Exploring Super-Resolution for Face Recognition
DOI:
https://doi.org/10.20873/uft.2675-3588.2022.v3n1.p1-8Keywords:
Facial Recognition, Deep Learning, Super-Resolution, Images and Videos, Image Processing, Computer VisionAbstract
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.
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Copyright (c) 2021 Patrick Anderson Matias de Araújo, Eduardo Ferreira Ribeiro
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