# LivDet2023 -- Fingerprint Liveness Detection Competition: Advancing Generalization

arXiv: https://arxiv.org/abs/2309.15578 · HTML: https://mlsanity.com/paper/2309.15578/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 27 Sep 2023.

## Builds on (most cited first, 5 of 5)

- [Generalizing Fingerprint Spoof Detector: Learning a One-Class Classifier](https://mlsanity.com/paper/1901.03918/) (2019)
- [Fingerprint Presentation Attack Detection: Generalization and Efficiency](https://mlsanity.com/paper/1812.11574/) (2018)
- [Review of the Fingerprint Liveness Detection (LivDet) competition series: from 2009 to 2021](https://mlsanity.com/paper/2202.07259/) (2022)
- [Analysis of "User-Specific Effect" and Impact of Operator Skills on Fingerprint PAD Systems](https://mlsanity.com/paper/1907.08068/) (2019)
- [Fingerprint recognition with embedded presentation attacks detection: are we ready?](https://mlsanity.com/paper/2110.10567/) (2021)

## Built on by (most cited first, 3 of 3)

- [Comprehensive Equity Index (CEI): Definition and Application to Bias Evaluation in Biometrics](https://mlsanity.com/paper/2409.01928/) (2024)
- [Improving fingerprint presentation attack detection by an approach integrated into the personal verification stage](https://mlsanity.com/paper/2504.11066/) (2025)
- [Boosting Generalization with Adaptive Style Techniques for Fingerprint Liveness Detection](https://mlsanity.com/paper/2310.13573/) (2023)

## Abstract (arXiv)

The International Fingerprint Liveness Detection Competition (LivDet) is a biennial event that invites academic and industry participants to prove their advancements in Fingerprint Presentation Attack Detection (PAD). This edition, LivDet2023, proposed two challenges, Liveness Detection in Action and Fingerprint Representation, to evaluate the efficacy of PAD embedded in verification systems and the effectiveness and compactness of feature sets. A third, hidden challenge is the inclusion of two subsets in the training set whose sensor information is unknown, testing participants ability to generalize their models. Only bona fide fingerprint samples were provided to participants, and the competition reports and assesses the performance of their algorithms suffering from this limitation in data availability.