Balanced Masked and Standard Face Recognition

Delong Qi, Kangli Hu, Weijun Tan, Qi Yao, Jingfeng Liu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2021, pp. 1497-1502

Abstract


We present the improved network architecture, data augmentation, and training strategies for the Webface track and Insightface/Glint360K track of the masked face recognition challenge of ICCV2021. One of the key goals is how to have a balanced performance of masked and standard face recognition. In order to prevent the overfitting for the masked face recognition, we balance the total number of masked faces by not more than 10% of the total face recognition in the training dataset. We propose a few key changes to the face recognition network including a new stem unit, drop block, and face alignment using YOLO5Face. With this strategy, we achieve good and balanced performance for both masked and standard face recognition.

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[bibtex]
@InProceedings{Qi_2021_ICCV, author = {Qi, Delong and Hu, Kangli and Tan, Weijun and Yao, Qi and Liu, Jingfeng}, title = {Balanced Masked and Standard Face Recognition}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops}, month = {October}, year = {2021}, pages = {1497-1502} }