Automated Visitor Record System

Fadhlan Hafizhelmi Kamaru Zaman (1), Ahmad Asari Sulaiman (2), Syahrul Afzal Che Abdullah (3), Mohd Fuad Abdul Latip (4), Zairi Ismael Rizman (5)
(1) Department of Computer Engineering, Faculty of Electrical Engineering, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia
(2) Department of Computer Engineering, Faculty of Electrical Engineering, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia
(3) Department of Computer Engineering, Faculty of Electrical Engineering, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia
(4) Department of Computer Engineering, Faculty of Electrical Engineering, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia
(5) Universiti Teknologi MARA
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How to cite (IJASEIT) :
Kamaru Zaman, Fadhlan Hafizhelmi, et al. “Automated Visitor Record System”. International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 5, Oct. 2017, pp. 1784-9, doi:10.18517/ijaseit.7.5.1353.
As normally practiced, the information of visitor is recorded manually by either the visitor or the guard when the visitor arrives at the guard house. This manual process takes large amount of time and involves tedious works. Furthermore, it is prone to fraudulent information provided by visitors and occasionally interferes with guard’s actual job in securing the area. This paper aims to provide an automated solution to these aforementioned problems. We address these problems by replacing the existing manual way of recording visitor information by an automated and fully computerized system. The solution proposed uses Radio-Frequency Identification (RFID), smart card information retrieval as well as computer vision and image processing to record and manage visitors’ data. To evaluate the similarity between face images from camera and National Registration Identification Card (NRIC), we propose a method to find dissimilarity index between the faces. We found that the system minimizes the need for human interventions, improves the time required during recording of visitors’ information as well as efficiently manages and analyses visitors’ records. Additionally, our proposed method for finding face similarity yields a promising result of TPR = 0.914 and FPR = 0.140 when tested using publicly available face dataset called AR Dataset. The system is able to minimize the need for guard and visitor interventions, improves the time required during recording of visitors’ information especially for recurring visits and also capable of securely and efficiently manages and analyses visitors’ records.

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