Development of AI-Enabled Contactless Visitor Access Monitoring System
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Abstract - This research focuses on developing an AI-enabled Contactless Visitors Access Monitoring System. The monitoring system integrated a facial recognition system with a real-time database. Visitors registered themselves through an online registration form. This research developed and compared two different facial recognition systems. The first facial recognition system integrated the dlib model with the face recognition library, while the second integrated the FaceNet model with the Haar Cascade Classifier. Twenty facial images were collected. The researcher found out that the facial recognition system with FaceNet has higher accuracy of 82% while the has 76% of accuracy. The value of EER obtained for FaceNet is at 51% with an allowed threshold of 0.52. This research found that the accuracy of the facial recognition system could be affected by different conditions, such as the visitors’ facial features, the distance between the camera and the face, and the illumination condition of the test environment. The number of images does not affect the speed and the accuracy of the facial recognition system in this research due to the small number of images.
(Manuscript received: 26 June 2023 | Accepted: 1st September 2023 | Published: 30 September 2023)
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