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How important are faces for person re-identification?

Dietlmeier, Julia orcid logoORCID: 0000-0001-9980-0910, Antony, Joseph orcid logoORCID: 0000-0001-6493-7829, McGuinness, Kevin orcid logoORCID: 0000-0003-1336-6477 and O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 (2021) How important are faces for person re-identification? In: 25th International Conference on Pattern Recognition (ICPR2020), 10-15 Jan 2021, Milan, Italy (Online).

Abstract
This paper investigates the dependence of existing state-of-the-art person re-identification models on the presence and visibility of human faces. We apply a face detection and blurring algorithm to create anonymized versions of several popular person re-identification datasets including Market1501, DukeMTMC-reID, CUHK03, Viper, and Airport. Using a cross-section of existing state-of-the-art models that range in accuracy and computational efficiency, we evaluate the effect of this anonymization on re-identification performance using standard metrics. Perhaps surprisingly, the effect on mAP is very small, and accuracy is recovered by simply training on the anonymized versions of the data rather than the original data. These findings are consistent across multiple models and datasets. These results indicate that datasets can be safely anonymized by blurring faces without significantly impacting the performance of person reidentification systems, and may allow for the release of new richer re-identification datasets where previously there were privacy or data protection concerns.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Computer Vision and Pattern Recognition
Subjects:UNSPECIFIED
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Published in: Proceedings 2020 25th International Conference on Pattern Recognition (ICPR). . IEEE.
Publisher:IEEE
Official URL:https://dx.doi.org/10.1109/ICPR48806.2021.9412340
Copyright Information:© 2021 The Authors
Funders:Science Foundation Ireland (SFI) under grant number SFI/15/SIRG/3283 and SFI/12/RC/2289 P2
ID Code:25079
Deposited On:13 Oct 2020 10:00 by Julia Dietlmeier . Last Modified 18 Oct 2022 15:06
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