Cross-Domain Deep Face Matching for Real Banking Security Systems

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual de Campinas (UNICAMP)-
Autor(es): dc.contributorUniversity of Brasilia (UnB)-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.creatorOliveira, Johnatan S.-
Autor(es): dc.creatorSouza, Gustavo B.-
Autor(es): dc.creatorRocha, Anderson R. [UNESP]-
Autor(es): dc.creatorDeus, Flavio E.-
Autor(es): dc.creatorMarana, Aparecido N.-
Data de aceite: dc.date.accessioned2022-02-22T00:26:48Z-
Data de disponibilização: dc.date.available2022-02-22T00:26:48Z-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-04-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/ICEDEG48599.2020.9096783-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/199228-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/199228-
Descrição: dc.descriptionEnsuring the security of transactions is currently one of the major challenges that banking systems deal with. The usage of face for biometric authentication of users is attracting large investments from banks worldwide due to its convenience and acceptability by people, especially in cross-domain scenarios, in which facial images from ID documents are compared with digital self-portraits (selfies) for the automated opening of new checking accounts, e.g, or financial transactions authorization. Actually, the comparison of selfies and IDs has also been applied in another wide variety of tasks nowadays, such as automated immigration control. The major difficulty in such process consists in attenuating the differences between the facial images compared given their different domains. In this work, in addition to collecting a large cross-domain face dataset, with 27,002 real facial images of selfies and ID documents (13,501 subjects) captured from the databases of the major public Brazilian bank, we propose a novel architecture for such cross-domain matching problem based on deep features extracted by two well-referenced Convolutional Neural Networks (CNN). Results obtained on the dataset collected, called FaceBank, with accuracy rates higher than 93%, demonstrate the robustness of the proposed approach to the cross-domain face matching problem and its feasible application in real banking security systems.-
Descrição: dc.descriptionInstitute of Computing University of Campinas (Unicamp)-
Descrição: dc.descriptionUniversity of Brasilia (UnB) Department of Electrical Engineering-
Descrição: dc.descriptionSao Paulo State University (Unesp) Department of Computing-
Descrição: dc.descriptionSao Paulo State University (Unesp) Department of Computing-
Formato: dc.format21-28-
Idioma: dc.languageen-
Relação: dc.relation2020 7th International Conference on eDemocracy and eGovernment, ICEDEG 2020-
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Título: dc.titleCross-Domain Deep Face Matching for Real Banking Security Systems-
Aparece nas coleções:Repositório Institucional - Unesp

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