1. Spatial Temporal Variation Graph Convolutional Networks (STV-GCN) for Skeleton-Based Emotional Action Recognition
- Author
-
Ming-Fong Tsai and Chiung-Hung Chen
- Subjects
spatial temporal graph convolution network ,General Computer Science ,Computer science ,business.industry ,Emotional intelligence ,Feature extraction ,General Engineering ,020206 networking & telecommunications ,020207 software engineering ,Pattern recognition ,02 engineering and technology ,Variation (game tree) ,Statistical classification ,Artificial emotional intelligence ,Core (graph theory) ,0202 electrical engineering, electronic engineering, information engineering ,Graph (abstract data type) ,General Materials Science ,human skeleton joint point ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,Artificial intelligence ,business ,lcsh:TK1-9971 - Abstract
The main core purpose of artificial emotional intelligence is to recognize human emotions. Technologies such as facial, semantic, or brainwave recognition applications have been widely proposed. However, the abovementioned recognition techniques for emotional features require a large number of training samples to obtain high accuracy. Human behaviour pattern can be trained and recognized by the continuous movement of the Spatial Temporal Graph Convolution Network (ST-GCN). However, this technology does not distinguish between the speed of delicate emotions, and the speed of human behaviour and delicate changes of emotions cannot be effectively distinguished. This research paper proposes Spatial Temporal Variation Convolutional Network training for human emotion recognition, using skeleton detection technology to calculate the degree of skeleton point change between consecutive actions and using the nearest neighbour algorithm to classify speed levels and train the ST-GCN recognition model to obtain the emotional state. Application of the speed change recognition ability of the Spatial Temporal Variation Graph Convolution Network (STV-GCN) to artificial emotional intelligence calculation makes it possible to efficiently recognize the delicate actions of happy, sad, fear, and angry in human behaviour. The STV-GCN technology proposed in this paper is compared with ST-GCN and can effectively improve the recognition accuracy by more than 50%.
- Published
- 2021