Real-time face detection on a Raspberry PI
The article describes the implementation of different face detection algorithms to capture human faces from real-time video frames using a Raspberry PI microprocessor. This article examines this issue, proposes the implementation of two distinct real-time face detection algorithms, and pres...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Information Technology Publishing House
2022-07-01
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| Series: | Problems of Information Society |
| Online Access: | https://jpis.az/uploads/article/en/2022_2/REAL-TIME_FACE_DETECTION_ON_A_RASPBERRY_PI.pdf |
| _version_ | 1797262548380155904 |
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| author | Leyla Muradkhanli Eshgin Mammadov |
| author_facet | Leyla Muradkhanli Eshgin Mammadov |
| author_sort | Leyla Muradkhanli |
| collection | DOAJ |
| description | The article describes the implementation of different face detection algorithms to capture human faces from real-time video frames using a Raspberry PI microprocessor. This article examines this issue, proposes the implementation of two distinct real-time face detection algorithms, and presents a comprehensive architectural design. Used methods include Haar Cascades which is known as Viola-Jones algorithm, and Histogram of Oriented Gradients + Linear Support Vector Machines algorithm. The algorithms are implemented with the help of the OpenCV and Dlib libraries, and the Python programming language was used to build the face detection system. The OpenCV and Dlib libraries include a large number of built-in packages that assist with face detection and conduct face operations separately, resulting in reduced processing time and increased efficiency overall. The results confirm that both methods can detect faces in real time with acceptable accuracy and computation time but there are several differences. The Histogram of Oriented Gradients + Linear Support Vector Machines algorithm.method is much more preferable in terms of accuracy, but the image pyramid construction will be computationally demanding. |
| first_indexed | 2024-04-24T23:58:52Z |
| format | Article |
| id | doaj.art-f507ea17ee0d449abf873a66222bd159 |
| institution | Directory Open Access Journal |
| issn | 2077-964X 2309-7566 |
| language | English |
| last_indexed | 2024-04-24T23:58:52Z |
| publishDate | 2022-07-01 |
| publisher | Information Technology Publishing House |
| record_format | Article |
| series | Problems of Information Society |
| spelling | doaj.art-f507ea17ee0d449abf873a66222bd1592024-03-14T10:41:24ZengInformation Technology Publishing HouseProblems of Information Society2077-964X2309-75662022-07-01132384510.25045/jpis.v13.i2.05Real-time face detection on a Raspberry PILeyla Muradkhanlihttps://orcid.org/0000-0001-6149-4698Eshgin Mammadov The article describes the implementation of different face detection algorithms to capture human faces from real-time video frames using a Raspberry PI microprocessor. This article examines this issue, proposes the implementation of two distinct real-time face detection algorithms, and presents a comprehensive architectural design. Used methods include Haar Cascades which is known as Viola-Jones algorithm, and Histogram of Oriented Gradients + Linear Support Vector Machines algorithm. The algorithms are implemented with the help of the OpenCV and Dlib libraries, and the Python programming language was used to build the face detection system. The OpenCV and Dlib libraries include a large number of built-in packages that assist with face detection and conduct face operations separately, resulting in reduced processing time and increased efficiency overall. The results confirm that both methods can detect faces in real time with acceptable accuracy and computation time but there are several differences. The Histogram of Oriented Gradients + Linear Support Vector Machines algorithm.method is much more preferable in terms of accuracy, but the image pyramid construction will be computationally demanding.https://jpis.az/uploads/article/en/2022_2/REAL-TIME_FACE_DETECTION_ON_A_RASPBERRY_PI.pdf |
| spellingShingle | Leyla Muradkhanli Eshgin Mammadov Real-time face detection on a Raspberry PI Problems of Information Society |
| title | Real-time face detection on a Raspberry PI |
| title_full | Real-time face detection on a Raspberry PI |
| title_fullStr | Real-time face detection on a Raspberry PI |
| title_full_unstemmed | Real-time face detection on a Raspberry PI |
| title_short | Real-time face detection on a Raspberry PI |
| title_sort | real time face detection on a raspberry pi |
| url | https://jpis.az/uploads/article/en/2022_2/REAL-TIME_FACE_DETECTION_ON_A_RASPBERRY_PI.pdf |
| work_keys_str_mv | AT leylamuradkhanli realtimefacedetectiononaraspberrypi AT eshginmammadov realtimefacedetectiononaraspberrypi |