# The Invisible Threat: Evaluating the Vulnerability of Cross-Spectral Face Recognition to Presentation Attacks

arXiv: https://arxiv.org/abs/2505.00380 · HTML: https://mlsanity.com/paper/2505.00380/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 37 references that are in the map.
- First posted on arXiv 1 May 2025.

## Builds on (most cited first, 13 of 13)

- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments](https://mlsanity.com/paper/1708.08197/) (2017)
- [Biometric Face Presentation Attack Detection with Multi-Channel Convolutional Neural Network](https://mlsanity.com/paper/1909.08848/) (2019)
- [Learning Meta Face Recognition in Unseen Domains](https://mlsanity.com/paper/2003.07733/) (2020)
- [DVG-Face: Dual Variational Generation for Heterogeneous Face Recognition](https://mlsanity.com/paper/2009.09399/) (2020)
- [Prepended Domain Transformer: Heterogeneous Face Recognition without Bells and Whistles](https://mlsanity.com/paper/2210.06529/) (2022)
- [The High-Quality Wide Multi-Channel Attack (HQ-WMCA) database](https://mlsanity.com/paper/2009.09703/) (2020)
- [Can Your Face Detector Do Anti-spoofing? Face Presentation Attack Detection with a Multi-Channel Face Detector](https://mlsanity.com/paper/2006.16836/) (2020)
- [A Comprehensive Evaluation on Multi-channel Biometric Face Presentation Attack Detection](https://mlsanity.com/paper/2202.10286/) (2022)
- [Beyond the Visible: A Survey on Cross-spectral Face Recognition](https://mlsanity.com/paper/2201.04435/) (2022)
- [Heterogeneous Face Recognition Using Domain Invariant Units](https://mlsanity.com/paper/2404.14343/) (2024)
- [Modality Agnostic Heterogeneous Face Recognition with Switch Style Modulators](https://mlsanity.com/paper/2407.08640/) (2024)
- [Rethinking the Domain Gap in Near-infrared Face Recognition](https://mlsanity.com/paper/2312.00627/) (2023)

## Built on by (most cited first, 1 of 1)

- [DriveFace: A Cross-Spectral Through-Glass Face Dataset for On-the-Move Vehicular Border Control](https://mlsanity.com/paper/2607.13515/) (2026)

## Abstract (arXiv)

Cross-spectral face recognition systems are designed to enhance the performance of facial recognition systems by enabling cross-modal matching under challenging operational conditions. A particularly relevant application is the matching of near-infrared (NIR) images to visible-spectrum (VIS) images, enabling the verification of individuals by comparing NIR facial captures acquired with VIS reference images. The use of NIR imaging offers several advantages, including greater robustness to illumination variations, better visibility through glasses and glare, and greater resistance to presentation attacks. Despite these claimed benefits, the robustness of NIR-based systems against presentation attacks has not been systematically studied in the literature. In this work, we conduct a comprehensive evaluation into the vulnerability of NIR-VIS cross-spectral face recognition systems to presentation attacks. Our empirical findings indicate that, although these systems exhibit a certain degree of reliability, they remain vulnerable to specific attacks, emphasizing the need for further research in this area.