Predicting Spread of Omicron Variant by Face Recognition Embedded with Distance Measure Sensor Using Machine Learning Techniques

Abstract

Abstract: In a human life pandemic situations are occurred at different time intervals. In December 2019 pandemic is started due to Covid-19 corona virus, still virus is spreading by changing its structure, structure of virus is changed due to mutation with itself, Indian government approved few vaccines like Covisheild, Covaxin, Sputnik, and so on, these vaccines are useful to increase immunity power to reduce impact of virus, maintaining social distancing give better results control spread of virus. To educate people to maintain social distancing, we proposed a method to predicting spread of omicron covid-19 variant by face recognition embedded with distance measure sensor using machine learning techniques, and it is working in five phases. In first phase movements of employees are captured using drones and CC cameras, in phase two all captured images are send to distance measure sensor devices, in phase three check distances with threshold values and if not met send details to next phase, in phase four face recognition technique is used to identify ids of persons who are not maintaining social distance, and in phase five output is presented to end user. To control covid-19 virus proposed model is used to identify list of employees who are not following social distancing, same list is forwarded to heads, and well as concern employees. Keywords: SARS_Cov2, Pandemic, Omicron variant, Machine Learning, Social Distance Detection, Face Recognition, Image processing