基于HMM的无溢出高维样本集正态归整方法

An HMM Based Normality Processing Method without Overflow for High Dimensional Sample Set

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.

Aiming at the overflow of the hidden Markov model （HMM） observation probability, a method is proposed, called Normality Processing. Firstly, the chi-square plot is used to test normality of the sample set, the transformation of square root is performed. The feasibility of the proposed method is validated on the expression sequences database of CED-WYU （1.0） and Cohn-Kanade （CMU）. The person-independent expression recognition experiment is made with continuous HMM based on the optical flow features and a better result is obtained when the normality processing is used.