Modeling Of Mask Detection Systems, Distance Between Objects And Facial Recognition Using The Tiny-YOLOV4 Method, Convolutional Neural Network, And Viola Jones

The development of technology is currently increasing very rapidly, the latest technology such as Face Detection and Face Recognition in public facilities has been widely applied, such as cameras for attendance, facial detection devices as mobile phone security and various other new innovations in the field of technology. The purpose of creating this model is to determine the distance, and the use of masks in public places and to determine the performance when the two algorithms between Tiny-YOLOV4 and Viola Jones are combined. This method uses CPU testing with a clock speed of 2.9 GHz and GPU with a clock speed of 1590 MHz with an accuracy level of 92.6% for mask object detection and 90.67% for face recognition with a maximum distance in front of the camera for human detection is 830 centimeters, mask detection 730 centimeters, and face recognition 530 centimeters. The results of the study produced an accuracy of 92.6% for mask detection and an accuracy of 90.67% for face recognition at maximum distances of 730 cm and 530 cm respectively. The conclusion shows that this system is effective for detecting masks, faces, and distance, providing a significant tool for monitoring compliance in public spaces. Implementing this system can serve as a preventive measure for potential future pandemics