Project Aria Open Ecosystem
EgoBlur helps researchers innovate responsibly
EgoBlur is used by Project Aria,
Meta’s research tool for accelerating AI and machine perception.
As a continued effort on responsible innovation, Meta has developed an advanced face and license plate anonymization system that has been used internally since the launch of the Project Aria program in 2020.
Privacy has always been a top priority for Project Aria, Meta’s research tool for accelerating AI and ML technology. As such, we have been committed to follow best practices of responsible AI.
We believe the external research community could also benefit from this state-of-the-art anonymization system, and as such, we have decided to open source both the faces and license plates' anonymization models under an open-source license (Apache 2.0) for both commercial and non-commercial use. This means that researchers working to accelerate AI and ML research can do so while maintaining the privacy of those around them.
A FasterRCNN-based detector for faces and vehicle license plates
Designed for blurring faces and license plates,
optimized for devices with a first-person perspective.
EgoBlur detects faces in both color and greyscale images, so that personally identifiable information may be removed from captured data.
The model performs consistently across the full range of ‘responsible AI labels’, as defined by the CCV2 dataset, to ensure privacy is respected, for everyone.
In addition to faces, EgoBlur also provides strong performance for obfuscating license plates.
EgoBlur provides comparable or better performance than alternative state of the art systems.
Optimized for devices with an egocentric perspective
At time of release, EgoBlur sets a high standard of performance comparable to or better than other publicly-available methods for face and license plate detection on cameras that capture a first-person perspective, such as AR and VR devices.
However, EgoBlur also provides strong and reliable face and license plate detection for many diverse camera types and perspectives.
23M images, 790M bounding boxes
EgoBlur’s advanced capabilities are the result of training on millions of images and masks collected through the use of weakly supervised learning. Additional techniques, such as data augmentation, were also used to increase the model’s performance on greyscale images.
Evaluated on real-world data with responsible AI attributes
Self-reported ‘responsible AI labels’ from the CCV2 dataset are used to evaluate EgoBlur against a number of attributes such as skin tone, self identified gender, age, and country. This helps to ensure EgoBlur works consistently for everybody.
Read the accompanying EgoBlur Research Paper
For more information about the EgoBlur model, read our paper on arXiv.
Access EgoBlur Models & Tools
If you are a researcher in AI or ML research, access the EgoBlur models and accompanying tools here.
Frequently Asked Questions