ColorVein: Colorful Cancelable Vein Biometrics

2025-04-19

AI Summary

Vein recognition faces privacy and anonymity risks due to biometric template leakage, and existing schemes lack revocable template generation methods tailored to vein characteristics. This paper proposes ColorVein—the first revocable template framework specifically designed for vein biometrics—mapping grayscale vein images into a controllable pseudo-random color space via interactive colorization, preserving original feature fidelity while ensuring strong privacy protection. Key innovations include: (1) a user-controllable coloring mechanism; (2) vein-specific deep feature embedding; (3) a secure center loss function enhancing inter-class discriminability; and (4) a comprehensive privacy evaluation framework covering unlinkability, irreversibility, revocability, and attack resistance. Evaluated on multi-source vein datasets, ColorVein achieves state-of-the-art recognition accuracy while strictly satisfying all four security requirements of revocable biometrics.

Technology Category

• Computer Vision: Bias, Fairness & Privacy
• Machine Learning: Privacy
• Humans and AI: Voting

Application Category

• Security and Privacy: Privacy-enhancing technologies
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Vein recognition technologies have become one of the primary solutions for high-security identification systems. However, the issue of biometric information leakage can still pose a serious threat to user privacy and anonymity. Currently, there is no cancelable biometric template generation scheme specifically designed for vein biometrics. Therefore, this paper proposes an innovative cancelable vein biometric generation scheme: ColorVein. Unlike previous cancelable template generation schemes, ColorVein does not destroy the original biometric features and introduces additional color information to grayscale vein images. This method significantly enhances the information density of vein images by transforming static grayscale information into dynamically controllable color representations through interactive colorization. ColorVein allows users/administrators to define a controllable pseudo-random color space for grayscale vein images by editing the position, number, and color of hint points, thereby generating protected cancelable templates. Additionally, we propose a new secure center loss to optimize the training process of the protected feature extraction model, effectively increasing the feature distance between enrolled users and any potential impostors. Finally, we evaluate ColorVein's performance on all types of vein biometrics, including recognition performance, unlinkability, irreversibility, and revocability, and conduct security and privacy analyses. ColorVein achieves competitive performance compared with state-of-the-art methods.

Problem

• Prevent biometric leakage in vein recognition systems
• Generate cancelable templates for vein biometrics
• Enhance security with dynamic color representations

Innovation

• Colorful cancelable vein biometric generation scheme
• Dynamic colorization enhances vein image information density
• Secure center loss optimizes protected feature extraction