Iris Presentation Attack Detection: Where Are We Now?

• URL: http://arxiv.org/abs/2006.13252v2
• Date: Thu, 16 Jul 2020 19:49:43 GMT
• Title: Iris Presentation Attack Detection: Where Are We Now?
• Authors: Aidan Boyd, Zhaoyuan Fang, Adam Czajka, Kevin W. Bowyer
• Abstract summary: This work presents an overview of the most important advances in the area of iris presentation attack detection. Recent literature can be broken into three categories: traditional "hand-crafted" feature extraction and classification, deep learning-based solutions, and hybrid approaches.
• Score: 14.671313104089906
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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