  Forensic iris recognition, as opposed to live iris recognition, is an
emerging research area that leverages the discriminative power of iris
biometrics to aid human examiners in their efforts to identify deceased
persons. As a machine learning-based technique in a predominantly
human-controlled task, forensic recognition serves as "back-up" to human
expertise in the task of post-mortem identification. As such, the machine
learning model must be (a) interpretable, and (b) post-mortem-specific, to
account for changes in decaying eye tissue. In this work, we propose a method
that satisfies both requirements, and that approaches the creation of a
post-mortem-specific feature extractor in a novel way employing human
perception. We first train a deep learning-based feature detector on
post-mortem iris images, using annotations of image regions highlighted by
humans as salient for their decision making. In effect, the method learns
interpretable features directly from humans, rather than purely data-driven
features. Second, regional iris codes (again, with human-driven filtering
kernels) are used to pair detected iris patches, which are translated into
pairwise, patch-based comparison scores. In this way, our method presents human
examiners with human-understandable visual cues in order to justify the
identification decision and corresponding confidence score. When tested on a
dataset of post-mortem iris images collected from 259 deceased subjects, the
proposed method places among the three best iris matchers, demonstrating better
results than the commercial (non-human-interpretable) VeriEye approach. We
propose a unique post-mortem iris recognition method trained with human
saliency to give fully-interpretable comparison outcomes for use in the context
of forensic examination, achieving state-of-the-art recognition performance.
