面向隐私保护的联邦域泛化行人重识别方法

doi:10.16451/j.cnki.issn1003-6059.202307005

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Abstract Person re-identification aims at recognizing images of target pedestrians in different cameras. The re-identification model trained in one scene cannot be directly applied in another scene, due to the domain bias between different scenes. The data collected from cameras often contains sensitive personal information. Most of the existing re-identification methods usually require centralization of training data, resulting in privacy leakage problems. Therefore, a method for federated domain generalization person re-identification with privacy preserving(PFReID) is proposed in this paper to learn a generalized model in a non-shared data domain with pedestrian privacy preserved. In PFReID, the frequency-domain spatial interpolation is introduced to smooth the domain deviation of each client on datasets, increase the diversity of samples and improve the generalization performance of client models. Moreover, a double-branch alignment learning network is designed for the update of the client-side local model by maximizing the consistency between the learned representation of the client-side local model and the learned representation of the global model. The superiority of PFReID is verified on public pedestrian datasets.

Key words： Person Re-identification Domain Generalization Federated Learning Data Privacy Alignment Learning

Received: 06 April 2023

ZTFLH:

TP391.4

Fund:Natural Science Foundation of Hebei Province(No.F2022201009), Science and Technology Project of Hebei Education Department(No.QN2023186), Hebei University High-Level Scientific Research Foundation for the Introduction of Talent(No.521100221029)

Corresponding Authors: PENG Jinjia, Ph.D., lecturer. Her research interests include person re-identification and image processing.

About author:: SONG Pengpeng, master student. His research interests include person re-identification. WANG Huibing, Ph.D., associate profe-ssor. His research interests include machine learning and image processing.

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	PENG Jinjia
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Cite this article:

PENG Jinjia,SONG Pengpeng,WANG Huibing. Federated Domain Generalization Person Re-identification with Privacy Preserving[J]. Pattern Recognition and Artificial Intelligence, 2023, 36(7): 634-646.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202307005 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2023/V36/I7/634

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