基于随机化和局部特征学习的隐私保护面部识别方法
Yanhua Huang1, Zhendong Wu1, Juan Chen1
1School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, China.
Journal of imaging
|March 27, 2024
概括
这项研究引入了一种新的保护隐私的面部识别方法. 它使用随机卷积和自学批量正常化对面图像进行混,在保护个人隐私的同时达到99%以上的准确性.
科学领域:
- 计算机科学 计算机科学
- 生物识别信息 生物识别信息
- 网络安全 网络安全
背景情况:
- 个人隐私保护至关重要,特别是在面部识别应用中.
- 像加密或扰动这样的传统方法需要图像恢复才能识别.
- 现有的方法在平衡隐私和识别准确性方面面临挑战.
研究的目的:
- 开发一种新的保护隐私的面部识别方法.
- 为了实现高识别精度,同时有效地消除敏感的面部信息.
- 确保隐私保护的可撤销性和不可逆转性.
主要方法:
- 一种新的保护隐私的面部识别方法,结合随机卷积和自学批量规范化.
- 一代的隐私保护混的面部图像与高度的模糊性.
- 在服务器上直接识别编码的图像,而无需图像恢复.
主要成果:
- 拟议的方法实现了与正常面部图像相似的识别精度,在多个数据集上超过99%.
- 视觉信息的消除几乎完成,接近加密效应.
- 在准确性和信息消除方面,优于现有的保护隐私的面部识别技术.
结论:
- 这种新的方法有效地保护了面部隐私,同时保持了高的识别性能.
- 它为保护隐私的面部识别系统提供了强大的解决方案.
- 该技术确保隐私保护既可撤销又不可逆转.
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