# ColorVein: Colorful Cancelable Vein Biometrics

arXiv: https://arxiv.org/abs/2504.14253 · HTML: https://mlsanity.com/paper/2504.14253/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 71 references that are in the map.
- First posted on arXiv 19 Apr 2025.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data](https://mlsanity.com/paper/1904.00592/) (2019)
- [Real-Time User-Guided Image Colorization with Learned Deep Priors](https://mlsanity.com/paper/1705.02999/) (2017)
- [Scribbler: Controlling Deep Image Synthesis with Sketch and Color](https://mlsanity.com/paper/1612.00835/) (2016)
- [Reference-Based Sketch Image Colorization using Augmented-Self Reference and Dense Semantic Correspondence](https://mlsanity.com/paper/2005.05207/) (2020)
- [Learning Diverse Image Colorization](https://mlsanity.com/paper/1612.01958/) (2016)
- [DDColor: Towards Photo-Realistic Image Colorization via Dual Decoders](https://mlsanity.com/paper/2212.11613/) (2022)
- [Ranking Based Locality Sensitive Hashing Enabled Cancelable Biometrics: Index-of-Max Hashing](https://mlsanity.com/paper/1703.05455/) (2017)
- [General Framework to Evaluate Unlinkability in Biometric Template Protection Systems](https://mlsanity.com/paper/2311.04633/) (2023)
- [Physics-Driven Spectrum-Consistent Federated Learning for Palmprint Verification](https://mlsanity.com/paper/2308.00451/) (2023)
- [CFVNet: An End-to-End Cancelable Finger Vein Network for Recognition](https://mlsanity.com/paper/2409.14774/) (2024)

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

- [FVeinSyn: Synthetic Finger Vein Image Generator](https://mlsanity.com/paper/2608.27527/) (2026)

## Abstract (arXiv)

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.