# Fingerprint Spoof Buster

arXiv: https://arxiv.org/abs/1712.04489 · HTML: https://mlsanity.com/paper/1712.04489/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2017.

## Built on by (most cited first, 27 of 27)

- [Biometrics: Trust, but Verify](https://mlsanity.com/paper/2105.06625/) (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)
- [LivDet 2021 Fingerprint Liveness Detection Competition -- Into the unknown](https://mlsanity.com/paper/2108.10183/) (2021)
- [EmMixformer: Mix transformer for eye movement recognition](https://mlsanity.com/paper/2401.04956/) (2024)
- [Taming Self-Supervised Learning for Presentation Attack Detection: De-Folding and De-Mixing](https://mlsanity.com/paper/2109.04100/) (2021)
- [Fingerprint Presentation Attack Detection by Channel-wise Feature Denoising](https://mlsanity.com/paper/2111.07620/) (2021)
- [StarLKNet: Star Mixup with Large Kernel Networks for Palm Vein Identification](https://mlsanity.com/paper/2405.12721/) (2024)
- [A Unified Model for Fingerprint Authentication and Presentation Attack Detection](https://mlsanity.com/paper/2104.03255/) (2021)
- [GAN pretraining for deep convolutional autoencoders applied to Software-based Fingerprint Presentation Attack Detection](https://mlsanity.com/paper/2105.10213/) (2021)
- [Fingerprint Presentation Attack Detector Using Global-Local Model](https://mlsanity.com/paper/2402.12754/) (2024)
- [MoSFPAD: An end-to-end Ensemble of MobileNet and Support Vector Classifier for Fingerprint Presentation Attack Detection](https://mlsanity.com/paper/2303.01465/) (2023)
- [Deep Learning based Fingerprint Presentation Attack Detection: A Comprehensive Survey](https://mlsanity.com/paper/2305.17522/) (2023)
- [An Open Patch Generator based Fingerprint Presentation Attack Detection using Generative Adversarial Network](https://mlsanity.com/paper/2306.03577/) (2023)
- [Spoofing Detection on Hand Images Using Quality Assessment](https://mlsanity.com/paper/2110.12923/) (2021)
- [Fingerprint Template Invertibility: Minutiae vs. Deep Templates](https://mlsanity.com/paper/2205.03809/) (2022)
- [ViT Unified: Joint Fingerprint Recognition and Presentation Attack Detection](https://mlsanity.com/paper/2305.07602/) (2023)
- [Improving fingerprint presentation attack detection by an approach integrated into the personal verification stage](https://mlsanity.com/paper/2504.11066/) (2025)
- [Saliency-Guided Training for Fingerprint Presentation Attack Detection](https://mlsanity.com/paper/2505.02176/) (2025)
- [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)
- [Introduction to Presentation Attack Detection in Fingerprint Biometrics](https://mlsanity.com/paper/2304.06723/) (2023)
- [EXPRESSNET: An Explainable Residual Slim Network for Fingerprint Presentation Attack Detection](https://mlsanity.com/paper/2305.09397/) (2023)
- [DyFFPAD: Dynamic Fusion of Convolutional and Handcrafted Features for Fingerprint Presentation Attack Detection](https://mlsanity.com/paper/2308.10015/) (2023)
- [Continual Few-shot Adaptation for Synthetic Fingerprint Detection](https://mlsanity.com/paper/2603.14632/) (2026)
- [Towards Billion-scale Multi-modal Biometric Search](https://mlsanity.com/paper/2605.07655/) (2026)
- [Cascade Forgery Mining Network for Fingerprint Presentation Attack Detection](https://mlsanity.com/paper/2607.24090/) (2026)

## Abstract (arXiv)

The primary purpose of a fingerprint recognition system is to ensure a reliable and accurate user authentication, but the security of the recognition system itself can be jeopardized by spoof attacks. This study addresses the problem of developing accurate, generalizable, and efficient algorithms for detecting fingerprint spoof attacks. Specifically, we propose a deep convolutional neural network based approach utilizing local patches centered and aligned using fingerprint minutiae. Experimental results on three public-domain LivDet datasets (2011, 2013, and 2015) show that the proposed approach provides state-of-the-art accuracies in fingerprint spoof detection for intra-sensor, cross-material, cross-sensor, as well as cross-dataset testing scenarios. For example, in LivDet 2015, the proposed approach achieves 99.03% average accuracy over all sensors compared to 95.51% achieved by the LivDet 2015 competition winners. Additionally, two new fingerprint presentation attack datasets containing more than 20,000 images, using two different fingerprint readers, and over 12 different spoof fabrication materials are collected. We also present a graphical user interface, called Fingerprint Spoof Buster, that allows the operator to visually examine the local regions of the fingerprint highlighted as live or spoof, instead of relying on only a single score as output by the traditional approaches.