Gan-based realistic face pose synthesis

Detalhes bibliográficos
Ano de defesa: 2018
Autor(a) principal: Souza, Douglas Matos de lattes
Orientador(a): Ruiz, Duncan Dubugras Alcoba lattes
Banca de defesa: Não Informado pela instituição
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: eng
Instituição de defesa: Pontifícia Universidade Católica do Rio Grande do Sul
Programa de Pós-Graduação: Programa de Pós-Graduação em Ciência da Computação
Departamento: Escola Politécnica
País: Brasil
Palavras-chave em Português:
Palavras-chave em Inglês:
Área do conhecimento CNPq:
Link de acesso: http://tede2.pucrs.br/tede2/handle/tede/9250
Resumo: In computer vision, processing face images are accompanied by a series of complexities. Examples include variation of pose, light, face expression, and make up. Although all aspects are considered important, the one that impacts the most face-related computer vision systems is pose. In face recognition, for example, it has been long desired to have a method capable of bringing faces to the same pose, usually a frontal view, in order to ease recognition. Synthesizing different views of a face is a great challenge, mostly because in non-frontal face images there are loss of information when one side of the face occludes the other (also known as self-occlusion). Several methods to address face pose synthesis were proposed, but the results usually miss a realistic finish. In this work, we present novel methods that improve on the previous ones, showing higher synthesis quality.