{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T17:29:16Z","timestamp":1791739756333,"version":"4.3.4"},"reference-count":25,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T00:00:00Z","timestamp":1769644800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Car accidents remain a leading cause of death worldwide, with drowsiness and distraction accounting for roughly 25% of fatal crashes in Ecuador. This study presents a real-time driver monitoring system that uses computer vision and deep learning to detect fatigue, distraction, and emotions from facial expressions. It combines a MobileNetV2-based CNN trained on RAF-DB for emotion recognition and MediaPipe\u2019s 468 facial landmarks to compute the EAR (Eye Aspect Ratio), the MAR (Mouth Aspect Ratio), the gaze, and the head pose. Tests with 27 participants in both real and simulated driving environments showed strong results. There was a 100% accuracy in detecting distraction, 85.19% for yawning, and 88.89% for eye closure. The system also effectively recognized happiness (100%) and anger\/disgust (96.3%). However, it struggled with sadness and failed to detect fear, likely due to the subtlety of real-world expressions and limitations in the training dataset. Despite these challenges, the results highlight the importance of integrating emotional awareness into driver monitoring systems, which helps reduce false alarms and improve response accuracy. This work supports the development of lightweight, non-invasive technologies that enhance driving safety through intelligent behavior analysis.<\/jats:p>","DOI":"10.3390\/s26030889","type":"journal-article","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T12:49:07Z","timestamp":1769690947000},"page":"889","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Driver Monitoring System Using Computer Vision for Real-Time Detection of Fatigue, Distraction and Emotion via Facial Landmarks and Deep Learning"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-4978-1521","authenticated-orcid":false,"given":"Tamia","family":"Zambrano","sequence":"first","affiliation":[{"name":"Departamento de Automatizaci\u00f3n y Control Industrial, Escuela Polit\u00e9cnica Nacional, Quito 170525, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3247-4633","authenticated-orcid":false,"given":"Luis","family":"Arias","sequence":"additional","affiliation":[{"name":"Departamento de Automatizaci\u00f3n y Control Industrial, Escuela Polit\u00e9cnica Nacional, Quito 170525, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9016-947X","authenticated-orcid":false,"given":"Edgar","family":"Haro","sequence":"additional","affiliation":[{"name":"Departamento de Automatizaci\u00f3n y Control Industrial, Escuela Polit\u00e9cnica Nacional, Quito 170525, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6616-3193","authenticated-orcid":false,"given":"Victor","family":"Santos","sequence":"additional","affiliation":[{"name":"Departamento de Automatizaci\u00f3n y Control Industrial, Escuela Polit\u00e9cnica Nacional, Quito 170525, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7250-6479","authenticated-orcid":false,"given":"Mar\u00eda","family":"Trujillo-Guerrero","sequence":"additional","affiliation":[{"name":"Departamento de Automatizaci\u00f3n y Control Industrial, Escuela Polit\u00e9cnica Nacional, Quito 170525, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,29]]},"reference":[{"key":"ref_1","unstructured":"Ecuavisa (2025, July 31). Una Persona Muere Cada Cuatro Horas por Accidentes de Tr\u00e1nsito en Ecuador. Available online: https:\/\/www.ecuavisa.com\/ecuador\/una-persona-muere-cada-cuatro-horas-por-accidentes-de-transito-en-ecuador-20250712-0013.html."},{"key":"ref_2","first-page":"1","article-title":"Driver Drowsiness Detection","volume":"9","author":"Prasanthi","year":"2025","journal-title":"Int. J. Sci. Res. Eng. Manag. (IJSREM)"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"179396","DOI":"10.1109\/ACCESS.2019.2958667","article-title":"A Real-time Driving Drowsiness Detection Algorithm with Individual Differences Consideration","volume":"7","author":"You","year":"2019","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Safarov, F., Akhmedov, F., Abdusalomov, A.B., Nasimov, R., and Cho, Y.I. (2023). Real-Time Deep Learning-Based Drowsiness Detection: Leveraging Computer-Vision and Eye-Blink Analyses for Enhanced Road Safety. Sensors, 23.","DOI":"10.3390\/s23146459"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"93075","DOI":"10.1109\/ACCESS.2024.3423723","article-title":"Biosignals Monitoring for Driver Drowsiness Detection Using Deep Neural Networks","volume":"12","author":"Alguindigue","year":"2024","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"S157","DOI":"10.1080\/15389588.2019.1622005","article-title":"The Detection of Drowsiness Using a Driver Monitoring System","volume":"20","author":"Schwarz","year":"2019","journal-title":"Traffic Inj. Prev."