Face Recognition Using Local Binary Patterns Histogram Method Using Raspberry PI
Throughout his life, humans have the ability to recognize tens to hundreds of faces. One of the biometric techniques that relate body measurements and calculations that are directly related to human characteristics is a system that can detect and identify faces. To be able to overcome various curren...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Khairun University, Faculty of Engineering, Department of Electrical Engineering
2024-01-01
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| Series: | Protek: Jurnal Ilmiah Teknik Elektro |
| Online Access: | https://ejournal.unkhair.ac.id/index.php/protk/article/view/4894 |
| _version_ | 1797367839750881280 |
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| author | Budi Cahyo Wibowo Imam Abdul Rozaq Andre Maulana Yusva |
| author_facet | Budi Cahyo Wibowo Imam Abdul Rozaq Andre Maulana Yusva |
| author_sort | Budi Cahyo Wibowo |
| collection | DOAJ |
| description | Throughout his life, humans have the ability to recognize tens to hundreds of faces. One of the biometric techniques that relate body measurements and calculations that are directly related to human characteristics is a system that can detect and identify faces. To be able to overcome various current problems, facial recognition is required through computer applications, including identification of criminals, development of security systems, image and film processing, and human-computer interaction. So the author makes a face processing system based on Raspberry Pi with the Local Binary Patterns Histogram (LBPH) method. In running a facial recognition system using a face, at the initial stage the process of sampling the face of the person who is the owner of the room access is carried out. Then from the face samples that have been obtained, the learning process is carried out by converting the image into digital values through the Local Binary Patterns Histogram method. This method reduces image data into simpler data, to speed up the face recognition process. The results of the test show that face recognition works as expected, even being able to detect at low light brightness values (≥6 lux) and even face recognition accuracy of 79.15%. For face data that has been through the learning process, the face can be recognized, then the recognized face data is stored in a directory. |
| first_indexed | 2024-03-08T17:23:53Z |
| format | Article |
| id | doaj.art-9433fd9c495047a097b95158fdb551d7 |
| institution | Directory Open Access Journal |
| issn | 2354-8924 2527-9572 |
| language | English |
| last_indexed | 2024-03-08T17:23:53Z |
| publishDate | 2024-01-01 |
| publisher | Khairun University, Faculty of Engineering, Department of Electrical Engineering |
| record_format | Article |
| series | Protek: Jurnal Ilmiah Teknik Elektro |
| spelling | doaj.art-9433fd9c495047a097b95158fdb551d72024-01-03T03:02:01ZengKhairun University, Faculty of Engineering, Department of Electrical EngineeringProtek: Jurnal Ilmiah Teknik Elektro2354-89242527-95722024-01-01111131910.33387/protk.v11i1.48943486Face Recognition Using Local Binary Patterns Histogram Method Using Raspberry PIBudi Cahyo Wibowo0Imam Abdul Rozaq1Andre Maulana Yusva2Universitas Muria KudusUniversitas Muria KudusUniversitas Muria KudusThroughout his life, humans have the ability to recognize tens to hundreds of faces. One of the biometric techniques that relate body measurements and calculations that are directly related to human characteristics is a system that can detect and identify faces. To be able to overcome various current problems, facial recognition is required through computer applications, including identification of criminals, development of security systems, image and film processing, and human-computer interaction. So the author makes a face processing system based on Raspberry Pi with the Local Binary Patterns Histogram (LBPH) method. In running a facial recognition system using a face, at the initial stage the process of sampling the face of the person who is the owner of the room access is carried out. Then from the face samples that have been obtained, the learning process is carried out by converting the image into digital values through the Local Binary Patterns Histogram method. This method reduces image data into simpler data, to speed up the face recognition process. The results of the test show that face recognition works as expected, even being able to detect at low light brightness values (≥6 lux) and even face recognition accuracy of 79.15%. For face data that has been through the learning process, the face can be recognized, then the recognized face data is stored in a directory.https://ejournal.unkhair.ac.id/index.php/protk/article/view/4894 |
| spellingShingle | Budi Cahyo Wibowo Imam Abdul Rozaq Andre Maulana Yusva Face Recognition Using Local Binary Patterns Histogram Method Using Raspberry PI Protek: Jurnal Ilmiah Teknik Elektro |
| title | Face Recognition Using Local Binary Patterns Histogram Method Using Raspberry PI |
| title_full | Face Recognition Using Local Binary Patterns Histogram Method Using Raspberry PI |
| title_fullStr | Face Recognition Using Local Binary Patterns Histogram Method Using Raspberry PI |
| title_full_unstemmed | Face Recognition Using Local Binary Patterns Histogram Method Using Raspberry PI |
| title_short | Face Recognition Using Local Binary Patterns Histogram Method Using Raspberry PI |
| title_sort | face recognition using local binary patterns histogram method using raspberry pi |
| url | https://ejournal.unkhair.ac.id/index.php/protk/article/view/4894 |
| work_keys_str_mv | AT budicahyowibowo facerecognitionusinglocalbinarypatternshistogrammethodusingraspberrypi AT imamabdulrozaq facerecognitionusinglocalbinarypatternshistogrammethodusingraspberrypi AT andremaulanayusva facerecognitionusinglocalbinarypatternshistogrammethodusingraspberrypi |