Federated Domain Generalization Person Re-identification with Privacy Preserving
PENG Jinjia1, SONG Pengpeng1, WANG Huibing2
1. School of Cyber Security and Computer, Hebei University, Baoding 071002; 2. Information Science and Technology College, Dalian Maritime University, Dalian 116026
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.
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.
[1] ZHENG K C, LIU W, HE L X, et al. Group-Aware Label Transfer for Domain Adaptive Person Re-identification // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2021: 5306-5315. [2] 杨静,张灿龙,李志欣,等.集成空间注意力和姿态估计的遮挡行人再辨识.计算机研究与发展, 2022, 59(7): 1522-1532. (YANG J, ZHANG C L, LI Z X, et al. Integrated Spatial Attention and Pose Estimation for Occluded Person Re-identification. Journal of Computer Research and Development, 2022, 59(7): 1522-1532.) [3] 陆萍,董虎胜,钟珊,等.基于跨视角判别词典嵌入的行人再识别.计算机研究与发展, 2019, 56(11): 2424-2437. (LU P, DONG H S, ZHONG S, et al. Person Re-identification by Cross-View Discriminative Dictionary Learning with Metric Embe-dding. Journal of Computer Research and Development, 2019, 56(11): 2424-2437.) [4] QIAO F C, PENG X. Uncertainty-Guided Model Generalization to Unseen Domains // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2021: 6786-6796. [5] JIN X, LAN C L, ZENG W J, et al. Style Normalization and Restitution for Domain Generalization and Adaptation. IEEE Transactions on Multimedia, 2021, 24: 3636-3651. [6] LI L, GAO K, CAO J, et al. Progressive Domain Expansion Network for Single Domain Generalization // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2021: 224-233. [7] WU G L, GONG S G. Decentralised Learning from Independent Multi-domain Labels for Person Re-identification. Proceedings of the AAAI Conference on Artificial Intelligence, 2021, 35(4): 2898-2906. [8] MCMAHAN B, MOORE E, RAMAGE D, et al. Communication-Efficient Learning of Deep Networks from Decentralized Data. Proceedings of Machine Leaning Research, 2017, 54: 1273-1282. [9] JIAO B L, LIU L Q, GAO L Y, et al. Dynamically Transformed Instance Normalization Network for Generalizable Person Re-identification // Proc of the 17th European Conference on Computer Vision. Berlin, Germany: Springer, 2022: 285-301. [10] ZHANG L, LIU Z P, ZHANG W S, et al. Style Uncertainty Based Self-Paced Meta Learning for Generalizable Person Re-identification. IEEE Transactions on Image Processing, 2023, 32: 2107-2119. [11] ZHAO J J, ZHAO Y F, CHEN X W, et al. Revisiting Stochastic Learning for Generalizable Person Re-identification // Proc of the 30th ACM International Conference on Multimedia. New York, USA: ACM, 2022: 1758-1768. [12] ZHAO Y Y, ZHONG Z, YANG F X, et al. Learning to Generalize Unseen Domains via Memory-Based Multi-source Meta-Learning for Person Re-identification // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2021: 6273-6282. [13] NI H, SONG J K, LUO X P, et al. Meta Distribution Alignment for Generalizable Person Re-identification // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2022: 2477-2486. [14] ZHOU K Y, YANG Y X, CAVALLARO A, et al. Learning Generalisable Omni-Scale Representations for Person Re-identification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(9): 5056-5069. [15] ZHANG P Y, DOU H Z, YU Y L, et al. Adaptive Cross-Domain Learning for Generalizable Person Re-identification // Proc of the 17th European Conference on Computer Vision. Berlin, Germany: Springer, 2022: 215-232. [16] GONG T T, CHEN K X, ZHANG L Y, et al. Debiased Contrastive Curriculum Learning for Progressive Generalizable Person Re-identification. IEEE Transactions on Circuits and Systems for Video Technology, 2023. DOI: 10.1109/TCSVT.2023.3262832. [17] LI L, FAN Y X, TSE M, et al. A Review of Applications in Fe-derated Learning. Computers and Industrial Engineering, 2020, 149. DOI: 10.1016/j.cie.2020.106854. [18] ZHUANG W M, GAN X, WEN Y G, et al. Optimizing Perfor-mance of Federated Person Re-identification: Benchmarking and Analysis. ACM Transactions on Multimedia Computing, Communi-cations, and Applications, 2023, 19(1s). DOI: 10.1145/3531013. [19] YANG F X, ZHONG Z, LUO Z M, et al. Federated and Genera-lized Person Re-identification through Domain and Feature Hallucinating[C/OL].[2023-03-22]. https://arxiv.org/pdf/2203.02689.pdf. [20] SUN S T, WU G L, GONG S G. Decentralised Person Re-identification with Selective Knowledge Aggregation[C/OL]. [2023-03-22]. https://arxiv.org/pdf/2110.11384v1.pdf. [21] ZHONG Z, ZHENG L, ZHENG Z D, et al. Camstyle: A Novel Data Augmentation Method for Person Re-identification. IEEE Transactions on Image Processing, 2019, 28(3): 1176-1190. [22] WANG H H, WU X D, HUANG Z Y, et al. High-Frequency Component Helps Explain the Generalization of Convolutional Neural Networks // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2020: 8681-8691. [23] ZHENG L, SHEN L Y, TIAN L, et al. Scalable Person Re-identification: A Benchmark // Proc of the IEEE International Confe-rence on Computer Vision. Washington, USA: IEEE, 2015: 1116-1124. [24] ZHENG Z D, ZHENG L, YANG Y. Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro // Proc of the IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2017: 3774-3782. [25] WEI L H, ZHANG S L, GAO W, et al. Person Transfer GAN to Bridge Domain Gap for Person Re-identification // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2018: 79-88. [26] LI W, ZHAO R, XIAO T, et al. DeepReID: Deep Filter Pairing Neural Network for Person Re-identification // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2014: 152-159. [27] GRAY D, TAO H. Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features // Proc of the European Conference on Computer Vision. Berlin, Germany: Springer, 2008: 262-275. [28] ZHENG W S, GONG S G, XIANG T. Associating Groups of Peo-ple[C/OL]. [2023-03-22]. http://www.eecs.qmul.ac.uk/~sgg/papers/ZhengGongXiang_BMVC09.pdf. [29] PASZKE A, GROSS S, CHINTALA S, et al. Automatic Differentiation in Pytorch[C/OL].[2023-03-22]. https://openreview.net/pdf?id=BJJsrmfCZ. [30] LI D, YANG Y X, SONG Y Z, et al. Learning to Generalize: Meta-Learning for Domain Generalization. Proceedings of the AAAI Conference on Artificial Intelligence, 2018, 32(1): 3490-3497. [31] SONG J F, YANG Y X, SONG Y Z, et al. Generalizable Person Re-identification by Domain-Invariant Mapping Network // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2019: 719-728. [32] PENG P X, XIANG T, WANG Y W, et al. Unsupervised Cross-dataset Transfer Learning for Person Re-identification // Proc of the IEEE Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2016: 1306-1315. [33] YANG Q Z, YU H X, WU A C, et al. Patch-Based Discriminative Feature Learning for Unsupervised Person Re-identification // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Re-cognition. Washington, USA: IEEE, 2019: 3628-3637. [34] WANG J Y, ZHU X T, GONG S G, et al. Transferable Joint Attribute-Identity Deep Learning for Unsupervised Person Re-identification // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2018: 2275-2284. [35] HU J L, LU J W, TAN Y P. Deep Transfer Metric Learning // Proc of the IEEE Conference on Computer Vision and Pattern Re-cognition. Washington, USA: IEEE, 2015: 325-333. [36] BAK S, CARR P, LALONDE J F. Domain Adaptation through Synthesis for Unsupervised Person Re-identification // Proc of the European Conference on Computer Vision. Berlin, Germany: Springer, 2018: 193-209. [37] SU C, ZHANG S L, XING J L, et al. Deep Attributes Driven Multi-camera Person Re-identification // Proc of the European Conference on Computer Vision. Berlin, Germany: Springer, 2016: 475-491. [38] SHANKAR S, PIRATLA V, CHAKRABARTI S, et al. Generalizing across Domains via Cross-Gradient Training[C/OL].[2023-03-22]. https://arxiv.org/pdf/1804.10745.pdf. [39] XU X, LIU W, WANG Z, et al. Towards Generalizable Person Re-identification with a Bi-stream Generative Model[C/OL].[2023-03-22]. https://arxiv.org/pdf/2206.09362v1.pdf. [40] FAN H H, ZHENG L, YAN C G, et al. Unsupervised Personre-Identification: Clustering and Fine-Tuning.ACM Transactions on Multimedia Computing, Communications, and Applications, 2018, 14(4). DOI: 10.1145/3243316. [41] DENG W J, ZHENG L, YE Q X, et al. Image-Image Domain Adap-tation with Preserved Self-Similarity and Domain Dissimilarity for Person Re-identification // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2018: 994-1003. [42] ZHONG Z, ZHENG L, LI S Z, et al. Generalizing a Person Retrieval Model Hetero-and Homogeneously // Proc of the European Conference on Computer Vision. Berlin, Germany: Springer, 2018: 176-192. [43] LIN Y T, DONG X Y, ZHENG L, et al. A Bottom-Up Clustering Approach to Unsupervised Person Re-identification. Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33(1): 8738-8745. [44] ZHUANG W M, WEN Y G, ZHANG S. Joint Optimization in Edge-Cloud Continuum for Federated Unsupervised Person Re-identification // Proc of the 29th ACM International Conference on Multimedia. New York, USA: ACM, 2021: 433-441. [45] LIAO S C, SHAO L. Interpretable and Generalizable Person Re-identification with Query-Adaptive Convolution and Temporal Lif-ting // Proc of the European Conference on Computer Vision. Berlin, Germany: Springer, 2020: 456-474. [46] CHOI S, KIM T, JEONG M, et al. Meta Batch-Instance Normalization for Generalizable Person Re-identification // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2021: 3424-3434. [47] JIN X, LAN C L, ZENG W J, et al. Style Normalization and Restitution for Generalizable Person Re-identification // Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington, USA: IEEE, 2020: 3140-3149. [48] JOO H T, KIM K J. Visualization of Deep Reinforcement Learning Using Grad-CAM: How AI Plays Atari Games? // Proc of the IEEE Conference on Games. Washington, USA: IEEE, 2019. DOI: 10.1109/CIG.2019.8847950.
2023, Vol. 36 
