WebFace260M Track of ICCV21-MFR

The Masked Face Recognition Challenge & Workshop(MFR) will be held in conjunction with the International Conference on Computer Vision (ICCV) 2021.

There're WebFace260M track here and InsightFace track in this workshop.

Traditionally, face recognition systems are presented with mostly non-occluded faces, which include primary facial features such as the eyes, nose, and mouth. However, there are a number of circumstances in which faces are occluded by masks such as in pandemic, medical settings, excessive pollution, or laboratories. During the COVID-19 coronavirus epidemic, almost everyone wears a facial mask, which poses a huge challenge to face recognition. For instance, a person wearing a mask attempts to authenticate against a prior visa or passport photo at the airport. Traditional face recognition systems may not effectively recognize the masked faces, but removing the mask for authentication will increase the risk of virus infection. To cope with the above-mentioned challenging scenarios arising from wearing masks, it is crucial to improve the existing face recognition approaches. Recently, some commercial providers have announced the availability of face recognition algorithms capable of handling face masks, and an increasing number of research publications have surfaced on the topic of face recognition on people wearing masks. However, due to the sudden outbreak of the epidemic, there are yet no publicly available masked face recognition benchmark. In this WebFace260M Track of ICCV21-MFR, we have developed a comprehensive benchmark for evaluating both standard and masked face recognition.

Rules (tentative):

The WebFace260M Track of ICCV21-MFR has two phases: For the first phase, participants should submit results based on 30% WebFace260M data (already open for application) in this link or other public data (such as MS1MV3 and Glint360K). If the results outperform the official ResNet-50 baseline, we will provide your team with the full WebFace260M data access for second phase. Note: The full WebFace260M data will be open for applications in the future, i.e., the teams participating challenges can obtain the access in advance.

Mask data-augmentation is allowed, for example this. The applied mask augmentation tool should be reproducible.

External dataset and pretrained models are both prohibited in second phase.

Participants submit onnx model, then get scores by our online evaluation. Test images are invisible during challenges.

WebFace260M Track adopts FRUITS (the Face Recognition Under Inference Time conStraint) protocol (1000 ms constrain for whole face recognition system, inference time is measured on a single core of an Intel Xeon CPU E5-2630-v4@2.20GHz processor. Please see our paper for details). Only these solutions are qualified for awards.

There are two ranks (also two winners): one is according to All in Standard Face Recognition, and the other is according to All-Masked in Masked Face Recognition.

Top-ranked participants should provide their solutions and codes to ensure their validity after submission closed.

Submission Guide

Please refer to for the submission package and details.
Submission server link:

1.Participants should put all models and files into $MFR_ROOT/assets/.
2.Participants must provide $MFR_ROOT/ which contains the PyWebFace260M class.
3.Participants should run the in $MFR_ROOT/demo/ on the provided docker file to ensure the correctness of feature and time constraints.
4.Participants must package the code directory for submission using zip -r $MFR_ROOT and then upload it to codalab.
5.Please sign-up with the real organization name. You can hide the organization name in our system if you like.
6.You can decide which submission to be displayed on the leaderboard.

Test Set for WebFace260M Track

Since public evaluations are most saturated and may contain noise, we manually construct an elaborated test set. It is well known that recognizing strangers, especially when they are similar-looking, is a difficult task even for experienced vision researchers. Therefore, our multi-ethnic annotators only select their familiar celebrities, which ensure the high-quality of the test set. Besides, annotators are encouraged to gather attribute-balanced faces, and recognition models are introduced to guide hard sample collection. The statistics of the final test set are listed in below table. In total, there are 60926 faces of 2478 identities. Rich attributes (e.g. age, gender, race, controlled, wild, masked) are accurately annotated.

The # identities and # faces statistics of our test set.

The samples of out test set. The left, middle and right three columns of faces have attributes of controlled, wild and masked respectively.


Zheng Zhu, Guan Huang, Jiankang Deng, Yun Ye, Junjie Huang, Xinze Chen, Jiagang Zhu, Tian Yang, Jia Guo, Jiwen Lu, Dalong Du and Jie Zhou

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