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Driver drowsiness detection based on novel eye openness recognition method and unsupervised feature learning

: Han, Wei; Yang, Yan; Huang, Guang-Bin; Sourina, Olga; Klanner, Felix; Denk, Cornelia


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society:
IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015. Proceedings : Big Data Analytics for Human-Centric Systems, 9-12 October 2015, Kowloon, Hong Kong
Los Alamitos, Calif.: IEEE Computer Society Conference Publishing Services (CPS), 2015
ISBN: 978-1-4799-8696-5
ISBN: 978-1-4799-8697-2
International Conference on Systems, Man, and Cybernetics (SMC) <2015, Hong Kong>
Fraunhofer IDM@NTU ()
face feature detection; feature extraction; machine learning; pattern recognition; Business Field: Virtual engineering; Business Field: Digital society; Research Area: Human computer interaction (HCI)

In this paper, we proposed a driver drowsiness detection method for which only eyelid movement information was required. The proposed method consists of two major parts. 1) In order to obtain accurate eye openness estimation, a visionbased eye openness recognition method was proposed to obtain an regression model that directly gave degree of eye openness from a low-resolution eye image without complex geometry modeling, which is efficient and robust to degraded image quality. 2) A novel feature extraction method based on unsupervised learning was also proposed to reveal hidden pattern from eyelid movements as well as reduce the feature dimension. The proposed method was evaluated and shown good performance.