/) (2023)
- [Adaptive thresholding pattern for fingerprint forgery detection](https://mlsanity.com/paper/2511.15322/) (2025)

## Abstract (arXiv)

The vulnerabilities of fingerprint-based recognition systems to direct attacks with and without the cooperation of the user are studied. Two different systems, one minutiae-based and one ridge feature-based, are evaluated on a database of real and fake fingerprints. Based on the fingerprint images quality and on the results achieved on different operational scenarios, we obtain a number of statistically significant observations regarding the robustness of the systems.