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Face recognition using the convolutional neural network for barrier gate system

Mochammad Langgeng, Prasetyo and Achmad Teguh, Wibowo and Mujib, Ridwan and Mohammad Khusnu, Milad and Sirajul, Arifin and Muhammad Andik, Izzuddin and Rr Diah Nugraheni, Setyowati and Ferda, Ernawan (2021) Face recognition using the convolutional neural network for barrier gate system. International Journal of Interactive Mobile Technologies (iJIM), 15 (10). 138 -153. ISSN 1865-7923

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Abstract

The implementation of face recognition technique using CCTV is able to prevent unauthorized person enter the gate. Face recognition can be used for authentication, which can be implemented for preventing of criminal incidents. This re-search proposed a face recognition system using convolutional neural network to open and close the real-time barrier gate. The process consists of a convolutional layer, pooling layer, max pooling, flattening, and fully connected layer for detecting a face. The information was sent to the microcontroller using Internet of Thing (IoT) for controlling the barrier gate. The face recognition results are used to open or close the gate in the real time. The experimental results obtained average error rate of 0.320 and the accuracy of success rate is about 93.3%. The average response time required by microcontroller is about 0.562ms. The simulation result show that the face recognition technique using CNN is highly recommended to be implemented in barrier gate system.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Barrier gate system; Convolutional neural network; Face recognition; IoT
Subjects: Q Science > QA Mathematics > QA76 Computer software
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Computing
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 20 Aug 2021 15:28
Last Modified: 20 Aug 2021 15:28
URI: http://umpir.ump.edu.my/id/eprint/31810
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