One-Class Anomaly Detection for Finger Vein Presentation Attack Detection
TL;DR
This work establishes the effectiveness of unsupervised, frequency-enhanced anomaly detection for robust biometric security by outperforming supervised baselines like support vector machines and convolutional neural networks in generalizing to unseen digital and glossy photo attacks.
Abstract
The reliability of finger vein biometric systems is increasingly threatened by sophisticated presentation attacks. Current presentation attack detection (PAD) methods, often relying on supervised learning, are vulnerable to novel, unseen attacks because they depend on comprehensive labeled spoof datasets that are impractical to collect. To address this zero-day threat, this research proposes a one-class anomaly detection framework trained solely on authentic finger vein samples. While utilizing a standard U-Net-inspired denoising autoencoder as the architectural backbone, this work introduces two key novel contributions to tailor the model for biometric security: (1) a Multi-Objective Anomaly Detection Loss Framework that uniquely integrates multi-scale reconstruction error, gradient preservation constraints, and deep Support Vector Data Description loss to strictly regularize the latent space and (2) a Spectral Error Optimization technique that applies adaptive frequency weighting to amplify subtle texture artifacts inherent in spoof mediums. This combination is significant because the multi-objective loss forces the model to learn fine-grained physiological vein patterns, while the spectral optimization captures high-frequency anomalies often missed by spatial reconstruction alone. Experimental results on the FVPAD-USM dataset demonstrate that this approach achieves an Attack Presentation Classification Error Rate below 2% and an Average Classification Error Rate below 10%. By outperforming supervised baselines like support vector machines and convolutional neural networks in generalizing to unseen digital and glossy photo attacks, this work establishes the effectiveness of unsupervised, frequency-enhanced anomaly detection for robust biometric security. Received: 30 August 2025 | Revised: 18 March 2026 | Accepted: 18 June 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in the FVPAD-USM database at http://drfendi.com/fvpad_usm_database/, reference number [33]. Author Contribution Statement Mohd Shahrimie Mohd Asaari: Conceptualization, Methodology, Software, Formal analysis, Writing – original draft. Bakhtiar Affendi Rosdi: Validation, Investigation, Writing review & editing, Supervision. Andrew Tiong Hoe Pin: Methodology, Software, Validation, Investigation, Data curation, Visualization. Zahid Ur Rahman: Investigation, Resources, Data curation. Muhammad Firdaus Akbar: Validation, Funding acquisition.