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Multi-Modal Long-Term Person Re-Identification Using Physical Soft Bio-Metrics and Body Figure

Shoukry, Nadeen ; Abd El Ghany, Mohamed A. ; Salem, Mohammed A.-M. (2022):
Multi-Modal Long-Term Person Re-Identification Using Physical Soft Bio-Metrics and Body Figure. (Publisher's Version)
In: Applied Sciences, 12 (6), MDPI, e-ISSN 2076-3417,
DOI: 10.26083/tuprints-00021107,
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Item Type: Article
Origin: Secondary publication DeepGreen
Status: Publisher's Version
Title: Multi-Modal Long-Term Person Re-Identification Using Physical Soft Bio-Metrics and Body Figure
Language: English
Abstract:

Person re-identification is the task of recognizing a subject across different non-overlapping cameras across different views and times. Most state-of-the-art datasets and proposed solutions tend to address the problem of short-term re-identification. Those models can re-identify a person as long as they are wearing the same clothes. The work presented in this paper addresses the task of long-term re-identification. Therefore, the proposed model is trained on a dataset that incorporates clothes variation. This paper proposes a multi-modal person re-identification model. The first modality includes soft bio-metrics: hair, face, neck, shoulders, and part of the chest. The second modality is the remaining body figure that mainly focuses on clothes. The proposed model is composed of two separate neural networks, one for each modality. For the first modality, a two-stream Siamese network with pre-trained FaceNet as a feature extractor for the first modality is utilized. Part-based Convolutional Baseline classifier with a feature extractor network OSNet for the second modality. Experiments confirm that the proposed model can outperform several state-of-the-art models achieving 81.4 % accuracy on Rank-1, 82.3% accuracy on Rank-5, 83.1% accuracy on Rank-10, and 83.7% accuracy on Rank-20.

Journal or Publication Title: Applied Sciences
Volume of the journal: 12
Issue Number: 6
Publisher: MDPI
Collation: 18 Seiten
Uncontrolled Keywords: FaceNet, long-term person re-identification, OSNet, PCB, PRCC dataset, Siamese network
Classification DDC: 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften
Divisions: 18 Department of Electrical Engineering and Information Technology > Institute of Computer Engineering > Integrated Electronic Systems (IES)
Date Deposited: 08 Apr 2022 11:21
Last Modified: 08 Apr 2022 11:21
DOI: 10.26083/tuprints-00021107
Corresponding Links:
URN: urn:nbn:de:tuda-tuprints-211075
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/21107
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