| Title | A Compositionality Assembled Model for Learning and Recognizing Emotion from Bodily Expression |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Zhong, J., Yang, C. |
| Conference Name | 2019 IEEE 4th International Conference on Advanced Robotics and Mechatronics (ICARM) |
| Keywords | Adaptation models, adaptive learning, adaptive learning techniques, bifurcation, bodily expression, bodily presentation, compositionality, compositionality hypothesis, compositionality principle, Computing Theory and Compositionality, emotion recognition, express our internal status, hierarchical model, hierarchical recurrent neural learning framework, Human Behavior, human body expression, human factors, internal emotional state, learning (artificial intelligence), machine learning, Neurons, nonlinear dynamic bifurcation, pubcrawl, real-time emotion recognition, recurrent neural nets, Recurrent neural networks, RNN learning, robots, single components, Training |
| Abstract | When we are express our internal status, such as emotions, the human body expression we use follows the compositionality principle. It is a theory in linguistic which proposes that the single components of the bodily presentation as well as the rules used to combine them are the major parts to finish this process. In this paper, such principle is applied to the process of expressing and recognizing emotional states through body expression, in which certain key features can be learned to represent certain primitives of the internal emotional state in the form of basic variables. This is done by a hierarchical recurrent neural learning framework (RNN) because of its nonlinear dynamic bifurcation, so that variables can be learned to represent different hierarchies. In addition, we applied some adaptive learning techniques in machine learning for the requirement of real-time emotion recognition, in which a stable representation can be maintained compared to previous work. The model is examined by comparing the PB values between the training and recognition phases. This hierarchical model shows the rationality of the compositionality hypothesis by the RNN learning and explains how key features can be used and combined in bodily expression to show the emotional state. |
| DOI | 10.1109/ICARM.2019.8834083 |
| Citation Key | zhong_compositionality_2019 |
A Compositionality Assembled Model for Learning and Recognizing Emotion from Bodily Expression