Project Aria Open Ecosystem
EgoBlur: a new AI model from Meta
to preserve privacy by detecting and blurring
PII from images
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.
What is it?
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.
Faces
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.
License plates
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.
How was the EgoBlur face detection model trained?
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.
How is EgoBlur evaluated?
Evaluated on real-world data with responsible AI attributes
To evaluate performance and reduce bias, EgoBlur is benchmarked against Aria Pilot Dataset & the CCV2 Dataset.
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.
EGOBLUR RESEARCH PAPER
Access EgoBlur Models & Tools
If you are a researcher in AI or ML research, access the EgoBlur models and accompanying tools here.
Models
Frequently Asked Questions
Yes. Both face and license plate models are licensed under Apache 2.0, meaning the models are available for use for both research and industry applications.
Both the EgoBlur face and license plate models are approximately 400 MB and have ~104 million parameters.
The EgoBlur face model takes approximately 7 days to train on 4 machines with 8 NVIDIA V100 GPUs. The license plate model takes about a day to train on similar configurations.
No. Like other open source models, such as RetinaFace, the EgoBlur models are trained only to locate the position of faces and license plates of vehicles within color or greyscale images. The models are not used to track or identify individual faces or license plates.
Yes, in addition to egocentric data from Project Aria, the EgoBlur models are also trained on non-egocentric data from the CCV2 dataset. The models should give a comparable performance to state-of the art models like RetinaFace for such non-egocentric data.
Egoblur is based on the Faster RCNN model with a ResNext backbone. The models are trained using Meta’s publicly available Detectron2 and Detectron2go libraries.
Based on an RGB 2 MP image, both the EgoBlur face detection and license plate models run in approximately 0.5 seconds on a GPU, and 8 seconds on a CPU.
Based on an 0.3 MP greyscale image, both face detection and licence plate models run in approximately 0.3 seconds on a GPU, and 1.8 seconds on a CPU.
No, Egoblur face and licence plate models are for detection only. The models output rectangular bounding boxes, not masks or labels.
Yes, the EgoBlur model works on both images and videos.
Please email projectaria@meta.com to report any bugs, or if you have any further queries about the EgoBlur models.
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