LivDet2023 -- Fingerprint Liveness Detection Competition: Advancing Generalization

TL;DR

LivDet2023 is a competition that advances fingerprint liveness detection by testing algorithms' generalization, effectiveness, and compactness in real-world scenarios with unknown sensor data.

Contribution

It introduces new challenges focusing on model generalization and feature efficiency in fingerprint PAD, with unseen sensor data to simulate real-world conditions.

Findings

Models' ability to generalize to unknown sensors is critically tested.

Compact feature sets can achieve competitive performance.

The competition benchmarks current state-of-the-art in fingerprint PAD.

Abstract

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.