Discrimination of Moderate and Acute Drowsiness Based on Spontaneous Facial Expressions

It is important for drowsiness detection systems to warn the driver before the critical stages of drowsiness arise and thus to provide sufficient intervention time for the driver. In our previous study, spontaneous facial expressions measured through computer vision techniques were used as an indicator to discriminate drowsy from alert episodes. Here we are extending it to separate moderate drowsiness and acute drowsiness. 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 with a set of 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 acute drowsy episodes. Moreover the study reveals new information in facial b...

• Type: Conference
• Year: 2010
• Where: ICPR
• Authors: Esra Vural, Marian Stewart Bartlett, Gwen Littlewort, Mujdat Cetin, Aytul Ercil, Javier Movellan
• Keywords: Acute Drowsiness, Acute Drowsy Episodes, Computer Vision, Drowsy, ICPR 2010