Third Place in NIR Iris Challenge Evaluation in Non-cooperative Environments: Segmentation and Localization (NIR-ISL 2021)

Pipleline

Requirement

python 3
pytorch
torchvision
albumentations
thop

Model Evaluation

Complexity

For the model complexity, the number of model parameters, the size of model memory (measured in MB), the amount of floating point operations (measured in FLOPs w.r.t the input of 640×480 pixels), and the average running speed (w.r.t the input of 640×480 pixels and GPU/CPU computing devices) are evaluated.

Model size(MB)	params(M)	Flops(G)	memory(MB)	speed(FPS)
282.97	43.687	55.521	6807	17.489

Test on a single TITAN V

Performance

For iris segmentation, the evaluation measures are type-I (E1) error rates (please refer to http://nice1.di.ubi.pt/evaluation.htm for more details), as used in the Noisy Iris Challenge Evaluation - Part I (NICE.I).
For iris localization, the evaluation measures are dice index when filling the iris boundaries into iris boundary masks, and Hausdorff distance between the predicted iris boundaries and the corresponding ground truth boundaries. (please refer to https://warwick.ac.uk/fac/sci/dcs/research/tia/glascontest/evaluation/ for more details).

Dataset	E1(%)	Dice	Hausdorff
CASIA-Iris-M1	0.7973	0.9769	0.0047
CASIA-Iris-Africa	0.4138	0.9617	0.0070
CASIA-Iris-Distance	0.3991	0.9662	0.0062
CASIA-Iris-Complex-Off-angle	0.3394	0.9742	0.0066
CASIA-Iris-Complex-Occlusion	0.4574	0.9648	0.0088

The table shows the performance of model (password：x3zm)

Getting Start

Prepare dataset of NIR-ISL2021

Please refer to mydataset.md for details to prepare dataset.

Testing submitted model

1. Download models to './example' (password：x3zm)
2. run python ./example/model_performance.py --dataset xxx --ckpath .../example/checkpoints/
   --dataset Support dataset in [CASIA-Iris-Africa, CASIA-distance, Occlusion, Off_angle, CASIA-Iris-Mobile-V1.0]
   --ckpath The folder path, which saves the models to get iris_mask, inner_boundary and outer_boundary respectively

Training your model

please refer to train.py default training parameters.

1. change experiment_name and dataset_name in train.py as you need
2. run python ./train.py -e 64 -b 16 -l 0.02 --log my-experiment.log

Testing your model

python ./test.py \
--dataset CASIA-Iris-Africa\
--ckpath .../.../

Contributor

EyeCool Research Group, Applied Mathematics, College of Sciences, Northeastern University,

Shenyang, Liaoning, P. R. China.

Only for research.