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基于HMM的无溢出高维样本集正态归整方法*

, PP. 363-368

Keywords: 隐马尔柯夫模型(HMM),卡方图,正态性,表情识别

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Abstract:

提出一种使用卡方图对高维特征向量样本集进行正态评估,并通过平方根变换处理,使样本集更接近正态分布的方法,称为无溢出正态归整方法.该方法解决高维特征样本对隐马尔柯夫模型(HMM)输出概率的溢出问题,其可行性在CED-WYU(1.0)及Cohn-Kanade(CMU)表情序列库上得到验证.利用连续HMM进行的基于光流特征的非特定人脸表情识别实验,采用正态归整得到更好的结果.

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