Recognition System for Fatigue Facial Expression of Driver-Reference-Cited by-同舟云学术

Recognition System for Fatigue Facial Expression of Driver

Published:2014-09 Issue: Volume:644-650 Page:4174-4177
ISSN:1662-7482
Container-title:Applied Mechanics and Materials
language:
Short-container-title:AMM

Author:

Wang Xue Mei¹,Zhang Jia Jun¹

Affiliation:

1. Zaozhuang University

Abstract

In order to improve the accuracy of recognition system for fatigue facial expression of driver, driver fatigue expression of this paper, the detection method for key feature points in the fatigue facial expression image of driver is applied in the paper to establish a fatigue expression image recognition model based on attention mechanism. Experimental results show that the algorithm can improve the recognition rate of driver's expression image, so as to record fatigue expression image of driver more accurate.

Publisher

Trans Tech Publications, Ltd.

Link

https://www.scientific.net/AMM.644-650.4174.pdf

Reference6 articles.

1. LianJinyi, Hua Xiaoyang. Study of mathematics model for rear-end collision avoidance system [J]. China journal of highway and transport. 2004, 35(4): 5-8.

2. Yang Wei, Wei Lang, Gong Jianqiang, et al. Research on Detection of Longitudinal Vehicle Spacing Based on Monocular Vision [J]. Computer Measurement & Control, 2012, 20(8): 2039-(2041).

3. Cheng Jinyong, Fan Yanbin, Song Jie, et al. Images Edge Detection Based on Wavelets Transform and Snake Model [J]. Journal of Qingdao University (natural sciences), 2005(3): 78-81.

4. Li Jianhua, Ma Xiaomei. Binarization method of fingerprint images based on orientation and dynamic threshold [J]. Journal of Dalian university of technology. 2002, 42(5): 626—628.

5. Wen Xue. Based on a Random Image Sequence 3 D Face Recognition Algorithm[J]. Bulletin of Science and Technolog, 2012, 10(28): 141-143. (in Chinese).

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Evaluation Technology of Classroom Students’ Learning State Based on Deep Learning;Computational Intelligence and Neuroscience;2021-10-19