# Liveness Detection Competition -- Noncontact-based Fingerprint Algorithms and Systems (LivDet-2023 Noncontact Fingerprint)

arXiv: https://arxiv.org/abs/2310.00659 · HTML: https://mlsanity.com/paper/2310.00659/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 25 references that are in the map.
- First posted on arXiv 1 Oct 2023.

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

- [Iris Presentation Attack Detection by Attention-based and Deep Pixel-wise Binary Supervision Network](https://mlsanity.com/paper/2106.14845/) (2021)
- [SynCoLFinGer: Synthetic Contactless Fingerprint Generator](https://mlsanity.com/paper/2110.09144/) (2021)
- [Presentation Attack Detection with Advanced CNN Models for Noncontact-based Fingerprint Systems](https://mlsanity.com/paper/2303.05459/) (2023)
- [RidgeBase: A Cross-Sensor Multi-Finger Contactless Fingerprint Dataset](https://mlsanity.com/paper/2307.05563/) (2023)
- [Intra and Cross-spectrum Iris Presentation Attack Detection in the NIR and Visible Domains](https://mlsanity.com/paper/2205.02573/) (2022)

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

- [Contactless Fingerprint Biometric Anti-Spoofing: An Unsupervised Deep Learning Approach](https://mlsanity.com/paper/2311.04148/) (2023)
- [GRU-AUNet: A Domain Adaptation Framework for Contactless Fingerprint Presentation Attack Detection](https://mlsanity.com/paper/2504.01213/) (2025)
- [Saliency-Guided Training for Fingerprint Presentation Attack Detection](https://mlsanity.com/paper/2505.02176/) (2025)

## Abstract (arXiv)

Liveness Detection (LivDet) is an international competition series open to academia and industry with the objec-tive to assess and report state-of-the-art in Presentation Attack Detection (PAD). LivDet-2023 Noncontact Fingerprint is the first edition of the noncontact fingerprint-based PAD competition for algorithms and systems. The competition serves as an important benchmark in noncontact-based fingerprint PAD, offering (a) independent assessment of the state-of-the-art in noncontact-based fingerprint PAD for algorithms and systems, and (b) common evaluation protocol, which includes finger photos of a variety of Presentation Attack Instruments (PAIs) and live fingers to the biometric research community (c) provides standard algorithm and system evaluation protocols, along with the comparative analysis of state-of-the-art algorithms from academia and industry with both old and new android smartphones. The winning algorithm achieved an APCER of 11.35% averaged overall PAIs and a BPCER of 0.62%. The winning system achieved an APCER of 13.0.4%, averaged over all PAIs tested over all the smartphones, and a BPCER of 1.68% over all smartphones tested. Four-finger systems that make individual finger-based PAD decisions were also tested. The dataset used for competition will be available 1 to all researchers as per data share protocol