The COVID-19 outbreak has taken a toll on humankind and the world’s health to a breaking point,causing millions of deaths and cases worldwide.Several preventive measures were put in place to counter the esca-lation o...The COVID-19 outbreak has taken a toll on humankind and the world’s health to a breaking point,causing millions of deaths and cases worldwide.Several preventive measures were put in place to counter the esca-lation of COVID-19.Usage of face masks has proved effective in mitigating various airborne diseases,hence immensely advocated by the WHO(World Health Organization).A compound CNN-LSTM network is developed and employed for the recognition of masked and none masked personnel in this paper.3833 RGB images,including 1915 masked and 1918 unmasked images sampled from the Real-World Masked Face Dataset(RMFD)and the Simulated Masked Face Dataset(SMFD),plus several personally taken images using a webcam are utilized to train the suggested compound CNN-LSTM model.The CNN-LSTM approach proved effective with 99%accuracy in detecting masked individuals.展开更多
文摘The COVID-19 outbreak has taken a toll on humankind and the world’s health to a breaking point,causing millions of deaths and cases worldwide.Several preventive measures were put in place to counter the esca-lation of COVID-19.Usage of face masks has proved effective in mitigating various airborne diseases,hence immensely advocated by the WHO(World Health Organization).A compound CNN-LSTM network is developed and employed for the recognition of masked and none masked personnel in this paper.3833 RGB images,including 1915 masked and 1918 unmasked images sampled from the Real-World Masked Face Dataset(RMFD)and the Simulated Masked Face Dataset(SMFD),plus several personally taken images using a webcam are utilized to train the suggested compound CNN-LSTM model.The CNN-LSTM approach proved effective with 99%accuracy in detecting masked individuals.
