Saliency-Guided Training for Fingerprint Presentation Attack Detection

TL;DR

This paper introduces saliency-guided training for fingerprint presentation attack detection, demonstrating improved accuracy and generalization, especially with limited data, and achieving first place on the LivDet-2021 benchmark.

Contribution

It is the first application of saliency-guided training to fingerprint PAD, utilizing human and algorithmic saliency maps to enhance model performance.

Findings

Saliency-guided training improves fingerprint PAD accuracy.

Effective in limited and large data scenarios.

Achieved first place on LivDet-2021 benchmark.

Abstract

Saliency-guided training, which directs model learning to important regions of images, has demonstrated generalization improvements across various biometric presentation attack detection (PAD) tasks. This paper presents its first application to fingerprint PAD. We conducted a 50-participant study to create a dataset of 800 human-annotated fingerprint perceptually-important maps, explored alongside algorithmically-generated "pseudosaliency," including minutiae-based, image quality-based, and autoencoder-based saliency maps. Evaluating on the 2021 Fingerprint Liveness Detection Competition testing set, we explore various configurations within five distinct training scenarios to assess the impact of saliency-guided training on accuracy and generalization. Our findings demonstrate the effectiveness of saliency-guided training for fingerprint PAD in both limited and large data contexts, and we present a configuration capable of earning the first place on the LivDet-2021 benchmark. Our results highlight saliency-guided training's promise for increased model generalization capabilities, its effectiveness when data is limited, and its potential to scale to larger datasets in fingerprint PAD. All collected saliency data and trained models are released with the paper to support reproducible research.