Iris Presentation Attack Detection: Where Are We Now?

TL;DR

This paper reviews recent advances in iris presentation attack detection, highlighting datasets, methodologies, and challenges, and discusses future research directions in the field.

Contribution

It provides a comprehensive overview of recent developments, categorizes approaches, and discusses datasets and future challenges in iris presentation attack detection.

Findings

Deep learning approaches are increasingly prominent.

Hybrid methods combining traditional and deep learning are emerging.

The task remains challenging despite recent progress.

Abstract

As the popularity of iris recognition systems increases, the importance of effective security measures against presentation attacks becomes paramount. This work presents an overview of the most important advances in the area of iris presentation attack detection published in recent two years. Newly-released, publicly-available datasets for development and evaluation of iris presentation attack detection are discussed. Recent literature can be seen to be broken into three categories: traditional "hand-crafted" feature extraction and classification, deep learning-based solutions, and hybrid approaches fusing both methodologies. Conclusions of modern approaches underscore the difficulty of this task. Finally, commentary on possible directions for future research is provided.