  Iris segmentation is a deterministic part of the iris recognition system.
Unreliable segmentation of iris regions especially the limbic area is still the
bottleneck problem, which impedes more accurate recognition. To make further
efforts on accurate and reliable iris segmentation, we propose a bilateral
self-attention module and design Bilateral Transformer (BiTrans) with
hierarchical architecture by exploring spatial and visual relationships. The
bilateral self-attention module adopts a spatial branch to capture spatial
contextual information without resolution reduction and a visual branch with a
large receptive field to extract the visual contextual features. BiTrans
actively applies convolutional projections and cross-attention to improve
spatial perception and hierarchical feature fusion. Besides, Iris Segmentation
Uncertainty Learning is developed to learn the uncertainty map according to
prediction discrepancy. With the estimated uncertainty, a weighting scheme and
a regularization term are designed to reduce predictive uncertainty. More
importantly, the uncertainty estimate reflects the reliability of the
segmentation predictions. Experimental results on three publicly available
databases demonstrate that the proposed approach achieves better segmentation
performance using 20% FLOPs of the SOTA IrisParseNet.
