Discrimination of Moderate and Acute Drowsiness Based on Spontaneous Facial Expressions
Esra Vural, Marian Bartlett, Gwen Littlewort, Müjdat Çetin, Aytül Erçil, Javier Movellan
International Conference on Pattern Recognition, Istanbul, Turkey, August 2010
It is important for drowsiness detection systems to identify different levels of drowsiness and respond appropriately at each level. This study explores how to discriminate moderate from acute drowsiness by applying computer vision techniques to the human face. In our previous study, spontaneous facial expressions measured through computer vision techniques were used as an indicator to discriminate alert from acutely drowsy episodes. In this study we are exploring which facial muscle movements are predictive of moderate and acute drowsiness. The effect of temporal dynamics of action units on prediction performances is explored by capturing temporal dynamics using an overcomplete representation of temporal Gabor Filters. In the ﬁnal system we perform feature selection to build a classiﬁer that can discriminate moderate drowsy from acute drowsy episodes. The system achieves a classiﬁcation rate of .96 A’ in discriminating moderately drowsy versus acutely drowsy episodes. Moreover the study reveals new information in facial behavior occurring during different stages of drowsiness.