# Generalizing Fingerprint Spoof Detector: Learning a One-Class Classifier

arXiv: https://arxiv.org/abs/1901.03918 · HTML: https://mlsanity.com/paper/1901.03918/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2019.

## Built on by (most cited first, 13 of 13)

- [Biometrics: Trust, but Verify](https://mlsanity.com/paper/2105.06625/) (2021)
- [One-Class Classification: A Survey](https://mlsanity.com/paper/2101.03064/) (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)
- [On the Generalisation Capabilities of Fisher Vector based Face Presentation Attack Detection](https://mlsanity.com/paper/2103.01721/) (2021)
- [GAN pretraining for deep convolutional autoencoders applied to Software-based Fingerprint Presentation Attack Detection](https://mlsanity.com/paper/2105.10213/) (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)
- [Deep Learning based Fingerprint Presentation Attack Detection: A Comprehensive Survey](https://mlsanity.com/paper/2305.17522/) (2023)
- [A Parameter-Efficient Quantum Anomaly Detection Method on a Superconducting Quantum Processor](https://mlsanity.com/paper/2412.16867/) (2024)
- [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)

Prevailing fingerprint recognition systems are vulnerable to spoof attacks. To mitigate these attacks, automated spoof detectors are trained to distinguish a set of live or bona fide fingerprints from a set of known spoof fingerprints. Despite their success, spoof detectors remain vulnerable when exposed to attacks from spoofs made with materials not seen during training of the detector. To alleviate this shortcoming, we approach spoof detection as a one-class classification problem. The goal is to train a spoof detector on only the live fingerprints such that once the concept of "live" has been learned, spoofs of any material can be rejected. We accomplish this through training multiple generative adversarial networks (GANS) on live fingerprint images acquired with the open source, dual-camera, 1900 ppi RaspiReader fingerprint reader. Our experimental results, conducted on 5.5K spoof images (from 12 materials) and 11.8K live images show that the proposed approach improves the cross-material spoof detection performance over state-of-the-art one-class and binary class spoof detectors on 11 of 12 testing materials and 7 of 12 testing materials, respectively.