Abstract: 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.

Key words: emotion recognition, human computer interaction, feature subset, artificial intelligence, kernel correlation analysis