Khan, Samee UllahKhan, NomanHussain, TanveerMuhammad, KhanHijji, MohammadDel Ser, JavierBaik, Sung Wook2024-09-062024-09-062023-05-01Khan , 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.2023.32606271932-4553https://hdl.handle.net/11556/4916Publisher Copyright: © 2023 IEEE.Learning 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.12enginfo:eu-repo/semantics/restrictedAccessjournal article10.1109/JSTSP.2023.3260627Deep learningmulti-view surveillance dataperson re-identificationsoft biometricSignal ProcessingElectrical and Electronic Engineeringhttp://www.scopus.com/inward/record.url?scp=85151496540&partnerID=8YFLogxK