A study on tiredness assessment by using eye blink detection
In this paper, the loss of attention of automotive drivers is studied by using eye blink detection. Facial landmark detection for detecting eye is explored. Afterward, eye blink is detected using Eye Aspect Ratio. By comparing the time of eye closure to a particular period, the driver’s tiredness is...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2019
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| Online Access: | http://journalarticle.ukm.my/14813/ http://journalarticle.ukm.my/14813/1/04.pdf |
| _version_ | 1848813649678827520 |
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| author | Islam, Arafat Rahaman, Naimur Ahad, Md Atiqur Rahman |
| author_facet | Islam, Arafat Rahaman, Naimur Ahad, Md Atiqur Rahman |
| author_sort | Islam, Arafat |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | In this paper, the loss of attention of automotive drivers is studied by using eye blink detection. Facial landmark detection for detecting eye is explored. Afterward, eye blink is detected using Eye Aspect Ratio. By comparing the time of eye closure to a particular period, the driver’s tiredness is decided. The total number of eye blinks in a minute is counted to detect drowsiness. Calculation of total eye blinks in a minute for the driver is done, then compared it with a known standard value. If any of the above conditions fulfills, the system decides the driver is unconscious. A total of 120 samples were taken by placing the light source front, back, and side. There were 40 samples for each position of the light source. The maximum error rate occurred when the light source was placed back with a 15% error rate. The best scenario was 7.5% error rate where the light source was placed front side. The eye blinking process gave an average error of 11.67% depending on the various position of the light source. Another 120 samples were taken at a different time of the day for calculating total eye blink in a minute. The maximum number of blinks was in the morning with an average blink rate of 5.78 per minute, and the lowest number of blink rate was in midnight with 3.33% blink rate. The system performed satisfactorily and achieved the eye blink pattern with 92.7% accuracy. |
| first_indexed | 2025-11-15T00:21:33Z |
| format | Article |
| id | oai:generic.eprints.org:14813 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:21:33Z |
| publishDate | 2019 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:148132020-07-10T02:00:58Z http://journalarticle.ukm.my/14813/ A study on tiredness assessment by using eye blink detection Islam, Arafat Rahaman, Naimur Ahad, Md Atiqur Rahman In this paper, the loss of attention of automotive drivers is studied by using eye blink detection. Facial landmark detection for detecting eye is explored. Afterward, eye blink is detected using Eye Aspect Ratio. By comparing the time of eye closure to a particular period, the driver’s tiredness is decided. The total number of eye blinks in a minute is counted to detect drowsiness. Calculation of total eye blinks in a minute for the driver is done, then compared it with a known standard value. If any of the above conditions fulfills, the system decides the driver is unconscious. A total of 120 samples were taken by placing the light source front, back, and side. There were 40 samples for each position of the light source. The maximum error rate occurred when the light source was placed back with a 15% error rate. The best scenario was 7.5% error rate where the light source was placed front side. The eye blinking process gave an average error of 11.67% depending on the various position of the light source. Another 120 samples were taken at a different time of the day for calculating total eye blink in a minute. The maximum number of blinks was in the morning with an average blink rate of 5.78 per minute, and the lowest number of blink rate was in midnight with 3.33% blink rate. The system performed satisfactorily and achieved the eye blink pattern with 92.7% accuracy. Penerbit Universiti Kebangsaan Malaysia 2019-10 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/14813/1/04.pdf Islam, Arafat and Rahaman, Naimur and Ahad, Md Atiqur Rahman (2019) A study on tiredness assessment by using eye blink detection. Jurnal Kejuruteraan, 31 (2). pp. 209-214. ISSN 0128-0198 http://www.ukm.my/jkukm/volume-312-2019/ |
| spellingShingle | Islam, Arafat Rahaman, Naimur Ahad, Md Atiqur Rahman A study on tiredness assessment by using eye blink detection |
| title | A study on tiredness assessment by using eye blink detection |
| title_full | A study on tiredness assessment by using eye blink detection |
| title_fullStr | A study on tiredness assessment by using eye blink detection |
| title_full_unstemmed | A study on tiredness assessment by using eye blink detection |
| title_short | A study on tiredness assessment by using eye blink detection |
| title_sort | study on tiredness assessment by using eye blink detection |
| url | http://journalarticle.ukm.my/14813/ http://journalarticle.ukm.my/14813/ http://journalarticle.ukm.my/14813/1/04.pdf |