Automatic detection of face with mask to prevent COVID contagion, using LBP algorithm embedded in Rasp-Berry Pi and use of audible alert (Nexo Revista Científica, 2024)
The detection of the faces of people in self-access systems, is a challenging task since the pandemic of the coronavirus (SARS-CoV2) has changed our way of living, especially because companies and some government entities such as hospitals need to grant permission to access their facilities especially if the person does not bring the mask, and although many facilities no longer allow its use, since the above mentioned entities if they justify its use especially if their facilities there are people with chronic diseases that need and justify the use of the mask. In this work, we mention an access system that consists of software embedded in a Rasp-berry PI device, which contains an application formed by the use of a neural network Multi-task cascade convolutional networks (MTCNN), with which the training of the images of faces that bring and do not bring mouthguards is done, Later, and for a better identification of the faces, an algorithm based on Local Binary Patterns Histogram (LBPH) is used, with which the characteristics of the face are obtained and later it is possible to classify if the person to be identified has or does not have a face mask. In the end, our proposed system has an average accuracy of 93% in the detection of faces with face masks.