A Machine Learning And Iot Based Contactless Entry System

ABSTRACT

The COVID-19 pandemic has intensely impacted humanities globally. This scenario has laid down many protocols and procedures to mitigate the risk and thus ensure the safety of individuals. The pandemic has augmented the demand of contactless mechanism especially in the public places.This paper presents novel machine learning and internet of things based solution for contactless entry to premises following mandatory checks at the entry as per COVID-19 protocols. The proposed model senses the body temperature and detects the face mask of an individual prior to entry. The entrance is allowed through contactless opening of the gate only if the body temperature is within prescribed limits and the face mask is properly put on.The current work uses a machine learning model for detection of the face mask which uses the real time image during screening;the algorithm is trained using the data sets with and without mask. The temperature screening is carried out with temperature sensor connected to the Arduino processor.A prototype model using Arduino is prepared based on the inputs received from the temperature sensor and the Machine learning Algorithm. The gate shall open for entry if a person has normal body temperature and wearing a proper face mask, else, will be restricted through a custom alert. Also a track of the number of persons entering is monitored and sent to a web portal on real time basis to account for overcrowding.The model ensures a contactless check at the entry, thus, controlling the outbreak of the Covid situation. © 2022 IEEE.