# Fingerprint Presentation Attack Detection: A Sensor and Material Agnostic Approach

arXiv: https://arxiv.org/abs/2004.02941 · HTML: https://mlsanity.com/paper/2004.02941/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2020.

## Built on by (most cited first, 7 of 7)

- [Biometrics: Trust, but Verify](https://mlsanity.com/paper/2105.06625/) (2021)
- [C2CL: Contact to Contactless Fingerprint Matching](https://mlsanity.com/paper/2104.02811/) (2021)
- [AFR-Net: Attention-Driven Fingerprint Recognition Network](https://mlsanity.com/paper/2211.13897/) (2022)
- [SpoofGAN: Synthetic Fingerprint Spoof Images](https://mlsanity.com/paper/2204.06498/) (2022)
- [Improving fingerprint presentation attack detection by an approach integrated into the personal verification stage](https://mlsanity.com/paper/2504.11066/) (2025)
- [Continual Few-shot Adaptation for Synthetic Fingerprint Detection](https://mlsanity.com/paper/2603.14632/) (2026)
- [Cascade Forgery Mining Network for Fingerprint Presentation Attack Detection](https://mlsanity.com/paper/2607.24090/) (2026)

## Abstract (arXiv)

The vulnerability of automated fingerprint recognition systems to presentation attacks (PA), i.e., spoof or altered fingers, has been a growing concern, warranting the development of accurate and efficient presentation attack detection (PAD) methods. However, one major limitation of the existing PAD solutions is their poor generalization to new PA materials and fingerprint sensors, not used in training. In this study, we propose a robust PAD solution with improved cross-material and cross-sensor generalization. Specifically, we build on top of any CNN-based architecture trained for fingerprint spoof detection combined with cross-material spoof generalization using a style transfer network wrapper. We also incorporate adversarial representation learning (ARL) in deep neural networks (DNN) to learn sensor and material invariant representations for PAD. Experimental results on LivDet 2015 and 2017 public domain datasets exhibit the effectiveness of the proposed approach.