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Eye Based Drowsiness Detection System for Driver

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Abstract

Most traffic accidents are caused by human error, i.e. drowsiness. A drowsiness detection system is then developed to respond to this situation. In this work, the drowsiness detection system is built through the OpenCV library by combining the Haar Cascade Classifier algorithm with Blur, Canny, and Contour function. Haar Cascade Classifier was used to detect areas of face and eyes whereas the combination of Blur, Canny, and Contour functions are used to detect the driver's eyes and analyze the opening or closing of the driver's eyes. The performance of the drowsiness detection system was tested through four variables; kernel size, threshold value, lighting condition (morning, noon, afternoon, and night), and eye's characteristic (eyeglasses or not). Based on the experiments, the best kernel size to detect the driver's eyes is 4,4. Then, the best lower threshold and upper thresholds are 70–110 and 210–240. Subsequently, the light conditions have a 20% error rate to the system. The eye's characteristic has a 16,7% error rate to the system.

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Acknowledgements

The author would like to thank the Directorate General of Higher Education and Research of Indonesia for supporting this research under PDUPT Grant No.417/UN2.R3.1/HKP.05.00/2018.

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Authors and Affiliations

  1. Department of Electrical Engineering, Universitas Indonesia, Depok, Indonesia

    Prima Dewi Purnamasari, Arie Kriswoyo, Anak Agung Putri Ratna & Dodi Sudiana

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  1. Prima Dewi Purnamasari
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  2. Arie Kriswoyo
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  4. Dodi Sudiana
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Correspondence to Prima Dewi Purnamasari.

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Purnamasari, P.D., Kriswoyo, A., Ratna, A.A.P. et al. Eye Based Drowsiness Detection System for Driver. J. Electr. Eng. Technol. 17, 697–705 (2022). https://doi.org/10.1007/s42835-021-00925-z

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  • DOI: https://doi.org/10.1007/s42835-021-00925-z

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