# Fingerprint Presentation Attack Detection: Generalization and Efficiency

arXiv: https://arxiv.org/abs/1812.11574 · HTML: https://mlsanity.com/paper/1812.11574/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2018.

## Built on by (most cited first, 9 of 9)

- [Biometrics: Trust, but Verify](https://mlsanity.com/paper/2105.06625/) (2021)
- [SpoofGAN: Synthetic Fingerprint Spoof Images](https://mlsanity.com/paper/2204.06498/) (2022)
- [A Unified Model for Fingerprint Authentication and Presentation Attack Detection](https://mlsanity.com/paper/2104.03255/) (2021)
- [LivDet2023 -- Fingerprint Liveness Detection Competition: Advancing Generalization](https://mlsanity.com/paper/2309.15578/) (2023)
- [Fingerprint Presentation Attack Detector Using Global-Local Model](https://mlsanity.com/paper/2402.12754/) (2024)
- [ViT Unified: Joint Fingerprint Recognition and Presentation Attack Detection](https://mlsanity.com/paper/2305.07602/) (2023)
- [EaZy Learning: An Adaptive Variant of Ensemble Learning for Fingerprint Liveness Detection](https://mlsanity.com/paper/2103.02207/) (2021)
- [A Uniform Representation Learning Method for OCT-based Fingerprint Presentation Attack Detection and Reconstruction](https://mlsanity.com/paper/2209.12208/) (2022)
- [Unsupervised Fingerphoto Presentation Attack Detection With Diffusion Models](https://mlsanity.com/paper/2409.18636/) (2024)

## Abstract (arXiv)

We study the problem of fingerprint presentation attack detection (PAD) under unknown PA materials not seen during PAD training. A dataset of 5,743 bonafide and 4,912 PA images of 12 different materials is used to evaluate a state-of-the-art PAD, namely Fingerprint Spoof Buster. We utilize 3D t-SNE visualization and clustering of material characteristics to identify a representative set of PA materials that cover most of PA feature space. We observe that a set of six PA materials, namely Silicone, 2D Paper, Play Doh, Gelatin, Latex Body Paint and Monster Liquid Latex provide a good representative set that should be included in training to achieve generalization of PAD. We also propose an optimized Android app of Fingerprint Spoof Buster that can run on a commodity smartphone (Xiaomi Redmi Note 4) without a significant drop in PAD performance (from TDR = 95.7% to 95.3% @ FDR = 0.2%) which can make a PA prediction in less than 300ms.