Eye Fatigue Detection in Vehicle Drivers Based on Facial Landmarks Features

Fia Dumi Hasanatul Fikriyah, Anan Nugroho, Alfa Faridh Suni


Human factors are unavoidable in traffic accidents, so early detection is needed. Fatigue while driving can be interpreted as drowsiness or nervous breakdown. Therefore, vehicle drivers must be able to increase their vigilance. The occurrence's cause is mainly human negligence due to tired driving. When the body feels tired, it is difficult to stay awake and focus. For this reason, this study was undertaken to propose a comprehensive approach to explore parameters indicating eye fatigue or driver drowsiness.. Detection of eye fatigue in vehicle drivers uses a smartphone camera (droid cam) to acquire and detect the driver's eyes using face recognition. This application development uses OpenCV with the Python programming language. This research was conducted by detecting faces using the facial landmarks 68 algorithms and then looking for points around the eyes to calculate the eye aspect ratio. The results of the detection analysis on the eyes when open and closed indicate the rider's size is tired and buggy. The input from the program is the number of blinks of the rider's eyes every minute so that the quantity indicates the driver is tired, sleepy or fit. This figure indicates that motorists are sleepy and need to anticipate taking a short break to continue the journey later.


Face recognition, landmarks, eye fatigue, drowsiness, blinking

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Abusharha, A. A. (2017). Changes in blink rate and ocular symptoms during different reading tasks. Clinical optometry, 9, 133.

Ahmad, F. L., Nugroho, A., & Suni, A. F. (2021). Deteksi Pemakai Masker Menggunakan Metode Haar Cascade Sebagai Pencegahan COVID 19. Edu Elektrika Journal, 10(1), 13-18.

Benedetto, S., Pedrotti, M., Minin, L., Baccino, T., Re, A., & Montanari, R. (2011). Driver workload and eye blink duration. Transportation research part F: traffic psychology and behaviour, 14(3), 199-208. WHO. World Healt Day: Road Safety Is No Accident.2004"

DiCarlo, J. J., Zoccolan, D., & Rust, N. C. (2012). How does the brain solve visual object recognition? Neuron, 73(3), 415-434.

Feng, J., Guo, Z., Wang, J., & Dan, G. (2020). Using eye aspect ratio to enhance fast and objective assessment of facial paralysis. Computational and mathematical methods in medicine, 2020.

Kong, W., Zhou, L., Wang, Y., Zhang, J., Liu, J., & Gao, S. (2015). A system of driving fatigue detection based on machine vision and its application on smart device. Journal of Sensors, 2015.

Lin, L., Huang, C., Ni, X., Wang, J., Zhang, H., Li, X., & Qian, Z. (2015). Driver fatigue detection based on eye state. Technology and health care, 23(s2), S453-S463.

Maior, C. B. S., Moura, M. C., de Santana, J. M., do Nascimento, L. M., Macedo, J. B., Lins, I. D., & Droguett, E. L. (2018). Real-time SVM classification for drowsiness detection using eye aspect ratio. Probabilistic Safety Assessment and Management PSAM, 14(09.2018).

Maslikah, S., Alfita, R., & Ibadillah, A. F. (2019). Sistem Deteksi Kantuk Pada Pengendara Roda Empat Menggunakan Eye Blink Detection. SinarFe7, 2(1), 123-128.

Mehta, S., Dadhich, S., Gumber, S., & Jadhav Bhatt, A. (2019, February). Real-time driver drowsiness detection system using eye aspect ratio and eye closure ratio. In Proceedings of international conference on sustainable computing in science, technology and management (SUSCOM), Amity University Rajasthan, Jaipur-India.

Niu, G., & Wang, C. (2014). Driver fatigue features extraction. Mathematical problems in engineering, 2014.

Nugroho, A., Hidayat, R., Nugroho, H. A., & Debayle, J. (2021). Ultrasound object detection using morphological region-based active contour: an application system. International Journal of Innovation and Learning, 29(4), 412-430.Nugroho, A., Hidayat, R., Nugroho, H. A, Debayle, J. (2021). Ultrasound object detection using morphological region-based active contour: an application system. International Journal of Innovation and Learning, 2021.

Ryan, C., O’Sullivan, B., Elrasad, A., Cahill, A., Lemley, J., Kielty, P., Posch, C.& Perot, E. (2021). Real-time face & eye tracking and blink detection using event cameras. Neural Networks, 141, 87-97.

Sandberg, D., Anund, A., Fors, C., Kecklund, G., Karlsson, J. G., Wahde, M., & Åkerstedt, T. (2011). The characteristics of sleepiness during real driving at night—a study of driving performance, physiology and subjective experience. Sleep, 34(10), 1317-1325.

Schmidt, J., Laarousi, R., Stolzmann, W., & Karrer-Gauß, K. (2018). Eye blink detection for different driver states in conditionally automated driving and manual driving using EOG and a driver camera. Behavior research methods, 50(3), 1088-1101.

Shanmugarajah, S., Tharmaseelan, J., & Sivagnanam, L. (2020, December). AI Approach In Monitoring The Physical And Psychological State Of Car Drivers And Remedial Action For Safe Driving. In 2020 2nd International Conference on Advancements in Computing (ICAC) (Vol. 1, pp. 186-191). IEEE.

Zhuang, Q., Kehua, Z., Wang, J., & Chen, Q. (2020). Driver Fatigue Detection Method Based on Eye States Withwith Pupil and Iris Segmentation. IEEE Access, 8, 173440-173449.


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