An automatic emotion recognition system that uses the human body posture

• Autor: Heredia Parillo, Juanpablo Andrew
• Formato: tesis de grado
• Fecha de Publicación: 2021
• Institución: Universidad Católica San Pablo
• Repositorio: UCSP-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.ucsp.edu.pe:20.500.12590/16702
• Enlace del recurso: https://hdl.handle.net/20.500.12590/16702
• Nivel de acceso: acceso abierto
• Materia: Emotion recognition; Posture Classification; Meta-learning; https://purl.org/pe-repo/ocde/ford#1.02.01

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 allows training with few examples. Only the meta-learning algorithm was tested, which demonstrated the adaptability and expands the applicability of the graph convolutional neural networks.