Toolkit evaluates face deidentification methods for privacy and quality
Toolkit for Facial Deidentification
Computer Vision and Pattern Recognition
Summary
Protecting people’s identities in photos is important for privacy but hard to measure. The authors created a free toolkit that helps test how well different face-blurring or masking methods work. It checks if faces are safely hidden without ruining the usefulness or quality of images. This toolkit supports many image collections and helps everyone compare new privacy tools fairly. It makes research on face privacy easier by providing one standard way to test methods.
What this means in practice
- •For privacy technology developers: Use the toolkit to benchmark and improve face deidentification methods ensuring privacy without sacrificing image usefulness.
- •For security system integrators: Evaluate and select face anonymization techniques that balance privacy protection with image clarity for surveillance or monitoring systems.
Authors
Esteban Leiva-Montenegro, Matthieu Pillot, Manfred Gonzalez-Hernandez, Vitomir Struc, Peter Peer, Blaž Meden
Abstract
This work presents a novel open-source toolkit designed for the evaluation of face deidentification techniques with available evaluation models. The framework provides a complete evaluation pipeline, including preprocessing, deidentification, and performance assessment, based on data utility, image quality, and identity protection metrics. It supports a variety of datasets, enabling researchers to easily benchmark new deidentification techniques. Results show the effectiveness of some techniques on maintaining privacy while preserving image utility. The toolkit is aimed at unifying future research by providing a standard evaluation framework for privacy-enhancing technologies in facial biometrics. Implementation is available at github.com/blazm/deid-toolkit.