  Most iris recognition pipelines involve three stages: segmenting into
iris/non-iris pixels, normalization the iris region to a fixed area, and
extracting relevant features for comparison. Given recent advances in deep
learning it is prudent to ask which stages are required for accurate iris
recognition. Lojez et al. (IWBF 2019) recently concluded that the segmentation
stage is still crucial for good accuracy.We ask if normalization is beneficial?
Towards answering this question, we develop a new iris recognition system
called ThirdEye based on triplet convolutional neural networks (Schroff et al.,
ICCV 2015). ThirdEye directly uses segmented images without normalization. We
observe equal error rates of 1.32%, 9.20%, and 0.59% on the ND-0405, UbirisV2,
and IITD datasets respectively. For IITD, the most constrained dataset, this
improves on the best prior work. However, for ND-0405 and UbirisV2,our equal
error rate is slightly worse than prior systems. Our concluding hypothesis is
that normalization is more important for less constrained environments.
