{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T15:12:31Z","timestamp":1787497951426,"version":"build-2736575974"},"reference-count":86,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T00:00:00Z","timestamp":1748563200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Qatar Research Development and Innovation Council","award":["ARG01-0522-230274"],"award-info":[{"award-number":["ARG01-0522-230274"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Human gait identification is a biometric technique that permits recognizing an individual from a long distance focusing on numerous features such as movement, time, and clothing. This approach in particular is highly useful in video surveillance scenarios, where biometric systems allow people to be easily recognized without intruding on their privacy. In the domain of computer vision, one of the essential and most difficult tasks is tracking a person across multiple camera views, specifically, recognizing the similar person in diverse scenes. However, the accuracy of the gait identification system is significantly affected by covariate factors, such as different view angles, clothing, walking speeds, occlusion, and low-lighting conditions. Previous studies have often overlooked the influence of these factors, leaving a gap in the comprehensive understanding of gait recognition systems. This paper provides a comprehensive review of the most effective gait recognition methods, assessing their performance across various image source databases while highlighting the limitations of existing datasets. Additionally, it explores the influence of key covariate factors, such as viewing angle, clothing, and environmental conditions, on model performance. The paper also compares traditional gait recognition methods with advanced deep learning techniques, offering theoretical insights into the impact of covariates and addressing real-world application challenges. The contrasts and discussions presented provide valuable insights for developing a robust and improved gait-based identification framework for future advancements.<\/jats:p>","DOI":"10.3390\/s25113471","type":"journal-article","created":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T08:34:13Z","timestamp":1748853253000},"page":"3471","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Person Recognition via Gait: A Review of Covariate Impact and Challenges"],"prefix":"10.3390","volume":"25","author":[{"given":"Abdul Basit","family":"Mughal","sequence":"first","affiliation":[{"name":"Department of Computer Science, Bahria University, Islamabad P.O. Box 44000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rafi Ullah","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Electrical & Computer Engineering, Pak Austria Fachhochschule: Institute of Applied Sciences and Technology, Haripur P.O. Box 22620, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amine","family":"Bermak","sequence":"additional","affiliation":[{"name":"Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha P.O Box 5825, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0248-7919","authenticated-orcid":false,"given":"Atiq ur","family":"Rehman","sequence":"additional","affiliation":[{"name":"Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha P.O Box 5825, Qatar"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"012069","DOI":"10.1088\/1757-899X\/53\/1\/012069","article-title":"Human gait recognition and classification using similarity index for various conditions","volume":"53","author":"Makhdoomi","year":"2013","journal-title":"IOP Conf. 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