Iris segmentation using feature channel optimization for noisy environments
Tensorflow 1.4.0
Keras 2.2.0
Python 3.5
R stands for recall rate, P stands for precision, and F-measure is a combination of the two. (unit: %)
| Dataset | F | R | P | Error rate(%) |
|---|---|---|---|---|
| CASIA | 98.11 | 97.96 | 98.27 | 0.81 |
| IITD | 97.84 | 97.78 | 97.91 | 0.98 |
We use CASIA V4.0 Interval (Abbr. CASIA) dataset, and the IIT Delhi v1.0 (Abbr. IITD) dataset. We provide a noisy dataset with Gaussian noise. When training the model, we use a '.npy' file of the dataset.
The weights we provide are the training results for Gaussian noise. In 'Model/CAV/gaussianNoise/model_new.hdf5' or 'Model/IITD/gaussianNoise/model_new.hdf5'
- To test the model, you can run
python test_predict.py
In 'Model/CAV', you can see the segmentation results.
- In order to measure the performance of the model with the RPF metric, you can run
python error_RPF.py
- To train the model, you can run
python model.py
The training results will be written to Model/CAV
Please cite this paper if you think it is useful for you.
Title: Iris segmentation using feature channel optimization for noisy environments
Author: Kangli Hao · Guorui Feng · Yanli Ren · Xinpeng Zhang