Gender Classification using the Gaze Distributions of Observers on Privacy-protected Training Images

Michiko Inoue, Masashi Nishiyama, Yoshio Iwai

2020

Abstract

We propose a method for classifying the gender of pedestrians using a classifier trained by images containing privacy-protection of the head region. Recently, manipulated training images containing pedestrians have been required to protect the privacy of pedestrians. In particular, the head regions of the training images are manipulated. However, the accuracy of gender classification decreases when privacy-protected training images are directly used. To overcome this issue, we aim to use the human visual ability to correctly discriminate males from females even though the head regions have been manipulated. We measure the gaze distributions of observers who view pedestrian images and use them to pre-process gender classifiers. The experimental results show that our method using gaze distribution improved the accuracy of gender classification when the head regions of the training images have been manipulated with masking, pixelization, and blur for privacy-protection.

Paper Citation

in Harvard Style

Inoue M., Nishiyama M. and Iwai Y. (2020). Gender Classification using the Gaze Distributions of Observers on Privacy-protected Training Images. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP; ISBN 978-989-758-402-2, SciTePress, pages 149-156. DOI: 10.5220/0008876101490156

in Bibtex Style

@conference{visapp20,
author={Michiko Inoue and Masashi Nishiyama and Yoshio Iwai},
title={Gender Classification using the Gaze Distributions of Observers on Privacy-protected Training Images},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP},
year={2020},
pages={149-156},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008876101490156},
isbn={978-989-758-402-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 4: VISAPP
TI - Gender Classification using the Gaze Distributions of Observers on Privacy-protected Training Images
SN - 978-989-758-402-2
AU - Inoue M.
AU - Nishiyama M.
AU - Iwai Y.
PY - 2020
SP - 149
EP - 156
DO - 10.5220/0008876101490156
PB - SciTePress