Driver Drowsiness Detection Based on Novel Eye Openness Recognition Method and Unsupervised Feature Learning

• Published in: 2015 IEEE International Conference on Systems, Man, and Cybernetics pp. 1470 - 1475
• Main Authors: Wei Han, Yan Yang, Guang-Bin Huang, Sourina, Olga, Klanner, Felix, Denk, Cornelia
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-10-2015
• Subjects: degree of eye openness; driver drowsiness detection; eyelid movements; Eyelids; Feature extraction; Iris recognition; Lighting; Manifolds; OLPP; unsupervised feature learning; Vehicles; Yttrium
• DOI: 10.1109/SMC.2015.260

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 vision based 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.