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Conference Paper
A Study on Feature Extraction Methods Used to Estimate a Driver’s Level of Drowsiness
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- Authors
- Kyong Hee Lee, Whui Kim, Hyun Kyun Choi, Byung Tae Jang
- Issue Date
- 2019-02
- Citation
- International Conference on Advanced Communications Technology (ICACT) 2019, pp.710-713
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- Recently, in addition to autonomous vehicle technology research and development, machine learning methods have been used to predict a driver's condition and emotions in order to provide information that will improve road safety. A driver's condition can be estimated not only by basic characteristics such as gender, age, and driving experience, but also by a driver's facial expressions, bio-signals, and driving behaviours. Recent developments in video processing using machine learning have enabled images obtained from cameras to be analysed with high accuracy. Therefore, based on the relationship between facial features and a driver's drowsy state, variables that reflect facial features have been established. In this paper, we proposed a method for extracting detailed features of the eyes, the mouth, and positions of the head using OpenCV and Dlib library in order to estimate a driver's level of drowsiness.
- KSP Keywords
- Autonomous vehicle, Basic characteristics, Driving experience, High accuracy, Machine Learning Methods, Recent developments, Road safety, Video processing, bio-signal, facial expression, facial features