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Person re-identification with soft biometrics through deep learning
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Lin, Shan and Li, Chang-Tsun (2020) Person re-identification with soft biometrics through deep learning. In: Jiang, R. and Li, C. T. and Crookes, D. and Meng, W. and Rosenberger, C., (eds.) Deep Biometrics. Unsupervised and Semi-Supervised Learning . Springer, pp. 21-36. ISBN 9783030325824
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WRAP-person-re-identification-soft-biometrics-through-deep-learning-Li-2020.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only until 29 January 2022. Contact author directly, specifying your specific needs. - Requires a PDF viewer. Download (1071Kb) |
Official URL: http://dx.doi.org/10.1007/978-3-030-32583-1_2
Abstract
Re-identification of persons is usually based on primary biometric features such as their faces, fingerprints, iris or gait. However, in most existing video surveillance systems, it is difficult to obtain these features due to the low resolution of surveillance footages and unconstrained real-world environments. As a result, most of the existing person re-identification techniques only focus on overall visual appearance. Recently, the use of soft biometrics has been proposed to improve the performance of person re-identification. Soft biometrics such as height, gender, age are physical or behavioural features, which can be described by humans. These features can be obtained from low-resolution videos at a distance ideal for person re-identification application. In addition, soft biometrics are traits for describing an individual with human-understandable labels. It allows human verbal descriptions to be used in the person re-identification or person retrieval systems. In some deep learning based person re-identification methods, soft biometrics attributes are integrated into the network to boot the robustness of the feature representation. Biometrics can also be utilised as a domain adaptation bridge for addressing the cross-dataset person re-identification problem. This chapter will review the state-of-the-art deep learning methods involving soft biometrics from three perspectives: supervised, semi-supervised and unsupervised approaches. In the end, we discuss the existing issues that are not addressed by current works.
| Item Type: | Book Item | ||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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| Divisions: | Faculty of Science > Computer Science | ||||||||
| Library of Congress Subject Headings (LCSH): | Biometric identification, Image processing -- Digital techniques | ||||||||
| Series Name: | Unsupervised and Semi-Supervised Learning | ||||||||
| Publisher: | Springer | ||||||||
| ISBN: | 9783030325824 | ||||||||
| Book Title: | Deep Biometrics | ||||||||
| Editor: | Jiang, R. and Li, C. T. and Crookes, D. and Meng, W. and Rosenberger, C. | ||||||||
| Official Date: | 2020 | ||||||||
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| Page Range: | pp. 21-36 | ||||||||
| DOI: | 10.1007/978-3-030-32583-1_2 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
| RIOXX Funder/Project Grant: |
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