---
title: "人脸去身份识别：从采集到处理的领域中心综述"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.25926v1"
permalink: "https://feed.astralor.com/items/itm_b1e32faf941d1a5790a63ea9"
published: "2026-07-28T16:15:12.000Z"
score: 81
channel: "ai"
tags: ["人脸去身份识别","传感器","计算机视觉","负责任AI","隐私保护"]
---

# 人脸去身份识别：从采集到处理的领域中心综述

## AI 点评

> 这篇综述非常及时，随着全球隐私法规（如GDPR）趋严，人脸去身份识别成为刚需。它从传感器到算法的全链条视角，对产品经理和技术选型者特别有启发——比如在安防摄像头中直接集成传感器级去身份，能够大幅降低后续处理复杂度和合规风险。推荐所有涉及人脸数据处理的团队仔细阅读，尤其是其中的评估基准部分，有助于建立内部测试标准。

## 速览

本文首次统一概述了人脸去身份识别（De-ID）的全数据采集流水线，涵盖物理域、传感器域和数字域。通过领域中心视角，系统分析了各阶段的方法论、技术进展和独特挑战，并梳理了现有评估协议，强调标准化基准的迫切需求。最后指出关键开放问题与未来研究方向。配套项目页面汇总了相关文献、数据集和开源代码。

## 关键点

- 首次从采集到处理的全流程统一梳理人脸去身份识别技术，覆盖物理域、传感器域和数字域。
- 系统分析了各领域的方法、进展与挑战，包括数字域像素级修改、传感器内集成隐私机制、物理域可穿戴伪装。
- 评估了现有评估协议，指出缺乏标准化基准是当前主要瓶颈。
- 总结了关键开放问题并提出了未来研究方向，附有开源项目页面。

## 全文

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.

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_b1e32faf941d1a5790a63ea9