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Efficient and Privacy-preserving Distributed Face Recognition Scheme via FaceNet

Published: 02 October 2021 Publication History

Abstract

In recent years, with the development of deep learning techniques, face recognition has draw numerous attention in both academic and industrial. Meanwhile, it is also widely deployed in smart home and brings great conveniences in people’s life. However, due to the sensitivity of biometric data, face recognition is still confronted with several crucial challenges including face feature data disclosure. In this paper, based on random matrix, BLS short signature and FaceNet, we propose an efficient and privacy-preserving face recognition scheme for smart home. Specifically, the scheme includes two main algorithms: face templates encryption algorithm and privacy-preserving similarity computation algorithm. With the proposed two algorithms, face recognition is achieved without revealing face feature data. Security analysis proves that the face feature data is well protected. Moreover, extensive experiments are carried out with LFW dataset, and the experiment results demonstrate that our scheme is indeed efficient and precise.

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Cited By

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  • (2025)Toward a Privacy-Preserving Face Recognition System: A Survey of Leakages and SolutionsACM Computing Surveys10.1145/367322457:6(1-38)Online publication date: 10-Feb-2025
  • (2024)PRO-Face C: Privacy-Preserving Recognition of Obfuscated Face via Feature CompensationIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.338897619(4930-4944)Online publication date: 2024
  • (2024)Privacy-Preserving Face Recognition Using Trainable Feature Subtraction2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.00036(297-307)Online publication date: 16-Jun-2024
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          cover image ACM Other conferences
          ACM TURC '21: Proceedings of the ACM Turing Award Celebration Conference - China
          July 2021
          284 pages
          ISBN:9781450385671
          DOI:10.1145/3472634
          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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          Publication History

          Published: 02 October 2021

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          Author Tags

          1. BLS signature.
          2. Face recognition
          3. home automation
          4. privacy-preserving
          5. random matrix

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          Cited By

          View all
          • (2025)Toward a Privacy-Preserving Face Recognition System: A Survey of Leakages and SolutionsACM Computing Surveys10.1145/367322457:6(1-38)Online publication date: 10-Feb-2025
          • (2024)PRO-Face C: Privacy-Preserving Recognition of Obfuscated Face via Feature CompensationIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.338897619(4930-4944)Online publication date: 2024
          • (2024)Privacy-Preserving Face Recognition Using Trainable Feature Subtraction2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.00036(297-307)Online publication date: 16-Jun-2024
          • (2024)Maintaining Privacy in Face Recognition Using Federated Learning MethodIEEE Access10.1109/ACCESS.2024.337369112(39603-39613)Online publication date: 2024
          • (2024)Privacy-Preserving Face Recognition with Adaptive Generative PerturbationsPattern Recognition10.1007/978-3-031-78341-8_14(210-227)Online publication date: 2-Dec-2024
          • (2023)Privacy-Preserving Face Recognition Using Random Frequency Components2023 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV51070.2023.01802(19616-19627)Online publication date: 1-Oct-2023
          • (2023)Privacy-preserving Adversarial Facial Features2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52729.2023.00794(8212-8221)Online publication date: Jun-2023

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