Proposed Detection Face Model by MobileNetV2 Using Asian Data Set
Copyright policy )Proposed Detection Face Model by MobileNetV2 Using Asian Data Set
In 2019, the infectious coronavirus disease 2019 (COVID-19) was first reported in Wuhan, China. It has then become a public health problem in the world. This pandemic is having a heavy impact on the lives of people in our country. All countries are trying to control the spread of this disease. To solve the problem, each person needs to wear masks in a public place. Therefore, we propose a model capable of distinguishing between masked and nonmasked faces using a convolutional neural network (CNN) based on deep learning (DL)—MobileNetV2 in this paper. The model can detect people who are not wearing masks. It has an accuracy of up to 99.37%. The model will be applied in places such as schools, offices, and so on to monitor the wearing masks.
- Vietnam National University, Ho Chi Minh City Viet Nam
- Department of Information Systems Switzerland
- Ho Chi Minh City University of Technology Viet Nam
- HUTECH University Viet Nam
- Le Hong Phong High School for the Gifted Viet Nam
Computer engineering. Computer hardware, Artificial intelligence, China, Face (sociological concept), Convolutional neural network, Set (abstract data type), Infectious disease (medical specialty), Motion Detection, Visual Object Tracking and Person Re-identification, TK7885-7895, Facial Landmark Detection, Sociology, Virology, Pathology, Disease, Face Recognition and Analysis Techniques, Face masks, Public health, Pandemic, Geography, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Deep learning, Outbreak, Social science, Computer science, FOS: Sociology, Programming language, Coronavirus disease 2019 (COVID-19), Generative Adversarial Networks in Image Processing, Archaeology, Computer Science, Physical Sciences, Medicine, Computer vision, Computer Vision and Pattern Recognition, 2019-20 coronavirus outbreak
Computer engineering. Computer hardware, Artificial intelligence, China, Face (sociological concept), Convolutional neural network, Set (abstract data type), Infectious disease (medical specialty), Motion Detection, Visual Object Tracking and Person Re-identification, TK7885-7895, Facial Landmark Detection, Sociology, Virology, Pathology, Disease, Face Recognition and Analysis Techniques, Face masks, Public health, Pandemic, Geography, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Deep learning, Outbreak, Social science, Computer science, FOS: Sociology, Programming language, Coronavirus disease 2019 (COVID-19), Generative Adversarial Networks in Image Processing, Archaeology, Computer Science, Physical Sciences, Medicine, Computer vision, Computer Vision and Pattern Recognition, 2019-20 coronavirus outbreak
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