面部去识别：从采集到处理的以领域为中心的综述
Face De-Identification: A Domain-Centric Survey from Capture to Processing
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中文总结 AI 辅助
该综述以领域为中心，涵盖面部去识别从采集到处理的完整数据管道，系统分析各阶段方法、进展与挑战，回顾评估协议，确定开放问题与新兴方向，为该领域未来工作提供指导。
中文摘要 AI 辅助
面部去识别旨在在图像或视频中去除或隐藏可识别个人身份的面部特征，以防止身份识别，同时保留对下游任务的实用性。随着对数据隐私和负责任人工智能的日益重视，面部去识别已成为一个活跃的研究领域，跨越计算机视觉和隐私保护社区。早期方法以及许多当代方法通过捕获后处理修改像素级或外观级特征在数字领域操作。最近的进展通过在图像采集期间将隐私机制直接集成到传感器中，将面部去识别扩展到后处理之外，连接传感系统和下游视觉算法。同时，物理领域方法探索可穿戴配件和材料，在捕获之前在现实世界环境中隐藏身份信息。在本综述中，我们首次对面部去识别进行统一概述，涵盖物理、传感器和数字领域的完整数据采集管道。通过以领域为中心的视角，我们系统地分析了当前方法、技术进展以及每个阶段固有的独特挑战。我们还回顾和整理了现有的评估协议，审视当前实践并强调对标准化、全面基准的迫切需求。最后，我们确定了关键的开放问题并概述了新兴研究方向，以指导这一快速发展领域的未来工作。为支持正在进行的研究，我们维护了一个项目页面，用收集的数据集和开源代码整理相关文献：此https URL。
英文摘要
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.
发表机构
- ELLIS Institute Finland（芬兰埃利斯研究所）
- Center for Machine Vision and Signal Analysis, University of Oulu（奥卢大学机器视觉与信号分析中心）
机构由 AI 辅助整理，请以论文原文为准。
