  Iris segmentation and localization in non-cooperative environment is
challenging due to illumination variations, long distances, moving subjects and
limited user cooperation, etc. Traditional methods often suffer from poor
performance when confronted with iris images captured in these conditions.
Recent studies have shown that deep learning methods could achieve impressive
performance on iris segmentation task. In addition, as iris is defined as an
annular region between pupil and sclera, geometric constraints could be imposed
to help locating the iris more accurately and improve the segmentation results.
In this paper, we propose a deep multi-task learning framework, named as
IrisParseNet, to exploit the inherent correlations between pupil, iris and
sclera to boost up the performance of iris segmentation and localization in a
unified model. In particular, IrisParseNet firstly applies a Fully
Convolutional Encoder-Decoder Attention Network to simultaneously estimate
pupil center, iris segmentation mask and iris inner/outer boundary. Then, an
effective post-processing method is adopted for iris inner/outer circle
localization.To train and evaluate the proposed method, we manually label three
challenging iris datasets, namely CASIA-Iris-Distance, UBIRIS.v2, and MICHE-I,
which cover various types of noises. Extensive experiments are conducted on
these newly annotated datasets, and results show that our method outperforms
state-of-the-art methods on various benchmarks. All the ground-truth
annotations, annotation codes and evaluation protocols are publicly available
at https://github.com/xiamenwcy/IrisParseNet.
