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Learning the hash code with generalised regression neural networks for handwritten signature biometric data retrieval

2015-10Conference paper Restricted access
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datacite.subject.fosEngenharia e Tecnologia
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
datacite.subject.sdg10:Reduzir as Desigualdades
dc.contributor.authorRibeiro, Bernardete
dc.contributor.authorLopes, Noel
dc.contributor.authorSilva, Catarina
dc.date.accessioned2025-06-16T15:20:33Z
dc.date.available2025-06-16T15:20:33Z
dc.date.issued2015-10
dc.descriptionPublished in: 2015 International Joint Conference on Neural Networks (IJCNN). Date of Conference: 12-17 July 2015.
dc.description.abstractHandwritten signature recognition is one important component of biometric authentication. This is a central process in a broad range of areas requiring personal identification, such as security, legal contracts and bank transactions. Extensive efforts have been put into the research towards the verification of handwritten signatures, which contain biometric information. Although many successful methods have been used, they often disregard the size of databases, which can be very large, posing scalability problems to their application in real-world scenarios. To overcome this problem, in this paper, we use binary embeddings of high-dimensional data which is an efficient tool for indexing big datasets of biometric images. The rationale is to find a good hash function such that similar data points in Euclidean space preserve their similarities in the resulting Hamming space for fast data retrieval and state-of-the-art classification performance. In the settings of an handwritten signature retrieval system, an indexing hashing-based scheme is presented. We propose to learn k-bits hash code with a generalised regression neural network (GRNN), which yielded competitive results in the GPDS database.por
dc.identifier.citationB. Ribeiro, N. Lopes and C. Silva, "Learning the hash code with generalised regression neural networks for handwritten signature biometric data retrieval," 2015 International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland, 2015, pp. 1-6, doi: 10.1109/IJCNN.2015.7280707.
dc.identifier.doi10.1109/ijcnn.2015.7280707
dc.identifier.isbn978-1-4799-1960-4
dc.identifier.issn2161-4407
dc.identifier.urihttp://hdl.handle.net/10400.8/13267
dc.language.isoeng
dc.peerreviewedyes
dc.publisherIEEE
dc.relation.hasversionhttps://ieeexplore.ieee.org/document/7280707
dc.relation.ispartof2015 International Joint Conference on Neural Networks (IJCNN)
dc.rights.uriN/A
dc.subjectAccuracy
dc.subjectBiological system modeling
dc.subjectDatabases
dc.subjectImage analysis
dc.subjectMeasurement
dc.titleLearning the hash code with generalised regression neural networks for handwritten signature biometric data retrievaleng
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.conferenceDate2015-07
oaire.citation.conferencePlaceKillarney, Ireland
oaire.citation.title2015 International Joint Conference on Neural Networks (IJCNN)
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameSilva
person.givenNameCatarina
person.identifier.orcid0000-0002-5656-0061
relation.isAuthorOfPublicationee28e079-5ca7-4842-9094-372c40f75c38
relation.isAuthorOfPublication.latestForDiscoveryee28e079-5ca7-4842-9094-372c40f75c38

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