A Comparative Evaluation of Driver Drowsiness Detection Techniques
Received: 26 January 2026 | Revised: 21 February 2026 and 9 March 2026 | Accepted: 13 March 2026 | Online: 21 April 2026
Corresponding author: Wedad M. Albalawi
Abstract
Drowsy driving is a leading cause of fatal road accidents, underscoring the need for accurate, efficient automated detection systems. Despite significant breakthroughs in deep learning approaches, there is still no clear consensus in the scientific community on whether end-to-end deep learning models or hybrid approaches, which decouple feature extraction from classification, offer more efficient performance. The present study aims to fill this gap by providing a comparative analysis of multiple algorithmic configurations, considering combinations of deep pre-trained feature extractors, such as Visual Geometry Group-16 (VGG16), Mobile Networks (MobileNet), and Extreme Inception (Xception), and traditional feature extractors, such as Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), Scale-Invariant Feature Transform (SIFT), and Gabor filters, with a wide variety of classifiers, such as machine learning-based classifiers like Support Vector Machines (SVMs), Random Forest (RF), and Naïve Bayes (NB), and deep learning-based classifiers like LeNet, VGG16, AlexNet, and Convolutional Neural Networks (CNNs). All experiments were conducted on three benchmark datasets: National Tsing Hua University-Driver Drowsiness Detection (NTHU-DDD), Yawning Detection Dataset (YAWDD), and Yawn and Eye Close. The experimental results reveal two main findings. First, hybrid architectures consistently outperform fully end-to-end deep models. For example, the VGG16 network combined with a LeNet classifier achieved 99.32% accuracy on the controlled YAWDD dataset and 98.69% on the real-world NTHU-DDD dataset, whereas MobileNet combined with SVM achieved 99.31% and 98.83%, respectively. Second, incorporating multimodal features (eye and mouth regions) significantly improves performance compared to using either modality alone, highlighting the importance of feature complementarity in drowsiness detection.
Keywords:
CNN, MobileNet, Xception, machine learning, feature extraction, driver drowsiness, deep learningDownloads
References
R. Kolb, "National Highway Traffic Safety Administration (NHTSA)," in Encyclopedia of Business Ethics and Society, 2455 Teller Road, Thousand Oaks, California 91320 United States: SAGE Publications, Inc., 2008.
Y. Liang, "Detecting driver distraction," Doctor of Philosophy, University of Iowa, Iowa City, IA, United States, 2009.
National Highway Traffic Safety Administration. "Drowsy Driving." NHTSA. [Online]. Available: https://www.nhtsa.gov/risky-driving/drowsy-driving.
M. Ramzan, H. U. Khan, S. M. Awan, A. Ismail, M. Ilyas, and A. Mahmood, "A Survey on State-of-the-Art Drowsiness Detection Techniques," IEEE Access, vol. 7, pp. 61904–61919, 2019.
S. Panda and M. Kolhekar, "Feature Selection for Driver Drowsiness Detection," in Proceedings of International Conference on Computational Intelligence and Data Engineering, 2019, vol. 28, pp. 127–140.
S. Bakheet and A. Al-Hamadi, "A Framework for Instantaneous Driver Drowsiness Detection Based on Improved HOG Features and Naïve Bayesian Classification," Brain Sciences, vol. 11, no. 2, Feb. 2021, Art. no. 240.
R. Chinthalachervu, I. Teja, M. Ajay Kumar, N. Sai Harshith, and T. Santosh Kumar, "Driver Drowsiness Detection and Monitoring System using Machine Learning," Journal of Physics: Conference Series, vol. 2325, no. 1, Aug. 2022, Art. no. 012057.
Y. Albadawi, A. AlRedhaei, and M. Takruri, "Real-Time Machine Learning-Based Driver Drowsiness Detection Using Visual Features," Journal of Imaging, vol. 9, no. 5, Apr. 2023, Art. no. 91.
G. Setyo Utomo, E. Rachmawati, and F. Sthevanie, "Fatigue Detection Through Car Driver’s Face Using Boosting Local Binary Patterns," Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 7, no. 5, pp. 988–995, Aug. 2023.
D. A. Navastara, W. Y. M. Putra, and C. Fatichah, "Drowsiness Detection Based on Facial Landmark and Uniform Local Binary Pattern," Journal of Physics: Conference Series, vol. 1529, no. 5, May 2020, Art. no. 052015.
B. K. Padamata and J. R. Singothu, "A Machine Learning Approach for Driver Drowsiness Detection," International Journal of Engineering Research and Applications, vol. 10, no. 11, pp. 58–65, Nov. 2020.
S. Chen, Z. Wang, and W. Chen, "Driver Drowsiness Estimation Based on Factorized Bilinear Feature Fusion and a Long-Short-Term Recurrent Convolutional Network," Information, vol. 12, no. 1, Dec. 2020, Art. no. 3.
M. Dua, Shakshi, R. Singla, S. Raj, and A. Jangra, "Deep CNN models-based ensemble approach to driver drowsiness detection," Neural Computing and Applications, vol. 33, no. 8, pp. 3155–3168, Apr. 2021.
I. Jahan et al., "4D: A Real-Time Driver Drowsiness Detector Using Deep Learning," Electronics, vol. 12, no. 1, Jan. 2023, Art. no. 235.
B. Akrout and S. Fakhfakh, "How to Prevent Drivers before Their Sleepiness Using Deep Learning-Based Approach," Electronics, vol. 12, no. 4, Feb. 2023, Art. no. 965.
R. K. Shukla, A. K. Tiwari, and A. K. Jha, "An Efficient Approach of Face Detection and Prediction of Drowsiness Using SVM," Mathematical Problems in Engineering, vol. 2023, no. 1, Jan. 2023, Art. no. 2168361.
