  Post-mortem iris recognition is an emerging application of iris-based human
identification in a forensic setup, able to correctly identify deceased
subjects even three weeks post-mortem. This technique thus is considered as an
important component of future forensic toolkits. The current advancements in
this field are seriously slowed down by exceptionally difficult data
collection, which can happen in mortuary conditions, at crime scenes, or in
``body farm'' facilities. This paper makes a novel contribution to facilitate
progress in post-mortem iris recognition by offering a conditional
StyleGAN-based iris synthesis model, trained on the largest-available dataset
of post-mortem iris samples acquired from more than 350 subjects, generating --
through appropriate exploration of StyleGAN latent space -- multiple
within-class (same identity) and between-class (different new identities)
post-mortem iris images, compliant with ISO/IEC 29794-6, and with decomposition
deformations controlled by the requested PMI (post mortem interval). Besides an
obvious application to enhance the existing, very sparse, post-mortem iris
datasets to advance -- among others -- iris presentation attack endeavors, we
anticipate it may be useful to generate samples that would expose professional
forensic human examiners to never-seen-before deformations for various PMIs,
increasing their training effectiveness. The source codes and model weights are
made available with the paper.
