Face recognition using the convolutional neural network for barrier gate system
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 neu...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
International Association of Online Engineering
2021
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/31810/ http://umpir.ump.edu.my/id/eprint/31810/1/Face%20recognition%20using%20the%20convolutional%20neural%20network%20for%20barrier%20gate%20system.pdf |
| _version_ | 1848823862557409280 |
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| author | Mochammad Langgeng, Prasetyo Achmad Teguh, Wibowo Mujib, Ridwan Mohammad Khusnu, Milad Sirajul, Arifin Muhammad Andik, Izzuddin Rr Diah Nugraheni, Setyowati Ferda, Ernawan |
| author_facet | Mochammad Langgeng, Prasetyo Achmad Teguh, Wibowo Mujib, Ridwan Mohammad Khusnu, Milad Sirajul, Arifin Muhammad Andik, Izzuddin Rr Diah Nugraheni, Setyowati Ferda, Ernawan |
| author_sort | Mochammad Langgeng, Prasetyo |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T03:03:53Z |
| format | Article |
| id | ump-31810 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:03:53Z |
| publishDate | 2021 |
| publisher | International Association of Online Engineering |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-318102021-08-20T15:28:27Z http://umpir.ump.edu.my/id/eprint/31810/ Face recognition using the convolutional neural network for barrier gate system Mochammad Langgeng, Prasetyo Achmad Teguh, Wibowo Mujib, Ridwan Mohammad Khusnu, Milad Sirajul, Arifin Muhammad Andik, Izzuddin Rr Diah Nugraheni, Setyowati Ferda, Ernawan QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering 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. International Association of Online Engineering 2021 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/31810/1/Face%20recognition%20using%20the%20convolutional%20neural%20network%20for%20barrier%20gate%20system.pdf 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. (Published) https://doi.org/10.3991/ijim.v15i10.20175 https://doi.org/10.3991/ijim.v15i10.20175 |
| spellingShingle | QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering Mochammad Langgeng, Prasetyo Achmad Teguh, Wibowo Mujib, Ridwan Mohammad Khusnu, Milad Sirajul, Arifin Muhammad Andik, Izzuddin Rr Diah Nugraheni, Setyowati Ferda, Ernawan Face recognition using the convolutional neural network for barrier gate system |
| title | Face recognition using the convolutional neural network for barrier gate system |
| title_full | Face recognition using the convolutional neural network for barrier gate system |
| title_fullStr | Face recognition using the convolutional neural network for barrier gate system |
| title_full_unstemmed | Face recognition using the convolutional neural network for barrier gate system |
| title_short | Face recognition using the convolutional neural network for barrier gate system |
| title_sort | face recognition using the convolutional neural network for barrier gate system |
| topic | QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/31810/ http://umpir.ump.edu.my/id/eprint/31810/ http://umpir.ump.edu.my/id/eprint/31810/ http://umpir.ump.edu.my/id/eprint/31810/1/Face%20recognition%20using%20the%20convolutional%20neural%20network%20for%20barrier%20gate%20system.pdf |