Gait Recognition Based on Deep Learning: A Survey

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Autor(es): dc.contributorUniversidade Federal de São Carlos (UFSCar)-
Autor(es): dc.contributorEldorado Research Institute-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorPetroleo Brasileiro S.A. - Petrobras-
Autor(es): dc.creatorFilipi Gonçalves Dos Santos, Claudio-
Autor(es): dc.creatorOliveira, Diego De Souza-
Autor(es): dc.creatorPassos, Leandro A.-
Autor(es): dc.creatorGonçalves Pires, Rafael-
Autor(es): dc.creatorFelipe Silva Santos, Daniel-
Autor(es): dc.creatorPascotti Valem, Lucas-
Autor(es): dc.creatorMoreira, Thierry P.-
Autor(es): dc.creatorSantana, Marcos Cleison S.-
Autor(es): dc.creatorRoder, Mateus-
Autor(es): dc.creatorPaulo Papa, Jo-
Autor(es): dc.creatorColombo, Danilo-
Data de aceite: dc.date.accessioned2025-08-21T17:35:26Z-
Data de disponibilização: dc.date.available2025-08-21T17:35:26Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-03-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1145/3490235-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/245876-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/245876-
Descrição: dc.descriptionIn general, biometry-based control systems may not rely on individual expected behavior or cooperation to operate appropriately. Instead, such systems should be aware of malicious procedures for unauthorized access attempts. Some works available in the literature suggest addressing the problem through gait recognition approaches. Such methods aim at identifying human beings through intrinsic perceptible features, despite dressed clothes or accessories. Although the issue denotes a relatively long-time challenge, most of the techniques developed to handle the problem present several drawbacks related to feature extraction and low classification rates, among other issues. However, deep learning-based approaches recently emerged as a robust set of tools to deal with virtually any image and computer-vision-related problem, providing paramount results for gait recognition as well. Therefore, this work provides a surveyed compilation of recent works regarding biometric detection through gait recognition with a focus on deep learning approaches, emphasizing their benefits and exposing their weaknesses. Besides, it also presents categorized and characterized descriptions of the datasets, approaches, and architectures employed to tackle associated constraints.-
Descrição: dc.descriptionFederal Institute of São Carlos - UFSCar Brazil and Eldorado Research Institute, Rod. Washington Luiz, 235, São Carlos-
Descrição: dc.descriptionEldorado Research Institute, Av. Alan Turing, 275, Campinas-
Descrição: dc.descriptionSão Paulo State University - UNESP, Av. Eng. Luís Edmundo Carrijo Coube, 14-01, Bauru-
Descrição: dc.descriptionCenpes Petroleo Brasileiro S.A. - Petrobras-
Descrição: dc.descriptionSão Paulo State University - UNESP, Av. Eng. Luís Edmundo Carrijo Coube, 14-01, Bauru-
Idioma: dc.languageen-
Relação: dc.relationACM Computing Surveys-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectbiometrics-
Palavras-chave: dc.subjectdeep learning-
Palavras-chave: dc.subjectGait recognition-
Título: dc.titleGait Recognition Based on Deep Learning: A Survey-
Tipo de arquivo: dc.typevídeo-
Aparece nas coleções:Repositório Institucional - Unesp

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