Implementation of driver's drowsiness assistance model based on eye movements detection

Authors

DOI:

https://doi.org/10.15587/1729-4061.2020.211755

Keywords:

driver's drowsiness, eye movements, Advanced LBP, Viola-Jones, NB, SVM

Abstract

Annually, lots of persons are carrying lifelong disabilities or losing their lives owing to fatal accidents on the road. In addition to mechanical failures and people errors, driver's drowsiness represents one of the fundamental reasons for fatal accidents on the road. When drivers feel drowsy, lots of physiological and behavioral symptoms appear such as changes in the waves of the human brain, changes in facial expressions, variations in eye activity, decreasing head movements, etc. Therefore, there is a significant necessity to provide developed models of driver's drowsiness detection that exploit these symptoms for reducing accidents by warning drivers of drowsiness and fatigue. This paper concentrates on proposing a driver's drowsiness assistance model to monitor and alarm drivers by utilizing a behavioral-based method (eye movements detection method). In the proposed method of detecting eye movements (closed/opened), the Advanced Local Binary Pattern (Advanced LBP) is used, in which the descriptors are utilized to represent eye images to extract the tissue features of different persons in the driving car to see if the driver is in a drowsy state or not and this occurs after recording the driver's video and detecting the eyes of the driver. To extract the features in this way, the image of the eyes is divided into small regions through the Advanced LBP and sequenced into a single feature vector, where this method is used to determine the similarity features in the training group and to classify the eye image. The Naive Bayes classifier (NB) and Support Vector Machine (SVM) are utilized for giving good accuracy. The results indicate that the system has a high accuracy rate compared with the other existing methods, where the accuracy rate of NB and SVM using an eye detection dataset with training 90 % and testing 10% is 96 % and 97 %, respectively

Supporting Agencies

  • Department of Computer Science
  • College of Science
  • University of Diyala
  • Iraq

Author Biographies

Jumana Waleed, University of Diyala Baquba, Diyala, Iraq

PhD, Assistant Professor

Department of Computer Science, College of Science

Thekra Abbas, Almustanseriah University Baghdad, Iraq

PhD, Assistant Professor, Head of Department

Department of Computer Science, College of Science

Taha Mohammed Hasan, University of Diyala Baquba, Diyala, Iraq

PhD, Assistant Professor, Head of Department

Department of Computer Science, College of Science

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Published

2020-10-31

How to Cite

Waleed, J., Abbas, T., & Mohammed Hasan, T. (2020). Implementation of driver’s drowsiness assistance model based on eye movements detection. Eastern-European Journal of Enterprise Technologies, 5(9 (107), 6–13. https://doi.org/10.15587/1729-4061.2020.211755

Issue

Vol. 5 No. 9 (107) (2020): Information and controlling system

Section

Information and controlling system

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Copyright (c) 2020 Jumana Waleed, Thekra Abbas, Taha Mohammed Hasan

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