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Authors: Natália Meira ; Ricardo Santos ; Mateus Silva ; Eduardo Luz and Ricardo Oliveira

Affiliation: Department of Computer Science, Federal University of Ouro Preto, Ouro Preto, Brazil

Keyword(s): GANs, Privacy, Data Anonymization, Representative Data, Face Swap, Deep Learning.

Abstract: Deep learning models based on autoencoders and generative adversarial networks (GANs) have enabled increasingly realistic face-swapping tasks. Surveillance cameras for detecting people and faces to monitor human behavior are becoming more common. Training AI models for these detection and monitoring tasks require large sets of facial data that represent ethnic, gender, and age diversity. In this work, we propose the use of generative facial manipulation techniques to build a new representative data augmentation set to be used in deep learning training for tasks involving the face. In the presented step, we implemented one of the most famous facial switching architectures to demonstrate an application for anonymizing personal data and generating synthetic data with images of drivers’ faces during their work activity. Our case study generated synthetic facial data from a driver at work. The results were convincing in facial replacement and preservation of the driver’s expression.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Meira, N. ; Santos, R. ; Silva, M. ; Luz, E. and Oliveira, R. (2023). Towards an Automatic System for Generating Synthetic and Representative Facial Data for Anonymization. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP; ISBN 978-989-758-634-7; ISSN 2184-4321, SciTePress, pages 854-861. DOI: 10.5220/0011796800003417

@conference{visapp23,
author={Natália Meira and Ricardo Santos and Mateus Silva and Eduardo Luz and Ricardo Oliveira},
title={Towards an Automatic System for Generating Synthetic and Representative Facial Data for Anonymization},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP},
year={2023},
pages={854-861},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011796800003417},
isbn={978-989-758-634-7},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP
TI - Towards an Automatic System for Generating Synthetic and Representative Facial Data for Anonymization
SN - 978-989-758-634-7
IS - 2184-4321
AU - Meira, N.
AU - Santos, R.
AU - Silva, M.
AU - Luz, E.
AU - Oliveira, R.
PY - 2023
SP - 854
EP - 861
DO - 10.5220/0011796800003417
PB - SciTePress

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