Computing non-contactable drowsiness monitoring system with mobile machine vision
This project proposes a human facial features detection based on color segmentation via skin color and Viola- Jones algorithm for real time application. YCbCr color space is used to detect the presence of skin in an image where the image is normalized, and luminance is removed to increase face detec...
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| Format: | Conference or Workshop Item |
| Language: | en en |
| Published: |
2022
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| Online Access: | https://eprints.ums.edu.my/id/eprint/38468/1/ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/38468/2/FULLTEXT.pdf https://eprints.ums.edu.my/id/eprint/38468/ https://ieeexplore.ieee.org/document/9936760 |
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| author | Alixson Polumpung Lorita Angeline Helen Sin Ee Chuo Tan, Min Keng Lim, Kit Guan Teo, Kenneth Tze Kin |
| author_facet | Alixson Polumpung Lorita Angeline Helen Sin Ee Chuo Tan, Min Keng Lim, Kit Guan Teo, Kenneth Tze Kin |
| author_sort | Alixson Polumpung |
| building | UMS Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Sabah |
| content_source | UMS Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | This project proposes a human facial features detection based on color segmentation via skin color and Viola- Jones algorithm for real time application. YCbCr color space is used to detect the presence of skin in an image where the image is normalized, and luminance is removed to increase face detection accuracy. The second method, Viola-Jones which use Haar feature to detect facial feature such as face and eye also developed and tested. To perform in real time detection, CamShift algorithm and template matching are used to track face and eyes sequentially in Android platform. Then, the real time detection and tracking are evaluated to assess its performance. Finally, the algorithm is applied to drowsiness detection using PERCLOS. |
| format | Conference or Workshop Item |
| id | my.ums.eprints-38468 |
| institution | Universiti Malaysia Sabah |
| language | en en |
| publishDate | 2022 |
| record_format | eprints |
| spelling | my.ums.eprints-384682024-05-13T07:23:41Z https://eprints.ums.edu.my/id/eprint/38468/ Computing non-contactable drowsiness monitoring system with mobile machine vision Alixson Polumpung Lorita Angeline Helen Sin Ee Chuo Tan, Min Keng Lim, Kit Guan Teo, Kenneth Tze Kin QA1-43 General This project proposes a human facial features detection based on color segmentation via skin color and Viola- Jones algorithm for real time application. YCbCr color space is used to detect the presence of skin in an image where the image is normalized, and luminance is removed to increase face detection accuracy. The second method, Viola-Jones which use Haar feature to detect facial feature such as face and eye also developed and tested. To perform in real time detection, CamShift algorithm and template matching are used to track face and eyes sequentially in Android platform. Then, the real time detection and tracking are evaluated to assess its performance. Finally, the algorithm is applied to drowsiness detection using PERCLOS. 2022-11-22 Conference or Workshop Item PeerReviewed text en https://eprints.ums.edu.my/id/eprint/38468/1/ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/38468/2/FULLTEXT.pdf Alixson Polumpung and Lorita Angeline and Helen Sin Ee Chuo and Tan, Min Keng and Lim, Kit Guan and Teo, Kenneth Tze Kin and UNSPECIFIED (2022) Computing non-contactable drowsiness monitoring system with mobile machine vision. In: 2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET), 13-15 September 2022, Kota Kinabalu, Malaysia. https://ieeexplore.ieee.org/document/9936760 |
| spellingShingle | QA1-43 General Alixson Polumpung Lorita Angeline Helen Sin Ee Chuo Tan, Min Keng Lim, Kit Guan Teo, Kenneth Tze Kin Computing non-contactable drowsiness monitoring system with mobile machine vision |
| title | Computing non-contactable drowsiness monitoring system with mobile machine vision |
| title_full | Computing non-contactable drowsiness monitoring system with mobile machine vision |
| title_fullStr | Computing non-contactable drowsiness monitoring system with mobile machine vision |
| title_full_unstemmed | Computing non-contactable drowsiness monitoring system with mobile machine vision |
| title_short | Computing non-contactable drowsiness monitoring system with mobile machine vision |
| title_sort | computing non-contactable drowsiness monitoring system with mobile machine vision |
| topic | QA1-43 General |
| url | https://eprints.ums.edu.my/id/eprint/38468/1/ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/38468/2/FULLTEXT.pdf https://eprints.ums.edu.my/id/eprint/38468/ https://ieeexplore.ieee.org/document/9936760 |
| url_provider | http://eprints.ums.edu.my/ |