  This paper presents a method for segmenting iris images obtained from the
deceased subjects, by training a deep convolutional neural network (DCNN)
designed for the purpose of semantic segmentation. Post-mortem iris recognition
has recently emerged as an alternative, or additional, method useful in
forensic analysis. At the same time it poses many new challenges from the
technological standpoint, one of them being the image segmentation stage, which
has proven difficult to be reliably executed by conventional iris recognition
methods. Our approach is based on the SegNet architecture, fine-tuned with
1,300 manually segmented post-mortem iris images taken from the
Warsaw-BioBase-Post-Mortem-Iris v1.0 database. The experiments presented in
this paper show that this data-driven solution is able to learn specific
deformations present in post-mortem samples, which are missing from alive
irises, and offers a considerable improvement over the state-of-the-art,
conventional segmentation algorithm (OSIRIS): the Intersection over Union (IoU)
metric was improved from 73.6% (for OSIRIS) to 83% (for DCNN-based presented in
this paper) averaged over subject-disjoint, multiple splits of the data into
train and test subsets. This paper offers the first known to us method of
automatic processing of post-mortem iris images. We offer source codes with the
trained DCNN that perform end-to-end segmentation of post-mortem iris images,
as described in this paper. Also, we offer binary masks corresponding to manual
segmentation of samples from Warsaw-BioBase-Post-Mortem-Iris v1.0 database to
facilitate development of alternative methods for post-mortem iris
segmentation.
