research-article

Deep Discrete Attention Guided Hashing for Face Image Retrieval

Authors:

Weiping WangAuthors Info & Claims

ICMR '20: Proceedings of the 2020 International Conference on Multimedia Retrieval

Pages 136 - 144

https://doi.org/10.1145/3372278.3390683

Published: 08 June 2020 Publication History

Abstract

Recently, face image hashing has been proposed in large-scale face image retrieval due to its storage and computational efficiency. However, owing to the large intra-identity variation (same identity with different poses, illuminations, and facial expressions) and the small inter-identity separability (different identities look similar) of face images, existing face image hashing methods have limited power to generate discriminative hash codes. In this work, we propose a deep hashing method specially designed for face image retrieval named deep Discrete Attention Guided Hashing (DAGH). In DAGH, the discriminative power of hash codes is enhanced by a well-designed discrete identity loss, where not only the separability of the learned hash codes for different identities is encouraged, but also the intra-identity variation of the hash codes for the same identities is compacted. Besides, to obtain the fine-grained face features, DAGH employs a multi-attention cascade network structure to highlight discriminative face features. Moreover, we introduce a discrete hash layer into the network, along with the proposed modified backpropagation algorithm, our model can be optimized under discrete constraint. Experiments on two widely used face image retrieval datasets demonstrate the inspiring performance of DAGH over the state-of-the-art face image hashing methods.

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Cited By

Elboushaki AHannane RAfdel K(2024)Similarity-based face image retrieval using sparsely embedded deep features and binary code learningInternational Journal of Multimedia Information Retrieval10.1007/s13735-024-00337-513:3Online publication date: 8-Jul-2024
https://doi.org/10.1007/s13735-024-00337-5
Zhu YGao JZhou X(2023)AVForensics: Audio-driven Deepfake Video Detection with Masking Strategy in Self-supervisionProceedings of the 2023 ACM International Conference on Multimedia Retrieval10.1145/3591106.3592218(162-171)Online publication date: 12-Jun-2023
https://dl.acm.org/doi/10.1145/3591106.3592218
Feng HWang NZhao FHuo W(2023)Deep attention sampling hashing for efficient image retrievalNeurocomputing10.1016/j.neucom.2023.126764559(126764)Online publication date: Nov-2023
https://doi.org/10.1016/j.neucom.2023.126764
Show More Cited By

Index Terms

Deep Discrete Attention Guided Hashing for Face Image Retrieval
1. Information systems
  1. Information retrieval
    1. Specialized information retrieval
      1. Multimedia and multimodal retrieval
        Image search

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Published In

cover image ACM Conferences

ICMR '20: Proceedings of the 2020 International Conference on Multimedia Retrieval

June 2020

605 pages

ISBN:9781450370875

DOI:10.1145/3372278

General Chairs:
Cathal Gurrin
Dublin City University, Ireland
,
Björn Þór Jónsson
IT University of Copenhagen, Denmark
,
Noriko Kando
National Institute of Informatics, Tokyo
,
Program Chairs:
Klaus Schoeffmann
Klagenfurt University, Austria
,
Phoebe Chen
La Trobe University, Australia
,
Noel E. O'Connor
Dublin City University, Ireland

Copyright © 2020 ACM.

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SIGMM: ACM Special Interest Group on Multimedia

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 June 2020

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Funding Sources

the Strategic Priority Research Program of the Chinese Academy of Sciences
the National Key Research and Development Program of China
the NUDT Research Program

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ICMR '20

Sponsor:

SIGMM

ICMR '20: International Conference on Multimedia Retrieval

June 8 - 11, 2020

Dublin, Ireland

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Overall Acceptance Rate 254 of 830 submissions, 31%

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Citations

Cited By

Elboushaki AHannane RAfdel K(2024)Similarity-based face image retrieval using sparsely embedded deep features and binary code learningInternational Journal of Multimedia Information Retrieval10.1007/s13735-024-00337-513:3Online publication date: 8-Jul-2024
https://doi.org/10.1007/s13735-024-00337-5
Zhu YGao JZhou X(2023)AVForensics: Audio-driven Deepfake Video Detection with Masking Strategy in Self-supervisionProceedings of the 2023 ACM International Conference on Multimedia Retrieval10.1145/3591106.3592218(162-171)Online publication date: 12-Jun-2023
https://dl.acm.org/doi/10.1145/3591106.3592218
Feng HWang NZhao FHuo W(2023)Deep attention sampling hashing for efficient image retrievalNeurocomputing10.1016/j.neucom.2023.126764559(126764)Online publication date: Nov-2023
https://doi.org/10.1016/j.neucom.2023.126764
Feng PZhang HSun YTang Z(2023)Lightweight Image Hashing Based on Knowledge Distillation and Optimal Transport for Face RetrievalMultiMedia Modeling10.1007/978-3-031-27818-1_35(423-434)Online publication date: 9-Jan-2023
https://dl.acm.org/doi/10.1007/978-3-031-27818-1_35
Testa RMachado-Lima ANunes F(2021)Facial expression synthesis based on similar facesMultimedia Tools and Applications10.1007/s11042-021-11525-480:30(36465-36489)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1007/s11042-021-11525-4
Fu XWang WTang J(2021)Deep Double Center Hashing for Face Image RetrievalPattern Recognition and Computer Vision10.1007/978-3-030-88007-1_52(636-648)Online publication date: 22-Oct-2021
https://doi.org/10.1007/978-3-030-88007-1_52
Zhao SWu DZhang WZhou YLi BWang WWen Chen CCucchiara RHua XQi GRicci EZhang ZZimmermann R(2020)Asymmetric Deep Hashing for Efficient Hash Code CompressionProceedings of the 28th ACM International Conference on Multimedia10.1145/3394171.3414033(763-771)Online publication date: 12-Oct-2020
https://dl.acm.org/doi/10.1145/3394171.3414033
Zhang WWu DZhou YLi BWang WMeng DWen Chen CCucchiara RHua XQi GRicci EZhang ZZimmermann R(2020)Deep Unsupervised Hybrid-similarity Hadamard HashingProceedings of the 28th ACM International Conference on Multimedia10.1145/3394171.3414028(3274-3282)Online publication date: 12-Oct-2020
https://dl.acm.org/doi/10.1145/3394171.3414028

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