Facial expression recognition using deep learning - convolutional neural network

• Ano de defesa: 2016
• Autor(a) principal: Lopes, André Teixeira
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Federal do Espírito Santo; BR; Mestrado em Informática; Centro Tecnológico; UFES; Programa de Pós-Graduação em Informática
• Palavras-chave em Português: Convolutional neural networks; Computer vision; Machine learning; Expression specific features; Deep learning; Aprendizagem profunda (Computação); Redes convolucionais; Visão computacional; Aprendizagem de máquina; Características específicas de expressões; Redes neurais (Computação); Visão por computador; Aprendizado do computador; Reconhecimento facial (Computação); Expressão facial; Inteligência artificial; Ciência da Computação; 004
• Link de acesso: http://repositorio.ufes.br/handle/10/4301

Facial expression recognition has been an active research area in the past ten years, with growing application areas such avatar animation, neuromarketing and sociable robots. The recognition of facial expressions is not an easy problem for machine learning methods, since people can vary signi cantly in the way that they show their expressions. Even images of the same person in one expression can vary in brightness, background and position. Hence, facial expression recognition is still a challenging problem. To address these problems, in this work we propose a facial expression recognition system that uses Convolutional Neural Networks. Data augmentation and di erent preprocessing steps were studied together with various Convolutional Neural Networks architectures. The data augmentation and pre-processing steps were used to help the network on the feature selection. Experiments were carried out with three largely used databases (Cohn-Kanade, JAFFE, and BU3DFE) and cross-database validations (i.e. training in one database and test in another) were also performed. The proposed approach has shown to be very e ective, improving the state-of-the-art results in the literature and allowing real time facial expression recognition with standard PC computers.