LivDet 2017 Fingerprint Liveness Detection Competition 2017
Not on the map. Why?
Abstract (arXiv)
Fingerprint Presentation Attack Detection (FPAD) deals with distinguishing images coming from artificial replicas of the fingerprint characteristic, made up of materials like silicone, gelatine or latex, and images coming from alive fingerprints. Images are captured by modern scanners, typically relying on solid-state or optical technologies. Since from 2009, the Fingerprint Liveness Detection Competition (LivDet) aims to assess the performance of the state-of-the-art algorithms according to a rigorous experimental protocol and, at the same time, a simple overview of the basic achievements. The competition is open to all academics research centers and all companies that work in this field. The positive, increasing trend of the participants number, which supports the success of this initiative, is confirmed even this year: 17 algorithms were submitted to the competition, with a larger involvement of companies and academies. This means that the topic is relevant for both sides, and points out that a lot of work must be done in terms of fundamental and applied research.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Mar 2018.
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Citing papers per year: 2021: 4; 2022: 2; 2023: 1; 2024: 1; 2025: 2; 2026: 1.
11 papers.
- 2022SpoofGAN: Synthetic Fingerprint Spoof Imagescited by 17
- 2021Taming Self-Supervised Learning for Presentation Attack Detection: De-Folding and De-Mixingcited by 5
- 2021Fingerprint Presentation Attack Detection by Channel-wise Feature Denoisingcited by 5
- 2022Review of the Fingerprint Liveness Detection (LivDet) competition series: from 2009 to 2021cited by 5
- 2021A Unified Model for Fingerprint Authentication and Presentation Attack Detectioncited by 4
- 2021Fingerprint recognition with embedded presentation attacks detection: are we ready?cited by 3
- 2024Fingerprint Presentation Attack Detector Using Global-Local Modelcited by 3
- 2023Deep Learning based Fingerprint Presentation Attack Detection: A Comprehensive Surveycited by 2
- 2025Saliency-Guided Training for Fingerprint Presentation Attack Detectioncited by 1
- 2025LitMAS: A Lightweight and Generalized Multi-Modal Anti-Spoofing Framework for Biometric Securitycited by 0
- 2026What's Old is New Again: Classical Dimensionality Reduction for Efficient Saliency-Guided Biometric Attack Detectioncited by 0
- 2026What's Old is New Again: Classical Dimensionality Reduction for Efficient Saliency-Guided Biometric Attack Detection11 Jun 2026
- 2025LitMAS: A Lightweight and Generalized Multi-Modal Anti-Spoofing Framework for Biometric Security7 Jun 2025
- 2025Saliency-Guided Training for Fingerprint Presentation Attack Detection4 May 2025
- 2024Fingerprint Presentation Attack Detector Using Global-Local Model20 Feb 2024
- 2023Deep Learning based Fingerprint Presentation Attack Detection: A Comprehensive Survey27 May 2023
- 2022SpoofGAN: Synthetic Fingerprint Spoof Images13 Apr 2022
- 2022Review of the Fingerprint Liveness Detection (LivDet) competition series: from 2009 to 202115 Feb 2022
- 2021Fingerprint Presentation Attack Detection by Channel-wise Feature Denoising15 Nov 2021
- 2021Fingerprint recognition with embedded presentation attacks detection: are we ready?20 Oct 2021
- 2021Taming Self-Supervised Learning for Presentation Attack Detection: De-Folding and De-Mixing9 Sep 2021
- 2021A Unified Model for Fingerprint Authentication and Presentation Attack Detection7 Apr 2021