Face recognition using the convolutional neural network for barrier gate system

• Main Authors: Mochammad Langgeng, Prasetyo, Achmad Teguh, Wibowo, Mujib, Ridwan, Mohammad Khusnu, Milad, Sirajul, Arifin, Muhammad Andik, Izzuddin, Rr Diah Nugraheni, Setyowati, Ferda, Ernawan
• Format: Article
• Language: English
• Published: International Association of Online Engineering 2021
• Subjects: QA76 Computer software; TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://umpir.ump.edu.my/id/eprint/31810/1/Face%20recognition%20using%20the%20convolutional%20neural%20network%20for%20barrier%20gate%20system.pdf; http://umpir.ump.edu.my/id/eprint/31810/; https://doi.org/10.3991/ijim.v15i10.20175; https://doi.org/10.3991/ijim.v15i10.20175
• Institution: Universiti Malaysia Pahang

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.