Abstract Recent advancements in face recognition (FR) technology in surveillance systems make it possible to monitor a person as they move around. FR gathers a lot of information depending on the quantity and data sources. The most severe privacy concern with FR technology is its use to identify people in real-time public monitoring applications or via an aggregation of datasets without their consent. Due to the importance of private data leakage in the FR environment, academia and business have given it a lot of attention, leading to the creation of several research initiatives meant to solve the corresponding challenges. As a result, this study aims to look at privacy-preserving face recognition (PPFR) methods. We propose a detailed and systematic study of the PPFR based on our suggested six-level framework. Along with all the levels, more emphasis is given to the processing of face images as it is more crucial for FR technology. We explore the privacy leakage issues and offer an up-to-date and thorough summary of current research trends in the FR system from six perspectives. We also encourage additional research initiatives in this promising area for further investigation.

摘要 监控系统中人脸识别 (FR) 技术的最新进展使得监控人员的移动成为可能。 FR 根据数量和数据源收集大量信息。 FR 技术最严重的隐私问题是它在实时公共监控应用程序中或在未经同意的情况下通过数据集聚合来识别人员身份。由于隐私数据泄露在 FR 环境中的重要性，学术界和企业界都给予了很多关注，从而催生了一些旨在解决相应挑战的研究计划。因此，本研究旨在研究保护隐私的人脸识别（PPFR）方法。我们建议根据我们建议的六级框架对 PPFR 进行详细而系统的研究。除了所有级别之外，还更加重视人脸图像的处理，因为它对于 FR 技术更为重要。我们探讨了隐私泄露问题，并从六个角度对当前FR系统的研究趋势进行了最新、全面的总结。我们还鼓励在这个有前景的领域开展更多研究活动，以进行进一步调查。