Authors:
Nawal Alioua
1
;
Aouatif Amine
2
;
Driss Aboutajdine
1
and
Mohammed Rziza
1
Affiliations:
1
LRIT, associated unit to CNRST, Faculty of Sciences and Mohammed V-Agdal University, Morocco
;
2
Mohammed V-Agdal University and Ibn Tofail University, Morocco
Keyword(s):
Eye state analysis, Driver’s drowsiness detection, Iris detection, Circular Hough Transform.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Image and Video Analysis
;
Image Shape Analysis
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
Eye state analysis is critical step for drowsiness detection. In this paper, we propose a robust algorithm for eye state analysis, which we incorporate into a system for driver’s drowsiness detection to extract micro-sleep periods. The proposed system begins by face extraction using Support Vector Machine (SVM) face detector then a new approach for eye state analysis based on Circular Hough Transform (CHT) is applied on eyes extracted regions. Finally, we proceed to drowsy decision. This new system requires no training data at any step or special cameras. The tests performed to evaluate our proposed driver’s drowsiness detection system using real video sequences acquired by low cost webcam, show that the algorithm provides good results and can work in real-time.