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 the 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 approach needs many training samples, but these are difficult to obtain for posture emotion recognition, thus, the proposed model trains under a meta-learning algorithm based on the “agnostic model”, which all...

People naturally express emotions in simultaneous different ways. Thus, multimodal methods are becoming popular for emotion recognition and analysis of reactions to many aspects of daily life. This research work presents a multimodal method for emotion recognition from images. The multi-modal method analyses facial expressions, body gestures and the characteristics of the body and the environment to determine an emotional state, processing each modality with a specialised deep learning model and then applying the proposed fusion method. The fusion method, called EmbraceNet+, consists of a branched architecture that integrates the EmbraceNet fusion method with other fusion methods. The tests carried out on an adaptation of the EMOTIC dataset show that the proposed multi-modal method is effective and improves the results obtained by individual processings, as well as competing with other s...