# PFSegIris: Precise and Fast Segmentation Algorithm for Multi-Source Heterogeneous Iris
## 3 Experiments and Analysis
### Iris Datasets and Data Augmentation  
A total of seven different iris datasets were used in the experiments, four of which were taken in near-infrared and three in visible light, containing images of iris regions of different sizes under various shooting conditions. In these seven datasets, images of the left and right irises from the same subject were classified into two classes, which meant that the left and right irises of the same subject could be randomly used with equal probability. The samples of seven iris datasets are shown in Figure 7. The use of seven iris datasets is described below. 1. The JLU-6.0 iris dataset is characterized by large iris regions with various quality iris images, taken at close range and in near-infrared light. We selected 100 classes for the experiments. After data augmentation, the total number of images used was 10,006, of which the training set contained 7004 images, the validation set contained 2001 images, and the test set contained 1001 images. 2. The JLU-7.0 iris dataset has smaller iris regions compared with JLU-6.0 iris dataset and also has various quality iris images taken at close range and in near-infrared light. In all, 78 classes were selected for experiments, and after data augmentation, the total number of images was 986 for the test set entirely. 3. The iris images from the CASIA-Iris-Interval-v4 iris dataset are of high quality, with detailed features of the iris clearly visible, taken at close range and in near-infrared light. Overall, 120 classes were selected. A total number of 960 images were used after augmentation, all of which were used for testing. 4. The CASIA-Iris-Lamp-v4 iris dataset intentionally introduced ambient lighting variations, acquiring iris images with non-linear deformation. Since most of irises are from Orientals, the iris regions are heavily obscured by the upper and lower eyelids and eyelashes. Images were taken at close range and in visible light. We chose 120 classes for experiments. There were totally 9600 images after data augmentation, of which  
Figure 6: Iris mask localization image and its corresponding original iris localization image: (**a**) mask localization image; (**b**) original iris localization image. the training set contained 6720 images, the validation set contained 1920 images, and the test set contained 960 images. 5. The UBIRIS.V2 iris dataset introduced more noises such as defocus blur, contact lens occlusion, and hair occlusion, taken at long distance and in visible light. From this dataset, 100 classes were selected. After data augmentation, 1000 images were used for the test set. 6. The main feature of the MICHE-I iris dataset is that it was acquired by mobile devices, containing more realistic noises. Most of images were obtained under unconstrained conditions, closer to realistic situations. We used 75 classes and 9360 images totally after augmentation. The training set contained 6552 images, the validation set contained 1872 images, and the test set contained 936 images. 7. The Mmu2 iris dataset has iris images from people of different races and ages in Asia, the Middle East, Africa, and Europe; with pupil spots, hair, eyelashes, eyebrows, and other noises; taken at close range and in near-infrared light. After augmentation, 974 iris images were all used for testing with 100 classes. In order to avoid overfitting of the proposed PFSeglris and improve the generalization ability of the model, we performed data augmentation on the iris datasets used for experiments. Targeting the accurate segmentation of multi-source heterogeneous iris images and considering the actual application scenarios of iris recognition, the following data augmentation methods were designed and used. 1. Randomly flip horizontally with the probability of 0.5. 2. Randomly rotate within a certain angle with the probability of 0.8. The rotation angle we used was a maximum of 30 degrees to the left and right. 3. Randomly resize with the probability of 0.8. Four different scales of 0.5, 0.75, 1.25, and 1.5 times of the original images were used.