  In this survey, we provide a comprehensive review of more than 200 papers,
technical reports, and GitHub repositories published over the last 10 years on
the recent developments of deep learning techniques for iris recognition,
covering broad topics on algorithm designs, open-source tools, open challenges,
and emerging research. First, we conduct a comprehensive analysis of deep
learning techniques developed for two main sub-tasks in iris biometrics:
segmentation and recognition. Second, we focus on deep learning techniques for
the robustness of iris recognition systems against presentation attacks and via
human-machine pairing. Third, we delve deep into deep learning techniques for
forensic application, especially in post-mortem iris recognition. Fourth, we
review open-source resources and tools in deep learning techniques for iris
recognition. Finally, we highlight the technical challenges, emerging research
trends, and outlook for the future of deep learning in iris recognition.
