  Post-mortem iris recognition can offer an additional forensic method of
personal identification. However, in contrary to already well-established human
examination of fingerprints, making iris recognition human-interpretable is
harder, and therefore it has never been applied in forensic proceedings. There
is no strong consensus among biometric experts which iris features, especially
those in iris images acquired post-mortem, are the most important for human
experts solving an iris recognition task. This paper explores two ways of
broadening this knowledge: (a) with an eye tracker, the salient features used
by humans comparing iris images on a screen are extracted, and (b)
class-activation maps produced by the convolutional neural network solving the
iris recognition task are analyzed. Both humans and deep learning-based
solutions were examined with the same set of iris image pairs. This made it
possible to compare the attention maps and conclude that (a) deep
learning-based method can offer human-interpretable decisions backed by visual
explanations pointing a human examiner to salient regions, and (b) in many
cases humans and a machine used different features, what means that a deep
learning-based method can offer a complementary support to human experts. This
paper offers the first known to us human-interpretable comparison of
machine-based and human-based post-mortem iris recognition, and the trained
models annotating salient iris image regions.
