Robust Convolutional Neural Network based on UNet for Iris Segmentation

Abstract

Nowadays, the iris recognition system is one of the most widely used and most accurate biometric systems. The iris segmentation is the most crucial stage of iris recognition system. The accurate iris segmentation can improve the efficiency of iris recognition. The main objective of iris segmentation is to obtain the iris area. Recently, the iris segmentation methods based on convolutional neural networks (CNNs) have been grown, and they have improved the accuracy greatly. Nevertheless, their accuracy is decreased by low-quality images captured in uncontrolled conditions. Therefore, the existing methods cannot segment low-quality images precisely. To overcome the challenge, this paper proposes a robust convolutional neural network (R-Net) inspired by UNet for iris segmentation. R-Net is divided into two parts: encoder and decoder. In this network, several layers are added to ResNet-34, and used in the encoder path. In the decoder path, four convolutions are applied at each level. Both help to obtain suitable feature maps and increase the network accuracy. The proposed network has been tested on four datasets: UBIRIS v2 (UBIRIS), CASIA iris v4.0 (CASIA) distance, CASIA interval, and IIT Delhi v1.0 (IITD). UBIRIS is a dataset that is used for low-quality images. The error rate (NICE1) of proposed network is 0.0055 on UBIRIS, 0.0105 on CASIA interval, 0.0043 on CASIA distance, and 0.0154 on IITD. Results show better performance of the proposed network compared to other methods.