基于改进MMI的HMM训练算法及其在面部表情识别中的应用

提出一种改进的最大互信息(MMI)准则函数并把它应用于隐马尔可夫模型(HMM)的参数估计,重新推导了HMM的迭代公式. 该准则函数相对于原来准则函数定义更为合理,能有效利用训练样本集中的鉴别信息,使得训练数据得到充分利用,提高了HMM的性能. 把这种改进的HMM算法应用于面部表情识别,利用改进的光流算法提取面部表情特征向量序列,并利用改进HMM算法和BP神经网络构建了面部表情混合分类器. 实验结果表明了该方法能有效提高面部表情识别率,有效解决HMM参数估计问题.

HMM training algorithm based improved MMI and its application in facial expression recognition

A new approach for hidden Markov model(HMM)training based on an improved maximum mutual information(MMI)criterion was presented and HMM parameter adjustment rules were induced.By adopting a more realistic MMI definition,discriminative information contained in the training data could be used to improve the performance of HMM and this method was also used in facial expression recognition.Facial expression feature vector flows were extracted by using the improved optical flow algorithm,and a hybrid classifier based on the improved HMM and BP neural network was designed.Experimental results show that the new method provides satisfactory recognition performance and the method is powerful for HMM parameter estimation.