Han, WeiWeiHanYang, YanYanYangHuang, Guang-BinGuang-BinHuangSourina, OlgaOlgaSourinaKlanner, FelixFelixKlannerDenk, CorneliaCorneliaDenk2022-03-132022-03-132015https://publica.fraunhofer.de/handle/publica/39113910.1109/SMC.2015.260In 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.enface feature detectionfeature extractionmachine learningpattern recognitionBusiness Field: Virtual engineeringBusiness Field: Digital societyResearch Area: Human computer interaction (HCI)conference paper