Open AccessFace Mask Detection and Sanitizer Despenser with Temperature Detection
Author(s) -
Akash Thakre,
Pravin Hande,
Abhishek Pounikar,
Jaydeo Dabre,
Virendra Kumar Yadav
Publication year - 2021
Publication title -
international journal of scientific research in science and technology
Language(s) - English
Resource type - Journals
eISSN - 2395-602X
pISSN - 2395-6011
DOI - 10.32628/ijsrst218391
Subject(s) - buzzer , raspberry pi , hand sanitizer , computer science , covid-19 , real time computing , face detection , simulation , embedded system , computer hardware , computer security , internet of things , engineering , artificial intelligence , facial recognition system , electrical engineering , alarm , pattern recognition (psychology) , medicine , disease , pathology , biology , infectious disease (medical specialty) , genetics
In the present scenario due to Covid-19, the need for face mask detection applications, temperature detection and hand sanitizing are now in high demand for Railway Entrance, Airport Entrance, Office Entrance, Museums and Amusement Parks, Other Public Places and enterprises to ensure safety. These steps are now done in manual way by which the personnel may get in contact with the other personnel while sanitizing and checking temperature might not be accurate. To mitigate the problem, aiming to increase Covid-19 entrance safety, covering several relevant aspects: Contactless temperature sensing, Mask detection, Automatic hand sanitizing. Contactless temperature sensing subsystem relies on Raspberry Pi using temperature sensor,while mask detection performed by leveraging computer vision techniques on camera-equipped Raspberry Pi, then the automatic hand sanitizing is achieved by the DC motor connected with the sensor and Raspberry Pi. Any person without temperature check, hand sanitizing and mask scan will not be provided entry. Only person having the conditions satisfied by the system is instantly allowed inside, else the buzzer will alert the security about the situation, if any violation of the condition is found. From the simulation results, it is clearly observed that the proposed method has high accuracy compare to the existing methods. Thus the system provides a 100% automated system to prevent the spread of Covid-19.