Visual Appearance and Soft Biometrics Fusion for Person Re-Identification Using Deep Learning

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dc.contributor.authorKhan, Samee Ullah
dc.contributor.authorKhan, Noman
dc.contributor.authorHussain, Tanveer
dc.contributor.authorMuhammad, Khan
dc.contributor.authorHijji, Mohammad
dc.contributor.authorDel Ser, Javier
dc.contributor.authorBaik, Sung Wook
dc.contributor.institutionIA
dc.date.accessioned2024-09-06T10:40:01Z
dc.date.available2024-09-06T10:40:01Z
dc.date.issued2023-05-01
dc.descriptionPublisher Copyright: © 2023 IEEE.
dc.description.abstractLearning descriptions of individual pedestrian is a common goal of both person re-identification (P-ReID) and attribute recognition methods, which are typically differentiated only in terms of their granularity. However, existing P-ReID methods only consider identification labels for individual pedestrian. In this article, we present a multi-scale pyramid attention (MSPA) model for P-ReID that jointly manipulates the complementarity between semantic attributes and visual appearance to address this limitation. The proposed MSPA method mainly comprises three steps. Initially, a backbone model followed by appearance and attribute networks is individually trained to perform P-ReID and pedestrian attribute classification tasks. The attribute network primarily focuses on suppressed image areas associated with soft biometric data while retaining the semantic context among attributes using a convolutional long short-term memory architecture. Additionally, the identification network extracts rich contextual features from an image at varying scales using a residual pyramid module. In the second step, the dual network features are fused, and MSPA is re-trained for the P-ReID task to further improve its complementary capabilities. Finally, we experimentally evaluated the proposed model on the two benchmark datasets Market-1501 and DukeMTMC-reID, and the results show that our approach achieved state-of-the-art performance.en
dc.description.sponsorshipThis work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government, MSIT, under Grant 2023R1A2C1005788.
dc.description.statusPeer reviewed
dc.format.extent12
dc.identifier.citationKhan , S U , Khan , N , Hussain , T , Muhammad , K , Hijji , M , Del Ser , J & Baik , S W 2023 , ' Visual Appearance and Soft Biometrics Fusion for Person Re-Identification Using Deep Learning ' , IEEE Journal on Selected Topics in Signal Processing , vol. 17 , no. 3 , pp. 575-586 . https://doi.org/10.1109/JSTSP.2023.3260627
dc.identifier.doi10.1109/JSTSP.2023.3260627
dc.identifier.issn1932-4553
dc.identifier.urihttps://hdl.handle.net/11556/4916
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85151496540&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofIEEE Journal on Selected Topics in Signal Processing
dc.relation.projectIDMinistry of Science, ICT and Future Planning, MSIP, 2023R1A2C1005788
dc.relation.projectIDNational Research Foundation of Korea, NRF
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsDeep learning
dc.subject.keywordsmulti-view surveillance data
dc.subject.keywordsperson re-identification
dc.subject.keywordssoft biometric
dc.subject.keywordsSignal Processing
dc.subject.keywordsElectrical and Electronic Engineering
dc.titleVisual Appearance and Soft Biometrics Fusion for Person Re-Identification Using Deep Learningen
dc.typejournal article
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