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"D\u00edaz-Santos, S., Cigala-\u00c1lvarez, \u00d3, Gonzalez-Sosa, E., Caballero-Gil, P., and Caballero-Gil, C. (2024). Driver Identification and Detection of Drowsiness while Driving. Appl. Sci., 14.","DOI":"10.3390\/app14062603"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Beles, H., Vesselenyi, T., Rus, A., Mitran, T., Scurt, F.B., and Tolea, B.A. (2024). Driver Drowsiness Multi-Method Detection for Vehicles with Autonomous Driving Functions. Sensors, 24.","DOI":"10.3390\/s24051541"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201323). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_10","unstructured":"Alok, S. (2025, August 01). RAF-DB Dataset. Available online: https:\/\/www.kaggle.com\/datasets\/shuvoalok\/raf-db-dataset."},{"key":"ref_11","unstructured":"Mashhour, Z. (2025, August 17). Driver Inattention Detection Dataset. Available online: https:\/\/www.kaggle.com\/datasets\/zeyad1mashhour\/driver-inattention-detection-dataset."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Dewi, C., Chen, R.C., Chang, C.W., Wu, S.H., Jiang, X., and Yu, H. (2022). Eye Aspect Ratio for Real-Time Drowsiness Detection to Improve Driver Safety. Electronics, 11.","DOI":"10.3390\/electronics11193183"},{"key":"ref_13","unstructured":"Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C., Yong, M., and Lee, J. (2019). MediaPipe: A Framework for Building Perception Pipelines. arXiv."},{"key":"ref_14","first-page":"891","article-title":"Online proctoring system using image processing and machine learning","volume":"6","author":"Gopalakrishnan","year":"2022","journal-title":"Int. J. Health Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Selva, P., Morlier, J., and Gourinat, Y. (2009). Development of a Dynamic Virtual Reality Model of the Inner Ear Sensory System as a Learning and Demonstrating Tool. Model. Simul. Eng., 2009.","DOI":"10.1155\/2009\/245606"},{"key":"ref_16","first-page":"677","article-title":"Real-Time Smart Driver Sleepiness Detection by Eye Aspect Ratio Using Computer Vision","volume":"34","author":"Yuen","year":"2024","journal-title":"Indones. J. Electr. Eng. Comput. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.aap.2017.10.006","article-title":"Aggression, emotional self-regulation, attentional bias, and cognitive inhibition predict risky driving behavior","volume":"109","author":"Sani","year":"2017","journal-title":"Accid. Anal. Prev."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Vinoth, T., Arivalahan, R., Madhanamohan, K., Dilliganesh, V., and Kannappan, A. (2022). A drowsiness detection using smart sensors during driving and smart message alert system to avoid accidents. AIP Conf. Proc., 2519.","DOI":"10.1063\/5.0112049"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"222","DOI":"10.32628\/IJSRSET2512335","article-title":"Real-Time Driver Drowsiness Detection Using Deep Learning and Computer Vision Techniques","volume":"12","author":"Madane","year":"2025","journal-title":"Int. J. Sci. Res. Sci. Eng. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1641","DOI":"10.2147\/NSS.S376755","article-title":"Automatic Driver Drowsiness Detection Using Artificial Neural Network Based on Visual Facial Descriptors: Pilot Study","volume":"14","author":"Inkeaw","year":"2022","journal-title":"Nat. Sci. Sleep"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Xia, S., Xu, X., Zhang, L., Yan, H., Xu, Y., and Zhang, Z. (2020). Driver Distraction Detection Method Based on Continuous Head Pose Estimation. Comput. Intell. Neurosci., 2020.","DOI":"10.1155\/2020\/9606908"},{"key":"ref_22","first-page":"480","article-title":"Smart Driver Monitoring System using Deep Learning","volume":"4","author":"Velusamy","year":"2024","journal-title":"Int. J. Adv. Res. Sci. Commun. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"187192","DOI":"10.1109\/ACCESS.2024.3489220","article-title":"Deep Learning-Based Driver Activity Recognition Using Diverse Driver Profiles","volume":"12","author":"Obaid","year":"2024","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Shahverdy, M., Fathy, M., Berangi, R., and Sabokrou, M. (2020). Driver behavior detection and classification using deep convolutional neural networks. Expert Syst. Appl., 149.","DOI":"10.1016\/j.eswa.2020.113240"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lin, X., Huang, Z., Ma, W., and Tang, W. (2025). EEG-based driver drowsiness detection based on simulated driving environment. Neurocomputing, 616.","DOI":"10.1016\/j.neucom.2024.128961"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/3\/889\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T12:50:31Z","timestamp":1769691031000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/3\/889"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,29]]},"references-count":25,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["s26030889"],"URL":"https:\/\/doi.org\/10.3390\/s26030889","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,29]]}}}