基于核相关分析算法的情感识别模型

摘要

In view of the problems of low recognition accuracy and slow speed in current emotion recognition model,we designed an emotion recognition model based on kernel correlation analysis algorithm.Firstly,the current research status of emotion recognition was analyzed,and the causes of low recognition accuracy were found out.Secondly,the characteristics of emotion recognition was extracted,and the feature subset of emotion recognition was selected by kernel correlation analysis algorithm to reduce the number of feature vectors of emotion recognition.Finally,the Gauss mixture model was used to model the training set of emotion recognition,and the simulation experiments were carried out by the specific emotional data set. The experimental results show that the kernel correlation analysis algorithm can effectively remove the disadvantageous features of emotion recognition,accelerate the speed of emotion recognition, and improve the accuracy of emotion recognition.%针对目前情感识别模型中存在的识别精度低、速度慢等问题,设计一种基于核相关分析算法的情感识别模型.首先对目前情感识别的研究现状进行分析,找出导致识别精度低的原因;然后提取情感识别的特征,并通过核相关分析算法选择最优情感识别的特征子集,减少情感识别的特征向量数;最后选择高斯混合模型对情感识别的训练集进行建模,并通过具体情感数据集进行仿真实验.实验结果表明,核相关分析算法可有效去除情感识别的不利特征,加快了情感识别速度,提高了情感识别的正确率.