Posture-Based Emotion Recognition via Text-Supervision and Multi-granularity Features
By analyzing the continuous motion patterns of the 3D pose skeleton,modeling a posture-based emotion recognition method has a wide application prospect in human-computer interaction.Aiming at the difficulty of capturing the local emotional features caused by easily-confused posture fea-tures,a posture-based emotion recognition method via text-supervision and multi-granularity features is proposed.With multi-granularity feature fusion network module,the dynamic posture of the human body in the emotion recognition problem is modeled.Meanwhile,the text description of the label is used to supervise and train the fused emotional features,guiding the network to extract the emotional features of each skeleton part.The method is evaluated on the MPI emotional body expression datas-et.The experimental results show that the accuracy of the method reaches 80.50%.Thus,its effec-tiveness is verified.
万方数据
维普
