An automatic emotion recognition system that uses the human body posture
Ticona Herrera, Regina Paola
Non-verbal communication is very present in our lives, but it can be interpreted in different ways according to many factors. With nonverbal gestures, people can express explicit and implicit messages, which makes them important to understand. Computer vision methods for recognising body gestures and machine learning classification methods offer an opportunity to understand what people express with their bodies. This research work focuses on the emotions expressed by body gestures, particularly posture. Thus, an automatic emotion recognition system from images is proposed, which uses a graph convolutional neural network to perform the classification. Generally, deep learning approaches need many training samples, but these are difficult to obtain for posture emotion recognition, thus, the proposed model trains under a metalearning algorithm based on the “agnostic model”, which allows training with few examples. Only the meta-learning algorithm was tested, which demonstrated the adaptability and expanded the applicability of the graph convolutional neural networks.