UniNet-Pytorch
Pytorch port of UniNet.
An accurate and generalizable deep learning framework for iris recognition.
Reference:
Zijing Zhao and Ajay Kumar, "Towards More Accurate Iris Recognition Using Deeply Learned Spatially Corresponding Features", Internation Conference on Computer Vision (ICCV), Spotlight, Venice, Italy, 2017.
Install
- Python 3.6
- Pytorch 1.0+
- torchvision 0.2.2+
- opencv 3.4
- caffe(Optional)
- tqdm(Optional)
Code structure
- ICCV17_release
- Source code and caffe model attached to the paper
- models
- Source code and caffe model attached to the paper
- util
- caffemodel2pth.py
- Export the network parameters from caffemodel to pytorch pth format
- normalize.py
- Function of iris image normalization.
- normalize_tool.py
- Tool for iris normalization.
- Left click to mark, right click to draw a circle (at least 3 points),'q' key to confirm, other keys to cancel
- Iris first, pupil rear
- segment.py
- Iris image segmentation
- caffemodel2pth.py
- enroll_dataset.py
- Register all images in the folder
- enroll_single.py
- Register single image in the folder
- evaluation.py
- Evaluation
- match.py
- Match
- verify.py
- Identify
- Compare the extracted mat file with all mat files in the folder