  One of the major challenges in ocular biometrics is the cross-spectral
scenario, i.e., how to match images acquired in different wavelengths
(typically visible (VIS) against near-infrared (NIR)). This article designs and
extensively evaluates cross-spectral ocular verification methods, for both the
closed and open-world settings, using well known deep learning representations
based on the iris and periocular regions. Using as inputs the bounding boxes of
non-normalized iris/periocular regions, we fine-tune Convolutional Neural
Network(CNN) models (based either on VGG16 or ResNet-50 architectures),
originally trained for face recognition. Based on the experiments carried out
in two publicly available cross-spectral ocular databases, we report results
for intra-spectral and cross-spectral scenarios, with the best performance
being observed when fusing ResNet-50 deep representations from both the
periocular and iris regions. When compared to the state-of-the-art, we observed
that the proposed solution consistently reduces the Equal Error Rate(EER)
values by 90% / 93% / 96% and 61% / 77% / 83% on the cross-spectral scenario
and in the PolyU Bi-spectral and Cross-eye-cross-spectral datasets. Lastly, we
evaluate the effect that the "deepness" factor of feature representations has
in recognition effectiveness, and - based on a subjective analysis of the most
problematic pairwise comparisons - we point out further directions for this
field of research.
