FDeID-Toolbox: Face De-Identification Toolbox
2026-03-13 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors created FDeID-Toolbox, a tool that helps researchers compare different face de-identification methods fairly and easily. Face de-identification means hiding personal information in photos but keeping useful details like age and expression. Their toolbox organizes data, methods, and tests in one place to solve problems with inconsistent experiments in the field. It lets people check how well methods protect privacy, keep useful info, and look good.
Face de-identificationPrivacy protectionUtility preservationVisual qualityGenerative modelsBenchmark datasetsEvaluation protocolsInference pipelinesComputer vision
Authors
Hui Wei, Hao Yu, Guoying Zhao
Abstract
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.