用视觉重要区域指导模型训练，提升指纹活体检测精度与泛化能力。
Saliency-Guided Training for Fingerprint Presentation Attack Detection
- 基于人眼感知与算法生成的显著图引导模型聚焦关键区域。
- 在2021年活体检测竞赛数据集上达到第一名，小样本下仍有效。
- 适合关注生物特征识别安全与模型泛化能力的研究者。
显著性引导训练通过引导模型关注图像的重要区域，在多种生物特征活体攻击检测任务中展现出更好的泛化性能。本文首次将其应用于指纹活体攻击检测（fingerprint PAD）。我们开展了一项包含50名参与者的实验，构建了800张由人工标注的指纹感知重要性地图，并对比了基于细节点、图像质量及自编码器生成的“伪显著性”图。在2021年指纹活体检测竞赛测试集上，评估了五种不同训练场景下的配置，以分析显著性引导训练对准确率和泛化能力的影响。结果表明，该方法在数据有限和数据充足两种情境下均有效，且可实现LivDet-2021基准的第一名。研究凸显了显著性引导训练在提升模型泛化能力、应对数据稀缺以及扩展至大规模数据集方面的潜力。所有收集的显著性数据与训练模型均已公开，支持可复现研究。
原文摘要 · Abstract (English)
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.
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