O. Nasir et al., "SAFE-DRIVE-AI: A CNN–LSTM–Attention Framework for Drowsiness Detection," Engineering, Technology & Applied Science Research, vol. 15, no. 5, pp. 27594–27600, Oct. 2025.
S. Anber, W. Alsaggaf, and W. Shalash, "A Hybrid Driver Fatigue and Distraction Detection Model Using AlexNet Based on Facial Features," Electronics, vol. 11, no. 2, Jan. 2022, Art. no. 285.
S. E. Bekhouche, Y. Ruichek, and F. Dornaika, "Driver drowsiness detection in video sequences using hybrid selection of deep features," Knowledge-Based Systems, vol. 252, Sept. 2022, Art. no. 109436.
F. You, X. Li, Y. Gong, H. Wang, and H. Li, "A Real-time Driving Drowsiness Detection Algorithm With Individual Differences Consideration," IEEE Access, vol. 7, pp. 179396–179408, 2019.
V. Vijayan and K. P. Pushpalatha, "Fused Architecture with Enhanced Bag of Visual Words for Efficient Drowsiness Detection," Journal of Advanced Computational Intelligence and Intelligent Informatics, vol. 27, no. 2, pp. 182–189, Mar. 2023.
Drowsiness_dataset. (2018), D. Perumandla. [Online]. Available: https://www.kaggle.com/datasets/dheerajperumandla/drowsiness-dataset.
S. Abtahi, M. Omidyeganeh, S. Shirmohammadi, and B. Hariri, "YawDD: a yawning detection dataset," in Proceedings of the 5th ACM Multimedia Systems Conference, Singapore, Singapore, Mar. 2014, pp. 24–28.
C.-H. Weng, Y.-H. Lai, and S.-H. Lai, "Driver Drowsiness Detection via a Hierarchical Temporal Deep Belief Network," in Computer Vision – ACCV 2016 Workshops, vol. 10118, C.-S. Chen, J. Lu, and K.-K. Ma, Eds. Cham: Springer International Publishing, 2017, pp. 117–133.
yawn and eye close Computer Vision Model. (2023), lab502. [Online]. Available: https://universe.roboflow.com/lab502/yawn-and-eye-close-5kie4.
J. Jo, S. J. Lee, K. R. Park, I.-J. Kim, and J. Kim, "Detecting driver drowsiness using feature-level fusion and user-specific classification," Expert Systems with Applications, vol. 41, no. 4, pp. 1139–1152, Mar. 2014.
R. C. Gonzalez and R. E. Woods, Digital image processing, Fourth, Global edition. New York, New York: Pearson Education, 2018.
N. Dalal and B. Triggs, "Histograms of Oriented Gradients for Human Detection," in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 2005, vol. 1, pp. 886–893.
T. Ahonen, A. Hadid, and M. Pietikainen, "Face Description with Local Binary Patterns: Application to Face Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 2037–2041, Dec. 2006.
D. G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, Nov. 2004.
N. Mishra and A. Bhatt, "Feature Extraction Techniques in Facial Expression Recognition," in 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), May 2021, pp. 1247–1251.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, "MobileNetV2: Inverted Residuals and Linear Bottlenecks," in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 2018, pp. 4510–4520.
X. Ran, T. Yan, and T. Cai, "MobileNet for Differential Constellation Trace Figure," in 2021 13th International Conference on Communication Software and Networks (ICCSN), June 2021, pp. 168–172.
J. Liu, "VGG, MobileNet and AlexNet on Recognizing Skin Cancer Symptoms," in 2022 3rd International Conference on Electronic Communication and Artificial Intelligence (IWECAI), Jan. 2022, pp. 525–528.
F. Chollet, "Xception: Deep Learning with Depthwise Separable Convolutions," in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017, pp. 1800–1807.
C. Cortes and V. Vapnik, "Support-vector networks," Machine Learning, vol. 20, no. 3, pp. 273–297, Sept. 1995.
J. Gwak, A. Hirao, and M. Shino, "An Investigation of Early Detection of Driver Drowsiness Using Ensemble Machine Learning Based on Hybrid Sensing," Applied Sciences, vol. 10, no. 8, Apr. 2020, Art. no. 2890.
C. B. S. Maior, M. J. D. C. Moura, J. M. M. Santana, and I. D. Lins, "Real-time classification for autonomous drowsiness detection using eye aspect ratio," Expert Systems with Applications, vol. 158, Nov. 2020, Art. no. 113505.
F. A. Jokhio and A. Jokhio, "Image Classification using AlexNet with SVM Classifier and Transfer Learning," Journal of Information Communication Technologies and Robotic Applications, vol. 10, no.1, pp. 44–51, June 2019.
L. Chen, G. Xin, Y. Liu, and J. Huang, "Driver Fatigue Detection Based on Facial Key Points and LSTM," Security and Communication Networks, vol. 2021, pp. 1–9, June 2021.
K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition." arXiv, Apr. 2015.
A. Bhanja, D. Parhi, D. Gajendra, K. Sinha, and A. K. Sahoo, "Driver drowsiness shield (DDSH): a real-time driver drowsiness detection system," ROBOMECH Journal, vol. 12, no. 1, May 2025, Art. no. 18.
S. Essahraui et al., "Real-Time Driver Drowsiness Detection Using Facial Analysis and Machine Learning Techniques," Sensors, vol. 25, no. 3, Art. no. 812, Jan. 2025.
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Copyright (c) 2026 Wedad M. Albalawi, Sahar Sabbeh, Nuha Zamzami
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