# Towards the Generation of Synthetic Images of Palm Vein Patterns: A Review

arXiv: https://arxiv.org/abs/2205.10179 · HTML: https://mlsanity.com/paper/2205.10179/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 168 references that are in the map.
- First posted on arXiv 20 May 2022.

## Builds on (most cited first, 14 of 14)

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [Assessing Generative Models via Precision and Recall](https://mlsanity.com/paper/1806.00035/) (2018)
- [A Survey on Generative Adversarial Networks: Variants, Applications, and Training](https://mlsanity.com/paper/2006.05132/) (2020)
- [Image Synthesis with Adversarial Networks: a Comprehensive Survey and Case Studies](https://mlsanity.com/paper/2012.13736/) (2020)
- [Synthetic Iris Presentation Attack using iDCGAN](https://mlsanity.com/paper/1710.10565/) (2017)
- [One-Shot Domain Adaptation For Face Generation](https://mlsanity.com/paper/2003.12869/) (2020)
- [Re-Training StyleGAN -- A First Step Towards Building Large, Scalable Synthetic Facial Datasets](https://mlsanity.com/paper/2003.10847/) (2020)

## Built on by (most cited first, 3 of 3)

- [Deep Learning Techniques for Hand Vein Biometrics: A Comprehensive Review](https://mlsanity.com/paper/2409.07128/) (2024)
- [FingerVeinSyn-5M: A Million-Scale Dataset and Benchmark for Finger Vein Recognition](https://mlsanity.com/paper/2506.03635/) (2025)
- [Motion Transfer-Driven intra-class data augmentation for Finger Vein Recognition](https://mlsanity.com/paper/2412.20327/) (2024)

## Abstract (arXiv)

With the recent success of computer vision and deep learning, remarkable progress has been achieved on automatic personal recognition using vein biometrics. However, collecting large-scale real-world training data for palm vein recognition has turned out to be challenging, mainly due to the noise and irregular variations included at the time of acquisition. Meanwhile, existing palm vein recognition datasets are usually collected under near-infrared light, lacking detailed annotations on attributes (e.g., pose), so the influences of different attributes on vein recognition have been poorly investigated. Therefore, this paper examines the suitability of synthetic vein images generated to compensate for the urgent lack of publicly available large-scale datasets. Firstly, we present an overview of recent research progress on palm vein recognition, from the basic background knowledge to vein anatomical structure, data acquisition, public database, and quality assessment procedures. Then, we focus on the state-of-the-art methods that have allowed the generation of vascular structures for biometric purposes and the modeling of biological networks with their respective application domains. In addition, we review the existing research on the generation of style transfer and biological nature-based synthetic palm vein image algorithms. Afterward, we formalize a general flowchart for the creation of a synthetic database comparing real palm vein images and generated synthetic samples to obtain some understanding into the development of the realistic vein imaging system. Ultimately, we conclude by discussing the challenges, insights, and future perspectives in generating synthetic palm vein images for further works.