计算机工程与应用Issue(15)：13-17,5.DOI:10.3778/j.issn.1002-8331.1402-0131
Gabor小波优化HMM算法的眼部疲劳状态识别
Gabor wavelet optimization and HMM algorithm in eye state fatigue recognition
摘要
Abstract
Distance education network learners can easily feel tired in the learning process due to the long-term lack of emotional interaction, and the learning fatigue usually presents by the eye state. To monitor the remote intelligent teaching system effectively, a kind of recognition algorithm of eye state in learning fatigue state is put forward based on Gabor wavelets and HMM. Due to the different characters of the eye openness degree in normal study, fatigue and confusion, the three learning states, the algorithm conducts gray difference processing to the eye image using Laplacian in YCbCr color space. It selects the second-dimension Gabor kernel function, constructing 48 most optimal filters, for 48 character-istic values. The 48 characteristic values will generate 48 characteristic vectors, and later HMM will be used to recog-nize the eye state of the eye by the set of observation sequence O formed by the characteristic vectors of the eye state image. The experimental result shows that the network learning fatigue recognition rate of this algorithm reaches 95.68%, with good robustness.关键词
学习疲劳/网络学习/Gabor小波/隐马尔可夫模型Key words
learning fatigue/E-learning/Gabor wavelet/Hidden Markov Model(HMM)分类
社会科学引用本文复制引用
杨秋芬,桂卫华,胡豁生,阳若宁..Gabor小波优化HMM算法的眼部疲劳状态识别[J].计算机工程与应用,2014,(15):13-17,5.基金项目
湖南省十二五规划课题（No.XJK013BXX006）；湖南省科技厅资助项目（No.2012GK3095）；湖南省教育厅资助项目（No.12C1158）。 （）
