FACE MASK DETECTION AND WARNING SYSTEM FOR PREVENTING RESPIRATORY INFECTION USING THE INTERNET OF THINGS
The respiratory-related diseases can spread and cause infection from patients to other people easily. The mask can help preventing or reduce the risk of spreading respiratory diseases from person to person. This research presents the development of the warning system to the people for wearing the masks by using the ESP32-CAM and IoT devices. The Haar-like feature and cascade classify training techniques are used to detect the human faces and the masks in this system. A total of 2,160 sampling images was trained for modeling the mask detection on human faces. The result shows that the accuracy of the developed system is 91.60% for mask detection. The system evaluation in black box testing has the mean value 4.51 with a standard deviation 0.51 and a mean value 4.62 with a standard deviation 0.49 by experts and users, respectively. Moreover, the acceptance of the system has a high consensus. It could be said that the developed system can detect and classify the person who is wearing the face mask for protecting the respiratory infection at the highest level.
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