Liveness Detection Competition -- Noncontact-based Fingerprint Algorithms and Systems (LivDet-2023 Noncontact Fingerprint)
Sandip Purnapatra, Humaira Rezaie, Bhavin Jawade, Yu Liu, Yue Pan,, Luke Brosell, Mst Rumana Sumi, Lambert Igene, Alden Dimarco, Srirangaraj, Setlur, Soumyabrata Dey, Stephanie Schuckers, Marco Huber, Jan Niklas Kolf,, Meiling Fang, Naser Damer, Banafsheh Adami, Raul Chitic

TL;DR
LivDet-2023 Noncontact Fingerprint is the first competition benchmarking state-of-the-art noncontact fingerprint PAD algorithms and systems, providing standardized evaluation protocols and a comprehensive dataset for research advancement.
Contribution
This paper introduces the first noncontact fingerprint PAD competition, establishing evaluation standards and benchmarking algorithms and systems across diverse devices and presentation attack instruments.
Findings
Winning algorithm achieved 11.35% APCER and 0.62% BPCER.
Winning system achieved 13.04% APCER and 1.68% BPCER.
Dataset will be publicly available for research use.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsBiometric Identification and Security
