  In the post-pandemic era, wearing face masks has posed great challenge to the
ordinary face recognition. In the previous study, researchers has applied
pretrained VGG16, and ResNet50 to extract features on the elaborate curated
existing masked face recognition (MFR) datasets, RMFRD and SMFRD. To make the
model more adaptable to the real world situation where the sample size is
smaller and the camera environment has greater changes, we created a more
challenging masked face dataset ourselves, by selecting 50 identities with 1702
images from Labelled Faces in the Wild (LFW) Dataset, and simulated face masks
through key point detection. The another part of our study is to solve the
masked face recognition problem, and we chose models by referring to the former
state of the art results, instead of directly using pretrained models, we fine
tuned the model on our new dataset and use the last linear layer to do the
classification directly. Furthermore, we proposed using data augmentation
strategy to further increase the test accuracy, and fine tuned a new networks
beyond the former study, one of the most SOTA networks, Inception ResNet v1.
The best test accuracy on 50 identity MFR has achieved 95%.
