Exploring Super-Resolution for Face Recognition

  • Patrick Anderson Matias de Araújo Universidade Federal do Tocantins
  • Eduardo Ferreira Ribeiro Universidade Federa do Tocantins
Palavras-chave: Facial Recognition, Deep Learning, Super-Resolution, Images and Videos, Image Processing, Computer Vision

Resumo

Biometric recognition is part of many aspects of modern society. With the popularization of smartphones, facial recognition gains space in this environment of biometric technologies. With the diversity of image capture devices, of different brands and qualities, the images will not always be in the ideal standard to be recognized. This article tests and compares different scenarios and situations to assess the results obtained by facial recognition in different environments. For this, the quantity method of data analysis was used. In the first scenario, all images were submitted without changes. In the following, we have the reduction of image resolution, which may or may not be followed by enlargement to the original resolution via bicubic interpolation or through the Image Super-Resolution algorithm, these images can be all, or only that undergo tests. Results indicate that the first scenario obtained the best performance, followed by only the tests images change. The worst performance occurs where the properties of all images are affected. In situations where there is a reduction and enlargement are optional, the enlargement option performs better, so the bicubic enlargement has an advantage over the ISR, the situation in which only the reduction occurs has the worst performance.

Biografia do Autor

Eduardo Ferreira Ribeiro, Universidade Federa do Tocantins

Professor Adjunto do Curso de Curso de Ciência da Computação da Universidade Federal do Tocantins (2010-Atual). Possui Doutorado do programa Doktoratsstudium - Technische Wissenschaften - Angewandte Informatik na Universidade de Salzburg - Áustria (2018), Mestrado em Ciência da Computação na área de Banco de Dados pela Universidade Federal de Uberlândia (2008) e graduação em Ciência da Computação pela Universidade Federal de Goiás (2006). Tem experiência na área de Ciência da Computação, com ênfase em Processamento de Imagens, Inteligência Artificial, Aprendizado de Máquina e Deep Learning.

Publicado
2021-10-19
Como Citar
[1]
Anderson Matias de Araújo, P. e Ferreira Ribeiro, E. 2021. Exploring Super-Resolution for Face Recognition. Academic Journal on Computing, Engineering and Applied Mathematics. 3, 1 (out. 2021), 1-8. DOI:https://doi.org/10.20873/uft.2675-3588.2022.v3n1.p1-8.
Seção
Artigos de Pesquisa