What's Old is New Again: Classical Dimensionality Reduction for Efficient Saliency-Guided Biometric Attack Detection

2026-06-11

AI Summary

This work addresses the limitations of existing saliency-guided training approaches in presentation attack detection (PAD), which rely on costly, domain-specific methods to obtain saliency maps and thus hinder broad applicability. For the first time, the study introduces classical dimensionality reduction techniques—Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA)—to generate saliency maps directly from raw training data, eliminating the need for manual annotations or domain-specific knowledge. By doing so, it overcomes critical bottlenecks in cost, generalizability, and scalability inherent in prior saliency-guided frameworks. The proposed method achieves competitive performance across multiple established and emerging PAD tasks, outperforming baseline approaches and even matching certain state-of-the-art methods, all without requiring additional resources or customized tools.

Abstract

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.

Problem

• saliency-guided training
• biometric presentation attack detection
• saliency acquisition
• scalability
• domain specificity

Innovation

• saliency-guided training
• dimensionality reduction
• PCA
• LDA
• presentation attack detection