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Authors: Vincenzo Carletti ; Antonio Greco ; Alessia Saggese and Mario Vento

Affiliation: Dept. of Information Eng., Electrical Eng. and Applied Mathematics (DIEM), University of Salerno, Italy

Keyword(s): Gender Recognition, Attention Mechanism, Masked Faces.

Abstract: Gender recognition from face images can be profitably used in several vertical markets, such as targeted advertising and cognitive robotics. However, in the last years, due to the COVID-19 pandemic, the unreliability of such systems when dealing with faces covered by a mask has emerged. In this paper, we propose a novel architecture based on attention layers and trained with a domain specific data augmentation technique for reliable gender recognition of masked faces. The proposed method has been experimentally evaluated on a huge dataset, namely VGGFace2-M, a masked version of the well known VGGFace2 dataset, and the achieved results confirm an improvement of around 4% with respect to traditional gender recognition algorithms, while preserving the performance on unmasked faces.

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Paper citation in several formats:
Carletti, V.; Greco, A.; Saggese, A. and Vento, M. (2022). Attention-based Gender Recognition on Masked Faces. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP; ISBN 978-989-758-555-5; ISSN 2184-4321, SciTePress, pages 672-678. DOI: 10.5220/0010978700003124

@conference{visapp22,
author={Vincenzo Carletti. and Antonio Greco. and Alessia Saggese. and Mario Vento.},
title={Attention-based Gender Recognition on Masked Faces},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP},
year={2022},
pages={672-678},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010978700003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP
TI - Attention-based Gender Recognition on Masked Faces
SN - 978-989-758-555-5
IS - 2184-4321
AU - Carletti, V.
AU - Greco, A.
AU - Saggese, A.
AU - Vento, M.
PY - 2022
SP - 672
EP - 678
DO - 10.5220/0010978700003124
PB - SciTePress

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