{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T05:08:46Z","timestamp":1787029726061,"version":"build-2736575974"},"reference-count":44,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T00:00:00Z","timestamp":1778025600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National College Students\u2019 Innovation and Entrepreneurship Training Program \u201cPerson Re-Identification Method Based on Hierarchical Graph Contrastive Learning\u201d","award":["202510616031"],"award-info":[{"award-number":["202510616031"]}]},{"name":"National College Students\u2019 Innovation and Entrepreneurship Training Program \u201cGraph Attention Person Re-Identification Method Based on Non-Adjacent Nodes\u201d","award":["202410616018"],"award-info":[{"award-number":["202410616018"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Occluded person re-identification (Re-ID) aims to accurately match occluded pedestrian images against complete gallery images captured across multiple cameras, a task that is critical to public security and intelligent surveillance systems. Existing graph neural network (GNN)-based methods typically assign uniform aggregation weights to all nodes, failing to reflect the inherent reliability difference between visible and occluded body regions, which allows noise from low-confidence nodes to propagate freely and corrupt the final pedestrian representation. To address this, we propose the Self-Information-Driven Gated Graph Convolutional Network (SI-GCN). Keypoint detection confidence scores are transformed into logarithmic self-information measures as uncertainty priors for a learnable gating mechanism. The proposed SIG module enables visible nodes to dominate information diffusion while occluded nodes absorb more from neighbors, achieving efficient feature updating. A dynamic confidence calibration (DCC) strategy further synchronizes node reliability estimates with feature evolution across successive GCN layers. Extensive experiments on six public benchmarks covering occluded, partial, and holistic Re-ID scenarios demonstrate that SI-GCN achieves state-of-the-art performance, with Rank-1 accuracy and mAP improvements of 1.2% and 0.9%, respectively, over the strongest baseline on the Occluded-REID dataset, demonstrating its strong potential for deployment in real-world public security and urban surveillance applications where occlusion is pervasive.<\/jats:p>","DOI":"10.3390\/s26092901","type":"journal-article","created":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T09:03:37Z","timestamp":1778144617000},"page":"2901","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Self-Information-Driven Gated Graph Convolutional Network for Occluded Person Re-Identification"],"prefix":"10.3390","volume":"26","author":[{"given":"Wanran","family":"Guo","sequence":"first","affiliation":[{"name":"School of Mathematical Sciences, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiake","family":"Meng","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Xue","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Chengdu University of Technology, Chengdu 610059, China"},{"name":"Geomathematics Key Laboratory of Sichuan Province, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaxian","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenyu","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Chengdu University of Technology, Chengdu 610059, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2872","DOI":"10.1109\/TPAMI.2021.3054775","article-title":"Deep learning for person re-identification: A survey and outlook","volume":"44","author":"Ye","year":"2021","journal-title":"IEEE Trans. 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