Abstract
Driver fatigue causes tragic events and hazardous consequences in transportation systems. Especially, in developing countries, drivers have longer working hours and drive longer distances with short breaks to gain more money. This paper develops a new real time, low cost, and non-intrusive system that detects the features of fatigue drivers. It detects cues from the face of people who didn’t sleep the right hours or who have over worked. It identifies eye-related cues such as red eyes and skin-related cues who have like dark areas under the eye. This work uses CascadeObjectDetector that supports the Haar Cascade Classifier, Local Binary Patterns (LBP) and Histograms of Oriented Gradients (HOG) to develop a new algorithm, and locates the areas under the eye and reference areas on the face to compare the skin color tone. It uses the semi-supervised anomaly detection algorithm to recognize abnormality of the area under the eyes and eye redness. The system was evaluated with 7 participants with different skin colors and various light conditions. The results are very promising. The accuracy is quite high. All cues are detected correctly.
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Nosseir, A., El-sayed, M.E. (2021). Detecting Cues of Driver Fatigue on Facial Appearance. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1250. Springer, Cham. https://doi.org/10.1007/978-3-030-55180-3_54
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