打造统一工具箱，让人脸去标识研究更可复现、可比较。
FDeID-Toolbox: Face De-Identification Toolbox
- 模块化设计，整合数据加载、方法实现与评估流程
- 支持多种主流模型，可在统一条件下公平对比性能
- 适合隐私保护与视觉任务平衡的研究者使用
人脸去标识（FDeID）旨在移除人脸图像中的个人身份信息，同时保留年龄、性别、表情等任务相关属性，对隐私保护型计算机视觉至关重要。然而该领域存在实现碎片化、评估协议不统一、结果不可比等问题，根源在于任务本身复杂：需覆盖年龄估计、性别识别、表情分析等多种下游应用，并在隐私保护、效用保持、视觉质量三个维度综合评估，导致现有代码库难以使用和扩展。为此，我们提出FDeID-Toolbox，一个面向可复现研究的综合性工具箱。其包含四个核心组件：(1) 主流基准数据集的标准数据加载器；(2) 从经典方法到最新生成模型的统一方法实现；(3) 灵活的推理流程；(4) 覆盖隐私、效用与质量指标的系统化评估协议。实验表明，FDeID-Toolbox可在一致条件下实现不同FDeID方法的公平、可复现比较。
原文摘要 · Abstract (English)
Face de-identification (FDeID) aims to remove personally identifiable information from facial images while preserving task-relevant utility attributes such as age, gender, and expression. It is critical for privacy-preserving computer vision, yet the field suffers from fragmented implementations, inconsistent evaluation protocols, and incomparable results across studies. These challenges stem from the inherent complexity of the task: FDeID spans multiple downstream applications (e.g., age estimation, gender recognition, expression analysis) and requires evaluation across three dimensions (e.g., privacy protection, utility preservation, and visual quality), making existing codebases difficult to use and extend. To address these issues, we present FDeID-Toolbox, a comprehensive toolbox designed for reproducible FDeID research. Our toolbox features a modular architecture comprising four core components: (1) standardized data loaders for mainstream benchmark datasets, (2) unified method implementations spanning classical approaches to SOTA generative models, (3) flexible inference pipelines, and (4) systematic evaluation protocols covering privacy, utility, and quality metrics. Through experiments, we demonstrate that FDeID-Toolbox enables fair and reproducible comparison of diverse FDeID methods under consistent conditions.
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