  This paper advances the state of the art in human examination of iris images
by (1) assessing the impact of different iris conditions in identity
verification, and (2) introducing an annotation step that improves the accuracy
of people's decisions. In a first experimental session, 114 subjects were asked
to decide if pairs of iris images depict the same eye (genuine pairs) or two
distinct eyes (impostor pairs). The image pairs sampled six conditions: (1)
easy for algorithms to classify, (2) difficult for algorithms to classify, (3)
large difference in pupil dilation, (4) disease-affected eyes, (5) identical
twins, and (6) post-mortem samples. In a second session, 85 of the 114 subjects
were asked to annotate matching and non-matching regions that supported their
decisions. Subjects were allowed to change their initial classification as a
result of the annotation process. Results suggest that: (a) people improve
their identity verification accuracy when asked to annotate matching and
non-matching regions between the pair of images, (b) images depicting the same
eye with large difference in pupil dilation were the most challenging to
subjects, but benefited well from the annotation-driven classification, (c)
humans performed better than iris recognition algorithms when verifying genuine
pairs of post-mortem and disease-affected eyes (i.e., samples showing
deformations that go beyond the distortions of a healthy iris due to pupil
dilation), and (d) annotation does not improve accuracy of analyzing images
from identical twins, which remain confusing for people.
