这篇综述把物理遮挡、传感器级和后期处理这三类人脸去识别方法都串起来了，想入行或找方案的读这篇就够了。
该综述首次统一覆盖物理域（可穿戴设备）、传感器域（集成隐私机制）和数字域（像素/外观级修改）的人脸去识别方法。数字域方法包括早期像素级修改和近期基于生成模型的保属性匿名化。传感器域方法在图像采集时嵌入隐私保护，弥合传感系统与下游视觉算法的鸿沟。物理域方法通过穿戴配件在现实环境中隐藏身份信息。现有评估协议缺乏标准化，该综述呼吁建立综合性基准。
Face De-Identification: A Domain-Centric Survey from Capture to Processing
Face de-identification (De-ID) aims to remove or conceal personally identifiable facial features in images or videos to prevent identity recognition while preserving utility for downstream tasks. With the rising emphasis on data privacy and responsible AI, face De-ID has emerged as an active research area spanning computer vision and privacy-preserving communities. Early approaches, and many contemporary ones, operate in the digital domain by modifying pixel-level or appearance-level features through post-capture processing. Recent advances extend face De-ID beyond post-processing by integrating privacy mechanisms directly into sensors during image acquisition, bridging sensing systems and downstream vision algorithms. In parallel, physical-domain methods explore wearable accessories and materials that conceal identity information in real-world environments prior to capture. In this survey, we present the first unified overview that spans the full data acquisition pipeline, encompassing the physical, sensor, and digital domains. Through this domain-centric lens, we systematically analyze current methodologies, technical progress, and the distinct challenges inherent to each stage. We then review and organize existing evaluation protocols, examining current practices and highlighting the critical need for standardized, comprehensive benchmarks. Finally, we identify key open problems and outline emerging research directions to guide future work in this rapidly evolving field. To support ongoing research, we maintain a project page that organizes relevant literature with collected datasets and open source code: https://github.com/CV-AC/Awesome-FaceDe-ID.