TY  - JOUR
TI  - Face Mask Recognition Using MobileNetV2
AU  - Vatsal Patel
AU  -  Dhruti Patel
JO  - International Journal of Scientific Research in Computer Science, Engineering and Information Technology
PB  - Technoscience Academy
DA  - 2021/10/30
PY  - 2021
DO  - https://doi.org/10.32628/CSEIT1217519
UR  - https://ijsrcseit.com/CSEIT1217519
VL  - 7
IS  - 5
SP  - 35	
EP  - 42
AB  - The pandemic of Corona Virus Disease is generating a public health emergency. Wearing a mask is one of the most efficient ways to combat the infection. This paper presents the detection of face masks, through mitigating, evaluating, preventing, and preparing actions regarding COVID-19. In this work, face mask identification is achieved using Machine Learning technique and the Image Classification algorithms are MobileNetV2 with major changes which includes Label Binarizer, ImageNet, and Binary Cross-Entropy. The methods involved in building the model are collecting the data, pre-processing, image generation, model construction, compilation, and finally testing. The proposed method can recognize people with and without masks. The training accuracy of the proposed method is 98.5% and the testing accuracy is 99%. This model is implemented in an image or video stream to detect faces with mask.