Drowsiness Detection Using Facial Expression Features

Paper #:
  • 2010-01-0466

Published:
  • 2010-04-12
DOI:
  • 10.4271/2010-01-0466
Citation:
Hachisuka, S., Kimura, T., Ishida, K., Nakatani, H. et al., "Drowsiness Detection Using Facial Expression Features," SAE Technical Paper 2010-01-0466, 2010, https://doi.org/10.4271/2010-01-0466.
Pages:
11
Abstract:
This paper presents the method of detecting driver's drowsiness level from the facial expression. The motivation for this research is to realize the novel safety system which can detect the driver's slight drowsiness and keep the driver awake while driving. The brain wave is commonly used as the drowsiness index. However, it is not suitable for the in-vehicle system since it is measured with sensors worn over the head. We precisely investigated the relationship between the change of brain wave and other drowsiness indices that can be measured without any contact; PERCLOS, heart rate, lane deviation, and facial expression. We found that the facial expression index had the highest linear correlation with the brain wave. Therefore, we selected the facial expression as the drowsiness-detection index and automated the drowsiness detection from the facial expression. Three problems need to be solved for automation; (1) how to define the features of drowsy expression, (2) how to capture the features from the driver's video-recorded facial image, and (3) how to estimate the driver's drowsiness index from the features. First, we found that frontalis muscle, zygomaticus major muscle, and masseter muscle activated with increase of drowsiness in more than 75 percents of participants. According to the result, we determined the coordinates data of points on eyebrows, eyelids, and mouth as the features of drowsiness expression. Second, we calculated the 3D coordinates data of the features by image processing with Active Appearance Model (AAM). Third, we applied k-Nearest-Neighbor method to classify the driver's drowsiness level. Eleven participants' data of the features and the drowsiness level estimated by trained observers were used as the training data. We achieved the classification of the drivers' drowsiness in a driving simulator into 6 levels. The average Root Mean Square Errors (RMSE) among 12 participants was less than 1.0 level.
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