Face Recognition Using the Convolutional Neural Network for Barrier Gate System

Face Recognition Using the Convolutional Neural Network for Barrier Gate System

barrier gate system

convolutional neural network

face recognition

IoT

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.

Prasetyo, Mochammad Langgeng

Wibowo, Achmad Teguh

Ridwan, Mujib

Milad, Mohammad Khusnu

Arifin, Sirajul

Izzuddin, Muhammad Andik

Setyowati, Rr Diah Nugraheni

Ernawan, Ferda

International Journal of Interactive Mobile Technologies (iJIM); Vol. 15 No. 10 (2021); pp. 138-153

1865-7923

International Association of Online Engineering (IAOE), Vienna, Austria

2021-05-25

Copyright (c) 2021 Mochammad Langgeng Prasetyo, Achmad Teguh Wibowo, Ferda Ernawan, Mujib Ridwan

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eng

info:eu-repo/semantics/article

info:eu-repo/semantics/publishedVersion

Peer-reviewed Article