Application of Computer Vision Systems for Monitoring the Condition of Drivers Based on Facial Image Analysis

This article explores the use of computer vision for recognizing human fatigue by the eyes. The primary attention is paid to the development of a software package that can be used in the future as a system for monitoring the condition of drivers. Face detection is based on the Viola–Jones method and...

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Bibliographic Details
Published in:Pattern recognition and image analysis 2021-07, Vol.31 (3), p.489-495
Main Author: Andriyanov, N. A.
Format: Article
Language:English
Subjects:
Algorithms
Applied Problems
Artificial neural networks
Cameras
Computer Science
Computer vision
Condition monitoring
Eye (anatomy)
Face recognition
Fatigue
Image analysis
Image Processing and Computer Vision
Object recognition
Pattern Recognition
Programming languages
Software
Software packages
Vision systems
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Summary:This article explores the use of computer vision for recognizing human fatigue by the eyes. The primary attention is paid to the development of a software package that can be used in the future as a system for monitoring the condition of drivers. Face detection is based on the Viola–Jones method and Haar cascades. This allows the algorithm to work in real time. However, convolutional neural networks are used to recognize eye conditions. Such networks training takes place on the eyes cut out from images of faces. Learning occurs in two eye states: open and closed. Moreover, the left and right eyes are analyzed separately. Different illumination characteristics have resulted in different accuracy rates for each eye. To develop the program, the Python programming language was used, the Jupyter Notebook was chosen as the development environment, and OpenCV was used as the main library, since it allows us to receive and process data from a USB camera. The developed software package allows us to detect closed eyes with precision and recall of about 90%. This uses a simple camera with a low resolution of 640 × 480 pixels. The proposed algorithm requires an additional increase in the accuracy and completeness of recognition of the closed eyes.
ISSN:1054-6618
1555-6212
DOI:10.1134/S1054661821030020