A Robust Iris Segmentation Scheme Based on Improved U-Net

Abstract

Iris segmentation plays an important role in the iris recognition system, and the accurate segmentation of iris can lay a good foundation for the follow-up work of iris recognition and can improve greatly the efficiency of iris recognition. We proposed four new feasible network schemes, and the best network model fully dilated convolution combining U-Net (FD-UNet) is obtained by training and testing on the same datasets. The FD-UNet uses dilated convolution instead of original convolution to extract more global features so that the details of images can be processed better. The proposed method is tested in the near-infrared illumination iris datasets (CASIA-iris-interval-v4.0 and ND-IRIS-0405) and the visible light illumination iris dataset (UBIRIS.v2). The f1 scores of our model on the CASIA-iris-interval-v4.0, ND-IRIS-0405, and UBIRIS.v2 datasets reached 97.36%, 96.74%, and 94.81%, respectively. The experimental results show that our network model improves the accuracy and reduces the error rate, which performs well on both near-infrared illumination and visible light illumination iris datasets with good robustness.

Keywords

• Iris recognition
• biometrics
• image segmentation
• convolutional neural network
• deep learning