  The extraction of consistent and identifiable features from an image of the
human iris is known as iris recognition. Identifying which pixels belong to the
iris, known as segmentation, is the first stage of iris recognition. Errors in
segmentation propagate to later stages. Current segmentation approaches are
tuned to specific environments. We propose using a convolution neural network
for iris segmentation. Our algorithm is accurate when trained in a single
environment and tested in multiple environments. Our network builds on the Mask
R-CNN framework (He et al., ICCV 2017). Our approach segments faster than
previous approaches including the Mask R-CNN network. Our network is accurate
when trained on a single environment and tested with a different sensors
(either visible light or near-infrared). Its accuracy degrades when trained
with a visible light sensor and tested with a near-infrared sensor (and vice
versa). A small amount of retraining of the visible light model (using a few
samples from a near-infrared dataset) yields a tuned network accurate in both
settings. For training and testing, this work uses the Casia v4 Interval, Notre
Dame 0405, Ubiris v2, and IITD datasets.
