Vision-Based Driver Drowsiness Detection Using Haar Cascade and Convolutional Neural Networks

Y Mohan Das; S Athiya Firdous

doi:https://doi.org/10.47001/CJESR/2026.306001

Authors

Y Mohan Das

Department of Computer Science Engineering (Data Science), GATES Institute of Technology, Gooty, Andhra Pradesh, India

S Athiya Firdous

Department of Computer Science Engineering (Data Science), GATES Institute of Technology, Gooty, Andhra Pradesh, India

Abstract

Driver drowsiness is one of the leading causes of road accidents worldwide, making the development of intelligent driver monitoring systems essential for improving road safety. This paper presents a vision-based driver drowsiness detection system using Haar Cascade classifiers and Convolutional Neural Networks (CNNs) to monitor the driver's alertness in real time. A webcam continuously captures the driver's facial features, and the Haar Cascade algorithm is employed to detect the face and eyes efficiently. The extracted eye images are then processed by a custom-designed CNN to classify the eye state as either open or closed. Based on the eye closure duration, blink frequency, yawning behavior, and head movement, the system determines the driver's level of drowsiness. When signs of fatigue exceed a predefined threshold, an audible alert is generated to warn the driver and help prevent potential accidents. The proposed approach provides a non-intrusive, real-time, and cost-effective solution with improved robustness under varying facial expressions and lighting conditions. The experimental results demonstrate that the system effectively detects driver drowsiness with high accuracy, making it suitable for integration into advanced driver assistance systems (ADAS) and intelligent transportation applications.

Keywords

Driver Drowsiness Detection Computer Vision Haar Cascade Classifier Convolutional Neural Network (CNN) Eye State Classification Fatigue Detection Facial Feature Detection Real-Time Monitoring Driver Monitoring System (DMS) Road Safety Deep Learning Advanced Driver Assistance Systems (ADAS).

Citation of this Article

Y Mohan Das, & S Athiya Firdous. (2026). Vision-Based Driver Drowsiness Detection Using Haar Cascade and Convolutional Neural Networks. Current Journal of Engineering and Science Research. 3(6), 1-9. Article DOI: https://doi.org/10.47001/CJESR/2026.306001

References

P. Viola and M. Jones, "Rapid Object Detection Using a Boosted Cascade of Simple Features," Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Kauai, HI, USA, vol. 1, pp. I-511-I-518, 2001.

P. Viola and M. Jones, "Robust Real-Time Face Detection," International Journal of Computer Vision, vol. 57, no. 2, pp. 137-154, 2004.

Y. LeCun, Y. Bengio, and G. Hinton, "Deep Learning," Nature, vol. 521, no. 7553, pp. 436-444, 2015.

A.Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," Advances in Neural Information Processing Systems (NeurIPS), vol. 25, pp. 1097-1105, 2012.

M. Ngxande, J. R. Tapamo, and M. Burke, "Driver Drowsiness Detection Using Behavioral Measures and Machine Learning Techniques: A Review of State-of-the-Art Techniques," Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobMech), pp. 156-161, 2017.

Y. Albadawi, M. Takruri, and M. Awad, "A Review of Recent Developments in Driver Drowsiness Detection Systems," Sensors, vol. 22, no. 5, Article 2069, 2022.

N. Kamarudin, N. A. Jumadi, N. L. Mun, N. C. Keat, A. H. K. Ching, W. M. H. Wan Mahmud, M. Morsin, and F. Mahmud, "Implementation of Haar Cascade Classifier and Eye Aspect Ratio for Driver Drowsiness Detection Using Raspberry Pi," Universal Journal of Electrical and Electronic Engineering, vol. 6, no. 5B, pp. 67-75, 2019.

M. Hashemi, A. Mirrashid, and A. B. Shirazi, "Driver Safety Development Real-Time Driver Drowsiness Detection System Based on Convolutional Neural Network," arXiv preprint arXiv:2001.05137, 2020.

S. Mujahidin, A. Ripaldi, B. Nugroho, and R. Paninggalih, "Development of Drowsiness Detection System for Drivers Using Haar Cascade Classifier and Convolutional Neural Network," International Journal of Computing Science and Applied Mathematics, vol. 10, no. 2, 2024.

R. M. Salman, M. Rashid, R. Roy, M. M. Ahsan, and Z. Siddique, "Driver Drowsiness Detection Using Ensemble Convolutional Neural Networks on YawDD," arXiv preprint arXiv:2112.10298, 2021.

Dr. Priya Gupta, Nidhi Saxena, Meetika Sharma, Jagriti Tripathi, Deep Neural Network for Human Face Recognition International Journal of Engineering and Manufacturing, vol.8, no.1, pp. 63-71. January 2018.

Jeyasekar A, Vivek Ravi Iyengar, Based on Behavioural Changes using ResNet , International Journal of Recent Technology and Engineering (IJRTE), vol. 8, no. 3, pp. 2 5-30, 2019.

Luigi Celona, Lorenzo Mammana, Simone Bianco, Raimondo Schettini, -Task CNN Framework for Driver Face Berlin, 2018.

Mandalapu Sarada Devi and Dr. Preeti R Bajaj, Detection Based on Eye Tracking, First International Conference on Emerging Trends in Engineering and Technology, vol.1, pp. 649-652, 2008.

Sanghyuk Park, Fei Pan, Sunghun Kang and Chang D. Yoo, Driver drowsiness detection system based on feature representation learning Springer International Publishing, Computer Vision ACCV 2016 Workshops.

Tawsin Uddin Ahmed ,Sazzad Hossain,Mohammad Shahadat Hossain,Raihan Ul Islam ,Karl Andersson, Facial Expression Recognition using Convolutional Neural Network with Data th International Conference on Informatics, Electronics & Vision (ICIEV), Washington, USA, 2019,

Upasana Sinha, Kamal K. Mehta, A.K. Shrivastava, Real Time Implementation for Monitoring Drowsiness Condition of a Train Driver using Brain Wave Sensor , International Journal of Computer Applications, vol. 139, no.9, pp. 25-30, 2016.

Xiaoxi Ma, Lap-Pui Chau and Kim--based Two- stream Convolutional Neural Networks for Driver Fatigue IEEE International Conference on Orange Technologies (ICOT), pp. 155-158, 2017.

Weiwei Zhang, Jinya Su Driver Yawning Detection based on Long , IEEE Symposium Series on Computational Intelligence (SSCI), Honolulu, HI, USA,2017.

Zhongke Gao , Xinmin Wang, Yuxuan Yang, Chaoxu Mu , Qing Cai, Weidong Dang , and Siyang Zuo EEG-Based Spatial-Temporal, IEEE Transactions on Neural networks and learning systems, vol. 30, no. 9, pp. 2755-2763, 2019.