Publication Details
Reconstruction and enhancement techniques for overcoming occlusion in facial recognition
Goldmann Tomáš, Ing., Ph.D. (DITS FIT BUT)
Malinka Kamil, Mgr., Ph.D. (DITS FIT BUT)
face recognition, face reconstruction, image enhancement, ArcFace, MagFace, QMagFace, GAN
Face occlusions on CCTV cameras obscure important key facial features, preventing face recognition (FR) systems from recognizing people. This work mainly focuses on reconstructing these missing facial parts using Generative Adversarial Neural Networks (GANs) to improve FR accuracy while maintaining a low False Acceptance Rate (FAR). In addition, we are trying to improve the generated images further by using different image enhancement methods to test whether they can be used to improve the FR accuracy. To test the results, we perform experiments using state-of-the-art FR methods such as QMagFace and ArcFace to see whether image reconstruction and image enhancement help to improve FR accuracy.
@ARTICLE{FITPUB13192,
author = "Filip Ple\v{s}ko and Tom\'{a}\v{s} Goldmann and Kamil Malinka",
title = "Reconstruction and enhancement techniques for overcoming occlusion in facial recognition",
pages = "1--21",
journal = "Eurasip Journal on Image and Video Processing",
volume = 2025,
number = 1,
year = 2025,
ISSN = "1687-5281",
doi = "10.1186/s13640-025-00670-7",
language = "english",
url = "https://www.fit.vut.cz/research/publication/13192"
}