Transactions on Machine Intelligence

Transactions on Machine Intelligence

Real-time Driver Drowsiness Detection Using Artificial Immune System

Document Type : Original Article

Authors
H. SoltaniZadeh 1
S. Bourbour 2
1 Assistant Professor, Department of Electronics, Faculty of Electrical Engineering and Computer Science, Semnan University, Semnan, Iran
2 Telecommunications Department, Faculty of Electrical Engineering and Computer Science, Semnan University, Semnan, Iran
10.47176/TMI.2018.219
Abstract
Driver drowsiness is considered one of the primary causes of traffic accidents. Drowsiness detection systems are typically categorized into two types: monitoring-based and vehicle motion-based systems, each with its own advantages and disadvantages. Monitoring-based methods, which utilize driver performance sensors, are considered more practical than other methods due to their non-intrusive nature. In this study, images of open and closed eyes are initially provided to the designed system for training purposes. Then, using the trained system, prolonged eye closure is detected. The images used for the training phase were collected from 5 individuals, with 40 pictures from each person, including images of the left and right eyes. The PCA algorithm is first used to extract features, and the data is then fed to various classification systems. For the testing phase, the same number of new images were used. Four classification methods Euclidean Distance, Max Likelihood, Neural Networks, and Artificial Immune System (AIS) as the proposed method were compared and evaluated. The results showed that the first two methods, despite not requiring a training phase, needed more testing time. Neural networks, despite shorter testing times, required significant training time. On the other hand, the Artificial Immune System required short times for both the training and testing phases, with no significant difference in the recognition accuracy across the methods.
Keywords
Artificial Immune System
Feature Extraction
Driver Drowsiness Detection
Neural Networks
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Transactions on Machine Intelligence
Volume 1, Issue 4
Autumn 2018
Pages 212-219

  • Receive Date 03 July 2018
  • Revise Date 28 September 2018
  • Accept Date 12 December 2018