Study of the Performance of Machine Learning Algorithms for Face Mask Detection

Nowadays, the situation of the Covid-19 virus still intensifying throughout the world. The number of populations of each country is severely infected and deaths. One solution to prevent is to wearing a masked face. Many businesses and organization need to adapt and protect an infected person by detecting whoever does not wear masked face; however, the number of users or customers are more than staffs result in difficult checking. This paper studies the performance of the three algorithms: KNN, SVM and MobileNet to find the best algorithm which is suitable for checking who wearing masked face in a real-time situation. The results show that MobileNet is the best accuracy both from input images and input video from a camera (real-time).