Saliency-guided training is a paradigm in visual recognition that encourages models to focus on the most relevant image regions during learning. While its application in biometric presentation attack detection (PAD) has shown strong benefits in robustness and generalization, adoption is often limited by the high cost, domain specificity, and limited scalability of existing saliency acquisition methods, such as human annotations over a limited dataset. We present a novel, cost-efficient, and highly-scalable approach to saliency acquisition using maps inspired by classical dimensionality reduction techniques: PCA and LDA. Our proposed methods generate saliency maps directly from raw training data, requiring no human annotation nor domain knowledge. We contextualize the effectiveness of these saliency sources in three saliency-explored domains (iris PAD, synthetic face detection, fingerprint PAD) and demonstrate its scalability in two saliency-novel domains (fingerprint vein PAD and ID card PAD). Across all domains tested, models trained using dimensionality reduction-sourced saliency maps exceed baseline and sometimes SOTA saliency methods without any resource investment or domain-specific tooling. Our findings overcome an important yet unaddressed barrier to saliency-guided training for biometric attack detection and beyond.

翻译：显著性引导训练是一种视觉识别范式，旨在促使模型在学习过程中聚焦图像中最相关的区域。尽管该方法在生物特征呈现攻击检测（PAD）的鲁棒性和泛化能力方面展现出显著优势，但其应用常受限于现有显著性获取方法的高成本、领域特异性及有限可扩展性（例如，在有限数据集上进行人工标注）。本文提出一种新颖、成本高效且高度可扩展的显著性获取方法，利用基于经典降维技术（PCA与LDA）生成的显著性图。所提方法可直接从原始训练数据生成显著性图，无需人工标注或领域知识。我们在三个显著性探索领域（虹膜PAD、合成人脸检测、指纹PAD）中验证了该显著性来源的有效性，并在两个显著性新兴领域（指静脉PAD与身份证件PAD）中展示了其可扩展性。在所有测试领域中，使用降维源显著性图训练的模型在无需任何资源投入或领域专用工具的情况下，均超越基线方法，部分甚至达到当前最优（SOTA）显著性方法。本研究克服了显著性引导训练在生物特征攻击检测及更广泛任务中面临的一项重要但尚未解决的障碍。