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Real Time Drivers Drowsiness Detection and alert System by Measuring EAR

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Rajneesh, Anudeep Goraya, Gurmeet Singh

Rajneesh, Anudeep Goraya and Gurmeet Singh. Real Time Drivers Drowsiness Detection and alert System by Measuring EAR. International Journal of Computer Applications 181(25):38-45, November 2018. BibTeX

	author = {Rajneesh and Anudeep Goraya and Gurmeet Singh},
	title = {Real Time Drivers Drowsiness Detection and alert System by Measuring EAR},
	journal = {International Journal of Computer Applications},
	issue_date = {November 2018},
	volume = {181},
	number = {25},
	month = {Nov},
	year = {2018},
	issn = {0975-8887},
	pages = {38-45},
	numpages = {8},
	url = {},
	doi = {10.5120/ijca2018918055},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


On road driver’s fatigue and drowsiness is contributing more than 30%[1] of reported road accidents. Driver drowsiness can be estimated by monitoring biomedical signals ,visual assessment of driver’s bio-behavior from face images , by monitoring drivers performance or by combines all above techniques. Proposed algorithm is based on live monitoring of EAR (Eye aspect Ratio) by application of Image processing. HD live video is decomposed in continues frames and facial landmarks has been detected using pre trained Neural Network based Dlib functions. Dlib functions are trained using HAAR Cascade algorithm. Intel’s Open source Image processing libraries (OPEN CV) is used as primary Image processing tool. Python Language is used as main codding language. EAR is calculated by calculating Euclidean distance between measured eye coordinates . Blink and microsleep detection mechanism is implemented by monitoring EAR against a threshold value. Blinks and drowsiness level are displayed on monitor screen with microsleep detection audio warning .


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EAR, Microsleep, Drowsiness, OPENCV, Dlib, Python.