# Motion Transfer-Driven intra-class data augmentation for Finger Vein Recognition

arXiv: https://arxiv.org/abs/2412.20327 · HTML: https://mlsanity.com/paper/2412.20327/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 29 Dec 2024.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [First Order Motion Model for Image Animation](https://mlsanity.com/paper/2003.00196/) (2020)
- [Motion Representations for Articulated Animation](https://mlsanity.com/paper/2104.11280/) (2021)
- [Controllable Person Image Synthesis with Attribute-Decomposed GAN](https://mlsanity.com/paper/2003.12267/) (2020)
- [Laplace Landmark Localization](https://mlsanity.com/paper/1903.11633/) (2019)
- [Towards the Generation of Synthetic Images of Palm Vein Patterns: A Review](https://mlsanity.com/paper/2205.10179/) (2022)

## Abstract (arXiv)

Finger vein recognition (FVR) has emerged as a secure biometric technique because of the confidentiality of vascular bio-information. Recently, deep learning-based FVR has gained increased popularity and achieved promising performance. However, the limited size of public vein datasets has caused overfitting issues and greatly limits the recognition performance. Although traditional data augmentation can partially alleviate this data shortage issue, it cannot capture the real finger posture variations due to the rigid label-preserving image transformations, bringing limited performance improvement. To address this issue, we propose a novel motion transfer (MT) model for finger vein image data augmentation via modeling the actual finger posture and rotational movements. The proposed model first utilizes a key point detector to extract the key point and pose map of the source and drive finger vein images. We then utilize a dense motion module to estimate the motion optical flow, which is fed to an image generation module for generating the image with the target pose. Experiments conducted on three public finger vein databases demonstrate that the proposed motion transfer model can effectively improve recognition accuracy. Code is available at: https://github.com/kevinhuangxf/FingerVeinRecognition.