UniNet

Towards More Accurate Iris Recognition Using Deeply Learned Spatially Corresponding Features

Project structure

This project is organized as follows:

dataset: contains the training data, validation data and test data.
reference: contains the reference materials.
snapshots: for saving model weights.
static: contains the pretrained model (MaskNet) and so on.
data.py: for constructing the data generator.
eval.py: for calculating TAR, FAR, EER.
loss.py: the implementation of the Extended Triplet Loss.
mask.py: for predicting masks for all the images in dataset using the pretrained MaskNet.
match.py: functions for the iris matching (e.g. Hanmming distance).
model.py: model class of the UniNet.
network.py: the implementation of the FeatNet and the MaskNet.
train.py: training file.

The moudle below is required for the project:

For conducting training, your dataset should be organized as follows:

Train.txt

.dataset/Train/person1_001 0 
.dataset/Train/person1_002 0
...
.dataset/Train/personN_001 N
...