  This paper proposes an end-to-end iris recognition method designed
specifically for post-mortem samples, and thus serving as a perfect application
for iris biometrics in forensics. To our knowledge, it is the first method
specific for verification of iris samples acquired after demise. We have
fine-tuned a convolutional neural network-based segmentation model with a large
set of diversified iris data (including post-mortem and diseased eyes), and
combined Gabor kernels with newly designed, iris-specific kernels learnt by
Siamese networks. The resulting method significantly outperforms the existing
off-the-shelf iris recognition methods (both academic and commercial) on the
newly collected database of post-mortem iris images and for all available time
horizons since death. We make all models and the method itself available along
with this paper.
