  Iris recognition of living individuals is a mature biometric modality that
has been adopted globally from governmental ID programs, border crossing, voter
registration and de-duplication, to unlocking mobile phones. On the other hand,
the possibility of recognizing deceased subjects with their iris patterns has
emerged recently. In this paper, we present an end-to-end deep learning-based
method for postmortem iris segmentation and recognition with a special
visualization technique intended to support forensic human examiners in their
efforts. The proposed postmortem iris segmentation approach outperforms the
state of the art and in addition to iris annulus, as in case of classical iris
segmentation methods - detects abnormal regions caused by eye decomposition
processes, such as furrows or irregular specular highlights present on the
drying and wrinkling cornea. The method was trained and validated with data
acquired from 171 cadavers, kept in mortuary conditions, and tested on
subject-disjoint data acquired from 259 deceased subjects. To our knowledge,
this is the largest corpus of data used in postmortem iris recognition research
to date. The source code of the proposed method are offered with the paper. The
test data will be available through the National Archive of Criminal Justice
Data (NACJD) archives.
