论文标题
虹膜认可的深度学习：一项调查
Deep Learning for Iris Recognition: A Survey
论文作者
论文摘要
在这项调查中，我们对过去十年中发表的200多篇论文，技术报告和GitHub存储库进行了全面综述，内容涉及有关IRIS认可的深度学习技术的最新发展，涵盖了有关算法设计，开源工具，开放挑战和新兴研究的广泛主题。首先，我们对针对虹膜生物识别技术的两个主要子任务开发的深度学习技术进行了全面分析：分割和识别。其次，我们专注于深度学习技术，以抗虹膜识别系统的鲁棒性，以防止演示攻击和通过人机配对。第三，我们深入研究法医应用深度学习技术，尤其是在验尸后识别中。第四，我们在深度学习技术中审查了虹膜识别的开源资源和工具。最后，我们强调了技术挑战，新兴的研究趋势以及虹膜认识深度学习的未来的前景。
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.
