Neighbour-based bag-of-samplings for person identification through handwritten dynamics and convolutional neural networks

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorVargem Limpa-
Autor(es): dc.creatorde Rosa, Gustavo H.-
Autor(es): dc.creatorRoder, Mateus-
Autor(es): dc.creatorPapa, João P.-
Data de aceite: dc.date.accessioned2025-08-21T15:51:14Z-
Data de disponibilização: dc.date.available2025-08-21T15:51:14Z-
Data de envio: dc.date.issued2022-05-01-
Data de envio: dc.date.issued2022-05-01-
Data de envio: dc.date.issued2022-05-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1111/exsy.12891-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/233847-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/233847-
Descrição: dc.descriptionBiometric recognition provides straightforward methods to deal with the problem of identifying people under certain circumstances. Additionally, a well-calibrated biometric system enhances security policies and prevents malicious attempts, such as fraud or identity theft. Deep learning has arisen to foster the problem by extracting high-level features that compose the so-called ‘user fingerprint’, that is, digital identification of a particular individual. Nevertheless, personal identification is not a trivial task, as many traits might define an individual, varying according to the task's domain. An exciting way to overcome such a problem is to employ handwritten dynamics, which are hand- and motor-based signals from an individual's writing style and obtained through a biometric smartpen. In this work, we propose using such signals to identify an individual through convolutional neural networks. Essentially, the proposed work uses a neighbour-based bag-of-samplings procedure to sample the signals to a fixed size and feeds them into a neural network responsible for extracting their features and further classifying them. The experiments were conducted over two handwritten dynamic datasets, NewHandPD and SignRec, and established new fruitful state-of-the-art concerning these particular datasets and the corresponding context.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionDepartment of Computing São Paulo State University-
Descrição: dc.descriptionAv. Eng. Luís Edmundo Carrijo Coube 14-01 Vargem Limpa, SP-
Descrição: dc.descriptionDepartment of Computing São Paulo State University-
Descrição: dc.descriptionFAPESP: 2013/07375-0-
Descrição: dc.descriptionFAPESP: 2014/12236-1-
Descrição: dc.descriptionFAPESP: 2019/02205-5-
Descrição: dc.descriptionFAPESP: 2019/07665-4-
Descrição: dc.descriptionFAPESP: 2020/12101-0-
Descrição: dc.descriptionCNPq: 307066/2017-7-
Descrição: dc.descriptionCNPq: 427968/2018-6-
Idioma: dc.languageen-
Relação: dc.relationExpert Systems-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectbag-of-samplings-
Palavras-chave: dc.subjectbiometrics-
Palavras-chave: dc.subjectconvolutional neural networks-
Palavras-chave: dc.subjecthandwritten dynamics-
Palavras-chave: dc.subjectperson identification-
Título: dc.titleNeighbour-based bag-of-samplings for person identification through handwritten dynamics and convolutional neural networks-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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