Blink detection using Adaboost and contour circle for fatigue recognition. (February 2017)

• Record Type: Journal Article
• Authors: Wang, Mei; Guo, Lin; Chen, Wen-Yuan
• Is Part Of: Computers & electrical engineering. Volume 58(2017)
• Page Start: 502
• Page End: 512
• Publication Date: 2017-02
• Subjects: Blink detection -- Adaboost -- Grid method -- Least squared method -- Contour circle -- Feature vector -- Threshold criterion -- Fatigue recognition; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2016.09.008
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: A recognition algorithm is proposed for fatigue blink recognition. A contour circle of the upper eyelid is created. The vertical coordinate of the contour circle center is the best feature to classify whether eyes are in open state or closed state with a linear classifier. The feature vector of the contour circle can classify the eye states by nonlinear decision surfaces. Graphical abstract: Abstract: Aiming at the problem of traffic accidents, an Adaboost and Contour Circle (ACC) algorithm is developed based on a traditional Adaboost method and the proposed contour circle (CC) for recognizing whether eyes are in open state or closed state. First, Adaboost method is used to detect human faces and eye regions. Second, the pixels of the pupil region are removed by the given grid method. Third, the least squares method is utilized to fit the CC of the upper eyelid. Fourth, the center and radius of the CC are extracted as the feature vector. Finally, the eyes state is recognized according to the defined threshold. It is experimentally proved that the vertical coordinate of the CC is the best feature which can classified whether the eyes are in open state or closed state by the linear decision surface. Besides, the feature vector can classify the eyes states by nonlinear decision surfaces. The correct ratio of the blink detection is 96.6%, and the fatigue blink recognition accuracy is 91.5%.