Super-Resolution Face Recognition: An Approach Using Generative Adversarial Networks and Joint-Learn

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Autor(es): dc.contributorInstituto Politécnico de Bragança-
Autor(es): dc.contributorUniversidade Tecnológica Federal do Paraná-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorde Oliveira, Rafael Augusto-
Autor(es): dc.creatorScheeren, Michel Hanzen-
Autor(es): dc.creatorRodrigues, Pedro João Soares-
Autor(es): dc.creatorJunior, Arnaldo Candido-
Autor(es): dc.creatorde Paula Filho, Pedro Luiz-
Data de aceite: dc.date.accessioned2025-08-21T21:12:33Z-
Data de disponibilização: dc.date.available2025-08-21T21:12:33Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2021-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/978-3-031-23236-7_51-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/246822-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/246822-
Descrição: dc.descriptionFace Recognition is a challenging task present in different applications and systems. An existing challenge is to recognize faces when imaging conditions are adverse, for example when images come from low-quality cameras or when the subject and the camera are far apart, thus impacting the accuracy of these recognizing systems. Super-Resolution techniques can be used to improve both image resolution and quality, hopefully improving the accuracy of the face recognition task. Among these techniques, the actual state-of-the-art uses Generative Adversarial Networks. One promising option is to train Super-Resolution and Face Recognition as one single network, conducting the network to learn super resolution features that will improve its capability when recognizing faces. In the present work, we trained a super resolution face recognition model using a jointly-learn approach, combining a generative network for super resolution and a ResNet50 for Face Recognition. The model was trained with a discriminator network, following the generative adversarial training. The images generated by the network were convincing, but we could not converge the face recognition model. We hope that our contributions could help future works on this topic. Code is publicly available at https://github.com/OliRafa/SRFR-GAN.-
Descrição: dc.descriptionInstituto Politécnico de Bragança-
Descrição: dc.descriptionUniversidade Tecnológica Federal do Paraná-
Descrição: dc.descriptionUniversidade Estadual Paulista-
Descrição: dc.descriptionUniversidade Estadual Paulista-
Formato: dc.format747-762-
Idioma: dc.languageen-
Relação: dc.relationCommunications in Computer and Information Science-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectFace Recognition-
Palavras-chave: dc.subjectGenerative Adversarial Networks-
Palavras-chave: dc.subjectMachine learning-
Palavras-chave: dc.subjectSuper-resolution-
Título: dc.titleSuper-Resolution Face Recognition: An Approach Using Generative Adversarial Networks and Joint-Learn-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

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