A Network for Detecting Facial Features During the COVID-19 Epidemic
Bin Lin, Yanlin Mu, Zili Fu, Chaochao Li, Xuliang Duan · 2021
The new coronavirus can spread through respiratory droplets, and wearing a mask correctly can effectively prevent the virus from spreading. However, the current detection algorithms are based on unobstructed faces, which affects the detection task when wearing a mask. To solve these problems, a facial feature detection algorithm based on Mtcnn+Mobilenet+GDBT in complex scenes is proposed. First, it can detect whether to wear a mask and the fatigue state of the face. Second, it can set different thresholds according to the facial characteristics of different people, and initialize the characteristics of different frames in 5 seconds. The innovation of our paper: the self-adaption characteristics for every person, it avoids measuring everyone by one standard, which is of great significance to the popularization of the product. Then train a dataset of masks and feature points containing 708 images. The experimental results show that compared with the traditional detection network, the new network can effectively detect facial features in the context of the epidemic. The loss we adopt is Focal loss. The lowest loss of net is 0.01 nearly.