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LIPSNN: A Light Intrusion-Proving Siamese Neural Network Model for Facial Verification

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dc.contributor.authorAlcaide, Asier
dc.contributor.authorPatricio Guisado, Miguel Ángel
dc.contributor.authorBerlanga de Jesús, Antonio
dc.contributor.authorArroyo, Angel
dc.contributor.authorCuadrado Gallego, Juan José
dc.contributor.funderComunidad de Madrides
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.contributor.funderUniversidad Carlos III de Madrides
dc.date.accessioned2023-11-08T11:28:33Z
dc.date.available2023-11-08T11:28:33Z
dc.date.issued2022-06-01
dc.description.abstractFacial verification has experienced a breakthrough in recent years, not only due to the improvement in accuracy of the verification systems but also because of their increased use. One of the main reasons for this has been the appearance and use of new models of Deep Learning to address this problem. This extension in the use of facial verification has had a high impact due to the importance of its applications, especially on security, but the extension of its use could be significantly higher if the problem of the required complex calculations needed by the Deep Learning models, that usually need to be executed on machines with specialised hardware, were solved. That would allow the use of facial verification to be extended, making it possible to run this software on computers with low computing resources, such as Smartphones or tablets. To solve this problem, this paper presents the proposal of a new neural model, called Light Intrusion-Proving Siamese Neural Network, LIPSNN. This new light model, which is based on Siamese Neural Networks, is fully presented from the description of its two block architecture, going through its development, including its training with the well- known dataset Labeled Faces in the Wild, LFW; to its benchmarking with other traditional and deep learning models for facial verification in order to compare its performance for its use in low computing resources systems for facial recognition. For this comparison the attribute parameters, storage, accuracy and precision have been used, and from the results obtained it can be concluded that the LIPSNN can be an alternative to the existing models to solve the facet problem of running facial verification in low computing resource devices.en
dc.description.sponsorshipThis study was funded by the private research project of Company BQ, the public research projects of the Spanish Ministry of Economy and Competitiveness (MINECO), references TEC2017-88048-C2-2-R, RTC-2016-5595-2, RTC-2016-5191-8 and RTC-2016-5059-8, and the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual agreement with UC3M in the line of Excellence of University Professors (EPUC3M17), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation).en
dc.identifier.bibliographicCitationAlcaide, A., Patricio, M.A., Berlanga, A., Arroyo A., Cuadrado Gallego, Juan J. (2022). LIPSNN: A Light Intrusion-Proving Siamese Neural Network Model for Facial Verification. IJIMAI, 7 (4), 121-131. https://doi.org/10.9781/ijimai.2021.11.003en
dc.identifier.doihttps://doi.org/10.9781/ijimai.2021.11.003
dc.identifier.issn1989-1660
dc.identifier.publicationfirstpage121
dc.identifier.publicationissue4
dc.identifier.publicationlastpage131
dc.identifier.publicationtitleInternational Journal of Interactive Multimedia and Artificial Intelligenceen
dc.identifier.publicationvolume7
dc.identifier.urihttps://hdl.handle.net/10016/38793
dc.identifier.uxxiAR/0000030622
dc.language.isoeng
dc.publisherUNIR La Universidad en Interneten
dc.relation.projectIDGobierno de España. TEC-2017-88048-C2-2-Res
dc.relation.projectIDGobierno de España. RTC-2016-5595-2es
dc.relation.projectIDGobierno de España. RTC-2016-5191-8es
dc.relation.projectIDGobierno de España. RTC-2016-5059-8es
dc.rights© UNIR, 2022
dc.rightsAtribución 3.0 España
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/
dc.subject.ecienciaInformáticaes
dc.subject.otherfacial verificationen
dc.subject.otherdeep learningen
dc.subject.otherartificial neural networksen
dc.subject.othersiamese neural networksen
dc.titleLIPSNN: A Light Intrusion-Proving Siamese Neural Network Model for Facial Verificationen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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