强大的跨域伪标签和对比学习用于无监督域调整 NIR-VIS 面部识别
概括
这项研究引入了一种新的强大的跨领域伪标签和对比学习 (RPC) 网络,用于无监督的近红外和可见 (NIR-VIS) 面部识别. 该RPC网络在伪标签分配方面达到99%以上的准确性,使大规模的高效人脸识别系统成为可能.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 生物识别信息 生物识别信息
背景情况:
- 近红外和可见 (NIR-VIS) 面部识别对于24小时的安全至关重要,特别是在低光条件下.
- 用于NIR-VIS识别的大型,异质面部数据集的手动注释是昂贵和耗时的,阻碍了现实世界的应用.
- 无监督域调整为克服NIR-VIS人脸识别中的注释限制提供了一个有希望的方法.
研究的目的:
- 为NIR-VIS人脸识别开发一个无监督域调整方法.
- 消除在大型NIR-VIS人脸识别系统中需要手动识别标签的需要.
- 提出一种新的网络架构,有效地处理NIR和VIS面部图像之间的域差异.
主要方法:
- 提出了一个新的强大的跨领域伪标签和对比学习 (RPC) 网络.
- 关键组件包括基于NIR集群的伪标签共享 (NPS) 来生成可靠的伪标签,域特定集群对比学习 (DCL) 来学习区分域内表示,以及域间集群对比学习 (ICL) 来进行强大的,独立于域的特征学习.
- 该NPS组件利用NIR集群与VIS领域共享标签知识,而DCL和ICL则通过对比学习策略来改进表示.
主要成果:
- 拟议的RPC网络在伪标签分配方面实现了99%以上的准确性.
- 在四个主流NIR-VIS数据集上的实验结果表明了RPC网络的先进性能.
- 该方法成功地学习了无需手动注释的强大且独立于域的表示.
结论:
- 该RPC网络有效地解决了在NIR-VIS人脸识别中无监督域调整的挑战.
- 拟议的方法大大减少了对手册注释的依赖,为可扩展的现实应用铺平了道路.
- 这项工作推进了NIR-VIS面部识别的最新技术,通过在不同的照明条件下实现准确和高效的识别.
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