A Robust Iris Segmentation Using Fully Convolutional Network with Dilated Convolutions

Abstract

Iris segmentation is a critical part in iris recognition systems. It segments the acquired image into iris and non-iris parts. It is the foundation of subsequent processing. The errors in this stage are propagated to subsequent processing stages, which will affecting the recognition rate of the whole system. A majority of iris segmentation algorithms require a significant amount of user cooperation during image acquisition process to provide good segmentation performance. However, the quality of iris images can not be guaranteed. When an iris image is acquired under non-ideal conditions (e.g., bad illumination, uncooperative subject, occluded iris, etc.), segmentation becomes a challenging task. In this paper, we present a more robust iris segmentation method using fully convolutional network (FCN) with dilated convolutions. We reduce the downsampling factor of the FCN model, and use the dilated convolutions to extract the more global features, which makes our method better at dealing with details. Moreover, our model supports end to end prediction, it does not need any pre-processing, such as adjusting the image to a fixed size. We used three datasets for training and testing, including CASIA-iris-interval-v4, UBIRIS v2 and IITD Delhi datasets. Experiments show that our model greatly reduced the error rate of the current state-of-the-arts by 79%, 84% and 79% on the CASIA-iris-interval-v4, IITD Delhi and UBIRIS v2 datasets respectively.