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Authors: Dong Han 1 ; 2 ; Yong Li 1 and Joachim Denzler 2

Affiliations: 1 Huawei European Research Center, Riesstraße 25, 80992 München, Germany ; 2 Computer Vision Group, Friedrich Schiller University Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany

Keyword(s): Privacy-Preserving, Face Recognition, Frequency Information, Color Information, Face Embedding Protection.

Abstract: Face recognition technology has been deployed in various real-life applications. The most sophisticated deep learning-based face recognition systems rely on training millions of face images through complex deep neural networks to achieve high accuracy. It is quite common for clients to upload face images to the service provider in order to access the model inference. However, the face image is a type of sensitive biometric attribute tied to the identity information of each user. Directly exposing the raw face image to the service provider poses a threat to the user’s privacy. Current privacy-preserving approaches to face recognition focus on either concealing visual information on model input or protecting model output face embedding. The noticeable drop in recognition accuracy is a pitfall for most methods. This paper proposes a hybrid frequency-color fusion approach to reduce the input dimensionality of face recognition in the frequency domain. Moreover, sparse color information is also introduced to alleviate significant accuracy degradation after adding differential privacy noise. Besides, an identity-specific embedding mapping scheme is applied to protect original face embedding by enlarging the distance among identities. Lastly, secure multiparty computation is implemented for safely computing the embedding distance during model inference. The proposed method performs well on multiple widely used verification datasets. Moreover, it has around 2.6% to 4.2% higher accuracy than the state-of-the-art in the 1:N verification scenario. (More)

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Paper citation in several formats:
Han, D.; Li, Y. and Denzler, J. (2024). Privacy-Preserving Face Recognition in Hybrid Frequency-Color Domain. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-679-8; ISSN 2184-4321, SciTePress, pages 536-546. DOI: 10.5220/0012373200003660

@conference{visapp24,
author={Dong Han. and Yong Li. and Joachim Denzler.},
title={Privacy-Preserving Face Recognition in Hybrid Frequency-Color Domain},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2024},
pages={536-546},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012373200003660},
isbn={978-989-758-679-8},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - Privacy-Preserving Face Recognition in Hybrid Frequency-Color Domain
SN - 978-989-758-679-8
IS - 2184-4321
AU - Han, D.
AU - Li, Y.
AU - Denzler, J.
PY - 2024
SP - 536
EP - 546
DO - 10.5220/0012373200003660
PB - SciTePress