{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T15:00:58Z","timestamp":1767798058985,"version":"3.49.0"},"reference-count":19,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T00:00:00Z","timestamp":1767744000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Applied Digital Transformation Laboratory","award":["UIDP\/06121\/2025"],"award-info":[{"award-number":["UIDP\/06121\/2025"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Gait recognition methods based on silhouette templates, such as the Gait Energy Image (GEI), achieve high accuracy under controlled conditions but often degrade when appearance varies due to viewpoint, clothing, or carried objects. In contrast, skeleton-based approaches provide interpretable motion cues but remain sensitive to pose-estimation noise. This work proposes two compact 2D skeletal descriptors\u2014Gait Skeleton Images (GSIs)\u2014that encode 3D joint trajectories into line-based and joint-based static templates compatible with standard 2D CNN architectures. A unified processing pipeline is introduced, including skeletal topology normalization, rigid view alignment, orthographic projection, and pixel-level rendering. Core design factors are analyzed on the GRIDDS dataset, where depth-based 3D coordinates provide stable ground truth for evaluating structural choices and rendering parameters. An extensive evaluation is then conducted on the widely used CASIA-B dataset, using 3D coordinates estimated via human pose estimation, to assess robustness under viewpoint, clothing, and carrying covariates. Results show that although GEIs achieve the highest same-view accuracy, GSI variants exhibit reduced degradation under appearance changes and demonstrate greater stability under severe cross-view conditions. These findings indicate that compact skeletal templates can complement appearance-based descriptors and may benefit further from continued advances in 3D human pose estimation.<\/jats:p>","DOI":"10.3390\/jimaging12010032","type":"journal-article","created":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T11:46:43Z","timestamp":1767786403000},"page":"32","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hybrid Skeleton-Based Motion Templates for Cross-View and Appearance-Robust Gait Recognition"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5204-4043","authenticated-orcid":false,"given":"Jo\u00e3o Ferreira","family":"Nunes","sequence":"first","affiliation":[{"name":"Escola Superior de Tecnologia e Gest\u00e3o, Instituto Polit\u00e9cnico de Viana do Castelo, Avenida do Atl\u00e2ntico, 4900-348 Viana do Castelo, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8371-0347","authenticated-orcid":false,"given":"Pedro Miguel","family":"Moreira","sequence":"additional","affiliation":[{"name":"ADiT-Lab, Instituto Polit\u00e9cnico de Viana do Castelo, 4900-347 Viana do Castelo, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7603-6526","authenticated-orcid":false,"given":"Jo\u00e3o Manuel R. S.","family":"Tavares","sequence":"additional","affiliation":[{"name":"Instituto de Ci\u00eancia e Inova\u00e7\u00e3o em Engenharia Mec\u00e2nica e Engenharia Industrial, Departamento de Engenharia Mec\u00e2nica, Faculdade de Engenharia, Universidade do Porto, 4200-465 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,7]]},"reference":[{"key":"ref_1","unstructured":"Firman, M. (July, January 26). RGBD Datasets: Past, Present and Future. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition-Workshops, CVPRW, Las Vegas, NV, USA."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Nunes, J.F., Moreira, P.M., and Tavares, J.M.R.S. (2019). Benchmark RGB-D Gait Datasets: A Systematic Review. 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