  We first, introduce a deep learning based framework named as DeepIrisNet2 for
visible spectrum and NIR Iris representation. The framework can work without
classical iris normalization step or very accurate iris segmentation; allowing
to work under non-ideal situation. The framework contains spatial transformer
layers to handle deformation and supervision branches after certain
intermediate layers to mitigate overfitting. In addition, we present a dual CNN
iris segmentation pipeline comprising of a iris/pupil bounding boxes detection
network and a semantic pixel-wise segmentation network. Furthermore, to get
compact templates, we present a strategy to generate binary iris codes using
DeepIrisNet2. Since, no ground truth dataset are available for CNN training for
iris segmentation, We build large scale hand labeled datasets and make them
public; i) iris, pupil bounding boxes, ii) labeled iris texture. The networks
are evaluated on challenging ND-IRIS-0405, UBIRIS.v2, MICHE-I, and CASIA v4
Interval datasets. Proposed approach significantly improves the
state-of-the-art and achieve outstanding performance surpassing all previous
methods.
