An eye state identification method based on the Embedded Hidden Markov Model

机译：基于嵌入式隐马尔可夫模型的眼睛状态识别方法

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This paper focuses on improving the accuracy and the speed of eye state identification, a novel method based on EHMM (Embedded Hidden Markov Model) was proposed. We extract the 2D-DCT feature of each eye image, use the low-frequency coefficients of the DCT to generate observation vector, then train the model according to the EHMM training algorithm and get classifiers. Experiment results show that when the sampling window to take 12×12, and the number of Gaussian Mixture Models to take 3, we achieve a satisfactory result. Comparing with other methods, the method presented in this paper is not sensitive to deflection angles of face and illumination. The recognition speed can be up to 20 frames/ sec so that it can be used in real system.

机译：本文着眼于提高眼睛状态识别的准确性和速度，提出了一种基于EHMM（Embedded Hidden Markov Model）的新方法。我们提取每个眼睛图像的2D-DCT特征，使用DCT的低频系数生成观察矢量，然后根据EHMM训练算法训练模型并获得分类器。实验结果表明，当采样窗口取12×12，高斯混合模型数取3时，取得了满意的结果。与其他方法相比，本文提出的方法对面部和照明的偏转角不敏感。识别速度可以高达20帧/秒，因此可以在实际系统中使用。

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来源
《2012 IEEE International Conference on Vehicular Electronics and Safety》|2012年|p.255- 260|共6页
会议地点 Istanbul(TR)
作者
Huabiao Qin; Jun Liu; Tianyi Hong;
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作者单位

School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China 510640;

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原文格式 PDF
正文语种 eng
中图分类汽车结构部件;
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An eye state identification method based on the Embedded Hidden Markov Model

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