  Subject matching performance in iris biometrics is contingent upon fast,
high-quality iris segmentation. In many cases, iris biometrics acquisition
equipment takes a number of images in sequence and combines the segmentation
and matching results for each image to strengthen the result. To date,
segmentation has occurred in 2D, operating on each image individually. But such
methodologies, while powerful, do not take advantage of potential gains in
performance afforded by treating sequential images as volumetric data. As a
first step in this direction, we apply the Flexible Learning-Free
Reconstructoin of Neural Volumes (FLoRIN) framework, an open source
segmentation and reconstruction framework originally designed for neural
microscopy volumes, to volumetric segmentation of iris videos. Further, we
introduce a novel dataset of near-infrared iris videos, in which each subject's
pupil rapidly changes size due to visible-light stimuli, as a test bed for
FLoRIN. We compare the matching performance for iris masks generated by FLoRIN,
deep-learning-based (SegNet), and Daugman's (OSIRIS) iris segmentation
approaches. We show that by incorporating volumetric information, FLoRIN
achieves a factor of 3.6 to an order of magnitude increase in throughput with
only a minor drop in subject matching performance. We also demonstrate that
FLoRIN-based iris segmentation maintains this speedup on low-resource hardware,
making it suitable for embedded biometrics systems.
