Face recognition using the convolutional neural network for Barrier Gate System

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

[thumbnail of Mochammad Langgeng Prasetyo_Face recognition using the convolutional neural network.pdf] Text
Mochammad Langgeng Prasetyo_Face recognition using the convolutional neural network.pdf
Available under License Creative Commons Attribution.

Download (974kB)


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 ontrolling 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: https://online-journals.org/index.php/i-jim/article/view/20175
Prasetyo, Mochammad Langgeng
Wibowo, Achmad Teguh
Ridwan, Mujib
Milad, Mohammad Khusnul
Arifin, Sirajul
sirajul.arifin@yahoo.com; sirajul.arifin@uinsby.ac.id
Izzuddin, Muhammad Andik
Setyowati, Rr Diah Nugraheni
Ernawan, Ferda
Uncontrolled Keywords: Barrier gate system; convolutional neural network; face recognition; IoT
Subjects: 08 INFORMATION AND COMPUTING SCIENCES > 0805 Distributed Computing > 080503 Networking and Communications
Divisions: Karya Ilmiah > Artikel
Depositing User: Susilo Joko
Date Deposited: 24 Aug 2021 03:36
Last Modified: 17 Sep 2021 03:44
URI: http://repository.uinsa.ac.id/id/eprint/40

Actions (login required)

View Item
View Item