Masked Face Recognition Algorithm for a Contactless Distribution Cabinet

Abstract

A contactless delivery cabinet is an important courier self-pickup device, for the reason that COVID-19 can be transmitted by human contact. During the pandemic period of COVID-19, wearing a mask to take delivery is a common application scenario, which makes the study of masked face recognition algorithm greatly significant. A masked face recognition algorithm based on attention mechanism is proposed in this paper in order to improve the recognition rate of masked face images. First, the masked face image is separated by the local constrained dictionary learning method, and the face image part is separated. Then, the dilated convolution is used to reduce the resolution reduction in the subsampling process. Finally, according to the important feature information of the face image, the attention mechanism neural network is used to reduce the information loss in the subsampling process and improve the face recognition rate. In the experimental part, the RMFRD and SMFRD databases of Wuhan University were selected to compare the recognition rate. The experimental results show that the proposed algorithm has a better recognition rate.