Drowsiness Detection Based on Eye Closure and Yawning Detection
B. Mohana1, C. M. Sheela Rani2
1Dr. C. M. Sheela Rani, Department of CSE, Koneru Lakshmaiah Educational Foundation, Guntur, India.
2B. Mohana, Department of CSE, Koneru Lakshmaiah Educational Foundation, Guntur, India.

Manuscript received on November 15, 2019. | Revised Manuscript received on November 28, 2019. | Manuscript published on 30 November, 2019. | PP: 8941-8944 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9716118419/2019©BEIESP | DOI: 10.35940/ijrte.D9716.118419

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The number of major road accidents that occur per day is on a rise and most of them are attributed to being the driver’s fault. According to the survey done in 2015, drivers are held responsible for approximately 78% of the accidents. To minimize the occurrence of these incidents a monitoring system that alerts the driver when he succumbs to sleep is proposed. This algorithm processes live video feed focused on the driver’s face and tracks his eye and mouth movements to detect eye closure and yawning rates. An alarm sounds if the driver is drowsy or already asleep. Haar-cascade classifiers run parallelly on the extracted facial features to detect eye closure and yawning.
Keywords: Drowsiness Detection, Eye Closure Detection, Haar- Based Classifier, Yawn Detection.
Scope of the Article: Vision-based applications.