# Improving fingerprint presentation attack detection by an approach integrated into the personal verification stage

arXiv: https://arxiv.org/abs/2504.11066 · HTML: https://mlsanity.com/paper/2504.11066/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 50 references that are in the map.
- First posted on arXiv 15 Apr 2025.

## Builds on (most cited first, 9 of 9)

- [Fingerprint Spoof Buster](https://mlsanity.com/paper/1712.04489/) (2017)
- [Fingerprint Presentation Attack Detection Based on Local Features Encoding for Unknown Attacks](https://mlsanity.com/paper/1908.10163/) (2019)
- [Fingerprint Presentation Attack Detection: A Sensor and Material Agnostic Approach](https://mlsanity.com/paper/2004.02941/) (2020)
- [Fingerprint Presentation Attack Detection by Channel-wise Feature Denoising](https://mlsanity.com/paper/2111.07620/) (2021)
- [Review of the Fingerprint Liveness Detection (LivDet) competition series: from 2009 to 2021](https://mlsanity.com/paper/2202.07259/) (2022)
- [Analysis of "User-Specific Effect" and Impact of Operator Skills on Fingerprint PAD Systems](https://mlsanity.com/paper/1907.08068/) (2019)
- [Fingerprint recognition with embedded presentation attacks detection: are we ready?](https://mlsanity.com/paper/2110.10567/) (2021)
- [LivDet2023 -- Fingerprint Liveness Detection Competition: Advancing Generalization](https://mlsanity.com/paper/2309.15578/) (2023)
- [ColFigPhotoAttnNet: Reliable Finger Photo Presentation Attack Detection Leveraging Window-Attention on Color Spaces](https://mlsanity.com/paper/2503.05247/) (2025)

## Built on by (most cited first, 1 of 1)

- [DiffSwap++: 3D Latent-Controlled Diffusion for Identity-Preserving Face Swapping](https://mlsanity.com/paper/2511.05575/) (2025)

## Abstract (arXiv)

Presentation Attack Detection (PAD) systems are usually designed independently of the fingerprint verification system. While this can be acceptable for use cases where specific user templates are not predetermined, it represents a missed opportunity to enhance security in scenarios where integrating PAD with the fingerprint verification system could significantly leverage users' templates, which are the real target of a potential presentation attack. This does not mean that a PAD should be specifically designed for such users; that would imply the availability of many enrolled users' PAI and, consequently, complexity, time, and cost increase. On the contrary, we propose to equip a basic PAD, designed according to the state of the art, with an innovative add-on module called the Closeness Binary Code (CC) module. The term "closeness" refers to a peculiar property of the bona fide-related features: in an Euclidean feature space, genuine fingerprints tend to cluster in a specific pattern. First, samples from the same finger are close to each other, then samples from other fingers of the same user and finally, samples from fingers of other users. This property is statistically verified in our previous publication, and further confirmed in this paper. It is independent of the user population and the feature set class, which can be handcrafted or deep network-based (embeddings). Therefore, the add-on can be designed without the need for the targeted user samples; moreover, it exploits her/his samples' "closeness" property during the verification stage. Extensive experiments on benchmark datasets and state-of-the-art PAD methods confirm the benefits of the proposed add-on, which can be easily coupled with the main PAD module integrated into the fingerprint verification system.