Eye Statement Recognition for Driver Fatigue Detection Based on Gabor Wavelet and HMM

Abstract:

Eye statement is one of the most important factors reflecting driver fatigue. A novel eye statement recognition method for driver fatigue detection based on Gabor transformation and Hidden Markov Model is proposed in this paper, in which, the eye detection algorithm is borrowed from Zafer Savas' TrackEye software, and Gabor features, i.e. the eye state features, of the eye are extracted by using Gabor wavelet. After that, by using these features, the classifier is trained by HMM (Hidden Markov Model) to distinguish the eye states including fatigue and alert, then the consecutive five frames are considered to judge whether there exists driver fatigue or not. Simulation results show that the new method has good accuracy and effectiveness.

• Periodical: Applied Mechanics and Materials (Volumes 128-129)
• Pages: 123-129
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.128-129.123
• Online since: October 2011
• Authors: Hai Yan Yang, Xin Hua Jiang, Lei Wang, Yong Hui Zhang
• Keywords: Fatigue Monitoring, Gabor Translation, Hidden Markov Model (HMM)

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