Driver Drowsiness Detection using Hybrid Algorithm

U. Poorna Lakshmi, P.V.S. Srinivas, S Shyam, Mallikarjun Reddy Muchintala, Viswanath Reddy Palugulla, Hemanth Yadav Mandra


In this work we focus on the discernment of sleepiness in drivers’ drowsiness proposing a hybrid algorithm which aims to confirm whether the driver's level of attention has decreased owing to a nap or any other medical issue, such as brain problems. Therefore, the proposed hybrid algorithm uses both Haarcascade classifier and Convolutional Neural Network (CNN) algorithm to detect drivers’ drowsiness. The driver's eyes will be monitored and an alert sound will be generated by Raspberry Pi module, but the face must be moving in real time, and the aspect ratio must be between 16:9 and 1.85:1. People often feel sleepy since activities like driving call for a proper mental state, and bad work-life balance has additional negative repercussions. When we give input through normal camera it analyses drivers state of eyes and mouth, actually it checks aspect ratio of eye. We proved in comparative trials that our hybrid algorithm beats current driving fatigue detection algorithms in speed as well as accuracy.


Convolutional Neural Network (CNN); Drowsiness Detection; Haar cascade Classifier; OpenCV


Su, Hong and Gangtie Zheng. “A Partial Least Squares Regression-Based Fusion Model for Predicting the Trend in Drowsiness.” IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 1085-1092, 38, 2008.

N. L. Fitriyani, C. -K. Yang and M. Syafrudin, "Real-time eye state detection system using haar cascade classifier and circular hough transform," 2016 IEEE 5th Global Conference on Consumer Electronics, Kyoto, Japan, pp. 1-3, 2016.

G. Zhenhai, L. DinhDat, H. Hongyu, Y. Ziwen and W. Xinyu, "Driver Drowsiness Detection Based on Time Series Analysis of Steering Wheel Angular Velocity," 2017 9th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), Changsha, China, pp. 99-101, 2017

Akrout, B., & Mahdi, “Yawning detection by the analysis of variational descriptor for monitoring driver drowsiness”. In 2016 International Image Processing, Applications and Systems (IPAS) (pp. 1-5), IEEE, 2016.

He, H., Zhang, X., Jiang, F., Wang, C., Yang, Y., Liu, W., & Peng, J, “A real-time driver fatigue detection method based on two-stage convolutional neural network”. IFAC-PapersOnLine, 53(2), 15374-15379, 2020.

L. B. Leng, L. B. Giin and W. -Y. Chung, "Wearable driver drowsiness detection system based on biomedical and motion sensors," 2015 IEEE SENSORS, Busan, Korea (South), pp. 1-4, 2015.

Wong, J. Y., & Lau, P. Y, “Real-time driver alert system using raspberry Pi”, ECTI Transactions on Electrical Engineering, Electronics, and Communications, 17(2), 193-203, 2019.

Deng, Wanghua and Ruoxue Wu. “Real-Time Driver-Drowsiness Detection System Using Facial Features.” IEEE Access 7, 118727-118738, 2019.

Xiong, Huiyuan, Xionglai Zhu and Rong-hui Zhang. “Energy Recovery Strategy Numerical Simulation for Dual Axle Drive Pure Electric Vehicle Based on Motor Loss Model and Big Data Calculation.” Complex, 4071743, 2018.

Jabbar, Rateb, Khalifa N. Al-Khalifa, Mohamed Kharbeche, Wael K. M. Alhajyaseen, Mohsen A. Jafari and Shan Jiang. “Real-time Driver Drowsiness Detection for Android Application Using Deep Neural Networks Techniques.” ANT/SEIT, 2018.

Arunasalam, M., Yaakob, N., Amir, A., Elshaikh, M., & Azahar, N. F, “Real-time drowsiness detection system for driver monitoring”. In IOP Conference Series: Materials Science and Engineering, IOP Publishing. Vol. 767, No. 1, p. 012066, 2020.

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.

Viola MJP, “Rapid object detection using a boosted cascade of simple features”. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, no. 1, pp 511–518, 2001.

Zhao, Zuopeng, Nana Zhou, Lan Zhang, Hualin Yan, Yi Xu, and Zhongxin Zhang. "Driver fatigue detection based on convolutional neural networks using EM-CNN." Computational intelligence and neuroscience 2020.

Chen, Long, Guojiang Xin, Yuling Liu, and Junwei Huang. "Driver fatigue detection based on facial key points and LSTM." Security and Communication Networks, 1-9, 2021.

Liu, Zhongmin, Yuxi Peng, and Wenjin Hu. "Driver fatigue detection based on deeply-learned facial expression representation." Journal of Visual Communication and Image Representation 71, 102723,2020.

Ed-Doughmi, Younes, Najlae Idrissi, and Youssef Hbali. "Real-time system for driver fatigue detection based on a recurrent neuronal network." Journal of imaging 6, no. 3, 2020.

Abtahi, S., Omidyeganeh, M., Shirmohammadi, S., and Hariri, B, “Yawdd: A yawning detection dataset”. In Proceedings of the 5th ACM Multimedia Systems Conference, 24–28. ACM, 2014.

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Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
ISSN 2089-3272

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