Abstract
In the era of epidemic, wearing a mask when going out has become a more common phenomenon. And how to complete face recognition and guarantee certain accuracy without letting users take off their masks has been a challenge in recently years’ face recognition field. In this paper, the traditional masked face recognition method and the new MTCNN deep learning masked face recognition method are compared and analyzed, and other similar masked experiments are introduced as data references. It is synthetically found that the accuracy of MTCNN masked face recognition using CASIA-FaceV5 as dataset is more than 90%, which is far better than other traditional recognition methods, but its requirement for dataset image quality is high, and the training time is long and costly. We hope that the process analysis and result comparison of different methods in this paper can shed light on the future development of masked face recognition technology.
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