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http://dx.doi.org/10.14400/JDC.2021.19.2.245

Design and Implementation of Visitor Access Control System using Deep learning Face Recognition  

Heo, Seok-Yeol (Dept. of IT Engineering & Application, Pusan National University)
Kim, Kang Min (Dept. of IT Engineering & Application, Pusan National University)
Lee, Wan-Jik (Dept. of IT Engineering & Application, Pusan National University)
Publication Information
Journal of Digital Convergence / v.19, no.2, 2021 , pp. 245-251 More about this Journal
Abstract
As the trend of steadily increasing the number of single or double household, there is a growing demand to see who is the outsider visiting the home during the free time. Various models of face recognition technology have been proposed through many studies, and Harr Cascade of OpenCV and Hog of Dlib are representative open source models. Among the two modes, Dlib's Hog has strengths in front of the indoor and at a limited distance, which is the focus of this study. In this paper, a face recognition visitor access system based on Dlib was designed and implemented. The whole system consists of a front module, a server module, and a mobile module, and in detail, it includes face registration, face recognition, real-time visitor verification and remote control, and video storage functions. The Precision, Specificity, and Accuracy according to the change of the distance threshold value were calculated using the error matrix with the photos published on the Internet, and compared with the results of previous studies. As a result of the experiment, it was confirmed that the implemented system was operating normally, and the result was confirmed to be similar to that reported by Dlib.
Keywords
Face recognition; Deep learning; Access control; OpenCV; Dlib; Design and Implementation;
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