Access control system through detection of faces with masks based on artificial vision
DOI:
https://doi.org/10.59169/pentaciencias.v5i4.654Keywords:
access control system; artificial vision; Convolutional Neural Networks; temperature detection; TensorFlow; OpenCV2Abstract
The Covid-19 virus pandemic has set precedents in terms of the biosecurity of the population, reflecting that prevention is an adequate tool to avoid the transmission of diseases. Therefore, the present work shows the development of a prototype of a detection system for the use of masks and body temperature scanning for access control to commercial premises based on artificial vision algorithms. This system uses a quantitative field research methodology at a descriptive level through a model built in Tensorflow that works on the video obtained using the OpenCV2 program. The implementation consists of three modules: body temperature detection through the Mlx90614 sensor, detection of the correct use of the mask through a front camera analyzed with convolutional neural networks, and access control using electronic components. In the performance tests, the performance metrics of the neural network are analyzed with different learning rates and number of iterations such as: precision, accuracy and sensitivity, in five classification cases: high or normal temperature, faces without a mask, faces with a mask incorrectly placed and with a mask placed correctly. The results present a learning rate of 87.5%, showing identifiers to detect if a client wears a mask correctly and issuing sound alerts when biosecurity measures are not complied with. In relation to access control, an effectiveness of 100% after detection.
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