Research on Emotion Classification Based on Clustering Algorithm

Emotion recognition, especially facial expression recognition (FER), has played a vital role in understanding human cognition. The current work focuses on the classification, learning and analysis of the six basic emotions (happy, sadness, fear, disgust, anger, and surprise) and other in-depth research fields. However, from the perspective of psychology, human emotions are subjective and complex, and the definition of emotion categories is also controversial, which has an important impact on the accuracy of the analysis results. This paper focuses on the basic issues of emotion classification, presets the position of complex emotions, and uses the improved k-means clustering algorithm to reclassify emotion categories with different emotions based on the subjective voting results of the FER+ face emotion data set. The recognition accuracy is used as objective data to classify the subjective emotion categories, and finally, the recognition accuracy of the emotion classification categories is used as a verification method to prove that the reclassified emotion categories can significantly improve the results of its classification, learning and analysis.