多维连续空间的多任务表情识别研究OA

A multi task emotion recognition model was designed to provide a more comprehensive and detailed emotion measurement tool for intelligent emotion interaction by combining Valence,Arousal,Dominance(VAD)three-dimensional continuous emotion analysis and dis-crete emotion classification.Firstly,utilize the relevant constraints between two recognition tasks(labeled as points in the VAD three-dimen-sional emotional space)to improve the recognition accuracy of the model;Then,a method and dataset for identifying multi-dimensional con-tinuous emotions in VAD three-dimensional space were provided,utilizing their correlation for multi task joint learning,and establishing con-straints between emotion categories and VAD multidimensional emotion space.Compared with traditional fixed emotion category labels,it can more comprehensively and meticulously describe emotional states,especially in dimension D,which is currently less studied;Finally,use the sentiment labels available in the sentiment category dataset FER2013 and manually added VAD annotations to measure VAD sentiment.The experiment shows that multi task learning with V and category,A and category,and D and category can significantly improve the recogni-tion performance of the model.