Deep Learning from 3DLBP Descriptors for Depth Image Based Face Recognition

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Autor(es): dc.contributorUniversidade Federal de São Carlos (UFSCar)-
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorUniv Florence-
Autor(es): dc.creatorCardia Neto, Joao Baptista-
Autor(es): dc.creatorMarana, Aparecido Nilceu [UNESP]-
Autor(es): dc.creatorFerrari, Claudio-
Autor(es): dc.creatorBerretti, Stefano-
Autor(es): dc.creatorDel Bimbo, Alberto-
Autor(es): dc.creatorIEEE-
Data de aceite: dc.date.accessioned2022-02-22T00:21:26Z-
Data de disponibilização: dc.date.available2022-02-22T00:21:26Z-
Data de envio: dc.date.issued2020-12-10-
Data de envio: dc.date.issued2020-12-10-
Data de envio: dc.date.issued2019-01-01-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/197821-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/197821-
Descrição: dc.descriptionIn this paper, we propose a new framework for face recognition from depth images, which is both effective and efficient. It consists of two main stages: First, a hand-crafted low-level feature extractor is applied to the raw depth data of the face, thus extracting the corresponding descriptor images (DIs); Then, a not-so-deep (shallow) convolutional neural network (SCNN) has been designed that learns from the DIs. This architecture showed two main advantages over the direct application of deep-CNN (DCNN) to the depth images of the face: On the one hand, the DIs are capable of enriching the raw depth data, emphasizing relevant traits of the face, while reducing their acquisition noise. This resulted decisive in improving the learning capability of the network; On the other, the DIs capture low-level features of the face, thus playing the role for the SCNN as the first layers do in a DCNN architecture. In this way, the SCNN we have designed has much less layers and can be trained more easily and faster. Extensive experiments on low- and high-resolution depth face datasets confirmed us the above advantages, showing results that are comparable or superior to the state-of-the-art, using by far less training data, time, and memory occupancy of the network.-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionSao Carlos Fed Univ UFSCAR, BR-13565905 Sao Carlos, SP, Brazil-
Descrição: dc.descriptionUNESP Sao Paulo State Univ, BR-17033360 Bauru, SP, Brazil-
Descrição: dc.descriptionUniv Florence, Media Integrat & Commun Ctr, Florence, Italy-
Descrição: dc.descriptionUNESP Sao Paulo State Univ, BR-17033360 Bauru, SP, Brazil-
Formato: dc.format7-
Idioma: dc.languageen-
Publicador: dc.publisherIeee-
Relação: dc.relation2019 International Conference On Biometrics (icb)-
???dc.source???: dc.sourceWeb of Science-
Título: dc.titleDeep Learning from 3DLBP Descriptors for Depth Image Based Face Recognition-
Aparece nas coleções:Repositório Institucional - Unesp

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