Iris recognition include tradition algorithm and deep learning.

• Code
  • script
  • tradition
  • CNN_feature
  • CNN classifier
• Dataset
  • CASIA-Iris Version 1.0
  • CASIA-Iris-Thousand
• Algorithm
  • 1.Tradition Algorithm
    • Preprocessing
    • USIT v2.2
    • Gabor Feature Extraction
    • Distance Based Match
    • Machine Learning Predict
  • 2.CNN Feature Extraction
  • 3.CNN Classification

code dir

• script
  • GetList.py Get the image path list and label list,then split to train set and test set.
  • GetPic.py Get and save the iris pictures after segmentation and ROI pictures after normalization and enhancement.
  • GetVector.py Get the feature vector list of train and test according gabor filter and save as csv.
  • copy_pic.py Copy all the picture into one dir.

• tradition
  • Segmentation.py Segment the iris ring with hough transform and canny edge detection.
  • Normalization.py Flat the circular ring into rectangle ROI.
  • Enhancement.py Enhancement the ROI after normalization.
  • Gabor.py Feature extraction with gabor filter.
  • Matching.py Match with cityblock、euclidean and cosine distance.
  • Evaluation.py Evaluate the accuracy rate with each distance.
  • iris_demo2.py Run and get the results.

• CNN_feature
  • inception_utils.py Inception utils from geogle tensorflow slim.
  • inceptionv4.py Inceptionv4 from geogle tensorflow slim.
  • resnet_utils.py Resnet_utils from geogle tensorflow slim.
  • ResNet.py Resnet from geogle tensorflow slim.
  • DenseNet.py DenseNet from geogle tensorflow slim.
  • cnn_feature.py CNN feature extraction with inceptionv4、resnet and densenet.
  • iris_demo1.py Run and get the results.

• CNN_classifier
  • utils.py Preprocess.
  • DenseNet.py Densenet.
  • train.py Train with a mini-densenet.
  • eval.py Eval and get the results.

Include 108 classes, each class has 7 images, three of them for train and the other for test.

Include 1000 classes, each class has 20 images,half are left eyes and half right. We random select 70% of them for train, and 30% for test.All the results are based on this dataset.

We use hough transform and canny edge detection to segment the iris,and then unfold the ring between the outer circle and inner circle into a rectangle of size 64*512. After normalization,we did local image equalization as Li Ma's paper.Finally,we use gabor filter to extract the feature vector from the ROI.

We also recommend an open-source software,USIT v2.2,from the University of Salzburg to complete the preprocessing. Github You just need to clone the git and install opencv and boost,and then release wahet.cpp.

usage:
for a single image

test.exe -i D:\study\iris\CASIA-Iris-Thousand\000\L\S5000L00.jpg -o texture.png -s 256 64 -e

for a batch images

test.exe -i D:\study\iris\CASIA\origin\train\*.jpg  -o D:/study/iris/CASIA/enhance_512/train/?1.jpg  -s 512 64 -e

If you don't need enhancement,you just need delete "-e".
If you need the segmentation,you just need add "-sr D:/study/iris/CASIA/seg/train/?1.jpg"

We use gabor filter to complete the feature extraction.

We use cityblock distance,euclidean distance and cosine distance to match,and results respectively are 88.19%,84.95% and 85.42%.

We use pca to reduce the dimension and then use KNN and SVM to train and predict.When the dimension reduce to 380, the result is the best,90.2% for KNN and 90.7% for SVM.

We use InceptionV4,ResNet-101,Densenet121 to extract feature from the ROI after enhancement.When inceptionV4 at "Mixed6a",ResNet with block[3,4,9] and DenseNet with block[6,12,3] and then use pca reduce the dimension to 580 for SVM to get the best results 95.8%,96.4% and 97.1%.We also append avgrage pooling after convolution to avoid MemoryError.

We also use a mini densenet with 40 layers to train a model.Dataset are the ROI of the CASIA-Iris-Thousand.However,it doesn't work for the limitation of the dataset and lead to over-fit.