👏 Sclera-TransFuse 🔥

This repository contains the testing code of Sclera-TransFuse: Fusing Vision Transformer and CNN for Accurate Sclera Segmentation and Recognition

Requirements

• Python>=3.8
• Pytorch>=1.13.0
• timm>=0.5

Ubiris.v2

A UBIRIS.v2 subset of 683 eye images with manually labeled sclera masks, which can be found in this download link (Google Drive) and download link (Baidu Pan) .

Experiments

1. Sclera-TransFuse-Seg
   • Downloading our trained weight from Google Drive, and move it into ./checkpoints
   • modify the path in Sclera_TransFuse.py
   • modify the path in testing.py
   • run testing.py
2. How to training Sclera-TransFuse-Rec
   • modify --train_root_path=" " --train_list=" " and --save_path=" " in training.bash
   • run training.bash
3. Feature extraction and matching
   • Downloading our trained weight from Google Drive
   • modify matching.py
   • run matching.py

These codes are not the final version.

Reference

Some of the codes in this repo are borrowed from:

• LightCNN
• DS-TransUNet
• Combining-EfficientNet-and-Vision-Transformers-for-Video-Deepfake-Detection

Questions

Please drop an email to haiqing_li@stu.bucea.edu.cn

Citation

If you find our work useful in your research, please consider citing:

@inproceedings{HaiqingIJCB2023,
    title={Sclera-TransFuse: Fusing Swin Transformer and CNN for Accurate Sclera Segmentation},
    author={Li, Haiqing and Wang, Caiyong and Zhao, Guangzhe and He, Zhaofeng and Wang, Yunlong and Sun, Zhenan},
    booktitle={Proceedings of the IEEE International Joint Conference on Biometrics (IJCB)},
    pages={1--8},
    year={2023},
    organization={IEEE}