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.

翻译：人脸去身份识别旨在从人脸图像中移除个人身份信息，同时保留与任务相关的实用属性，如年龄、性别和表情。这对于隐私保护的计算机视觉至关重要，然而该领域目前面临实现方案碎片化、评估协议不一致以及不同研究结果难以比较等问题。这些挑战源于任务固有的复杂性：FDeID涉及多个下游应用（例如年龄估计、性别识别、表情分析），并需要在三个维度上进行评估（例如隐私保护、效用保持和视觉质量），这使得现有代码库难以使用和扩展。为解决这些问题，我们提出了FDeID-Toolbox，一个专为可复现FDeID研究设计的综合性工具箱。我们的工具箱采用模块化架构，包含四个核心组件：（1）针对主流基准数据集的标准化数据加载器，（2）涵盖经典方法至SOTA生成模型的统一方法实现，（3）灵活的推理流水线，以及（4）覆盖隐私、效用和质量指标的系统化评估协议。通过实验，我们证明FDeID-Toolbox能够在一致条件下对多种FDeID方法进行公平且可复现的比较。