Visual Based Drowsiness Detection Using Facial Features
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In this paper, a camera-based system is proposed to detect and monitor drowsiness of a car driver in real time. The system utilizes an RGB image to track the drivers’ face and their eyes to detect sleepy sign. For the face detection and segmentation, a robust method based on Haar features is applied. Within the segmented areas of faces, random forest is utilized to locate eye regions. Once the eyes are located, the local region of eyes is extracted to yield binary images of the eye silhouettes in which the open and close stages of the eyes are revealed. The portion of the close states of the eyes during a certain number of frames is calculated to track the drowsiness signs. If this portion exceeds a predefined threshold, the system concludes that the driver tends to falling asleep and generate alert to the users.
KeywordsHuman-machine interface Drowsiness monitoring Face detection Decision tree
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This research is funded by Newton Research Collaboration Programme, reference number NRCP1516/1/74.
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