  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 to have a
balanced performance of masked and standard face recognition. In order to
prevent the overfitting for the masked face recognition, we control 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, face detection and alignment
using YOLO5Face, feature concatenation, a cycle cosine learning rate, etc. With
this strategy, we achieve good and balanced performance for both masked and
standard face recognition.

Computer Vision and Pattern Recognition;Artificial Intelligence