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A Method for Identifying Fatigue State of Driver's Face Based on Improved AAM Algorithm
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Author(s): Zuojin Li (Chongqing University of Science and Technology, China), Jun Peng (Chongqing University of Science and Technology, China), Liukui Chen (Chongqing University of Science and Technology, China), Ying Wu (Chongqing University of Science and Technology, China)and Jinliang Shi (Chongqing University of Science and Technology, China)
Copyright: 2020
Pages: 16
Source title:
Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-0414-7.ch070
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Abstract
The change of lighting conditions and facial pose often affects the driver's face's video registration greatly, which affects the recognition accuracy of the driver's fatigue state. In this paper, the authors first analyze the reasons for the failure of the driver's face registration in the light conditions and the changes of facial gestures, and propose an adaptive AAM (Active Appearance Model) algorithm of adaptive illumination and attitude change. Then, the SURF (speeded up robust feature) feature extraction is performed on the registered driver's face video images, and finally the authors input the extracted SURF feature into the designed artificial neural network to realize the recognition of driver's fatigue state. The experimental results show that the improved AAM method can better adapt to the driver's face under the illumination and attitude changes, and the driver's facial image's SURF feature is more obvious. The average correct recognition rate of the driver's fatigue states is 92.43%.
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