Comparison of Deep Neural Network Models of Face Mask Detection in Multi-Angle Head Pose
P. Sreevani, P. Sunitha Devi · Zenodo (CERN European Organization for Nuclear Research) · 2021
Wearing a Face Mask in public areas has become mandatory to all people in this present Covid-19 pandemic situation. As more number of people gather or visit public places like Supermarkets, Shopping malls, Office etc for their daily activities, air borne disease is more likely to spread from one person to another very fastly. So, wearing face mask not only helps oneself but also it protects others from spread of disease. Recognition of face is a popular and significant technology in recent years. Previously, detecting a face of a person and his face expression has been done. But identifying whether a person is wearing a face mask or not and that too in various angles like frontal face, side angle face is a present challenging task. The proposed system uses face mask dataset which consists of images like person with mask and person without mask. Input images are pre-processed, detection of face take place and given for deep neural networks for training, finally it classifies whether a person is without or with mask. The proposed system uses Convolutional Neural Network which is compared with different networks like MobileNetV2 and Vgg16. In real-time, proposed system is implemented by taking input from webcam which helps in detecting whether a person is wearing a face mask or not wearing a face mask.