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.contributorMedia Integration and Communication Center-
Autor(es): dc.creatorCardia Neto, Joao Baptista-
Autor(es): dc.creatorNilceu Marana, Aparecido [UNESP]-
Autor(es): dc.creatorFerrari, Claudio-
Autor(es): dc.creatorBerretti, Stefano-
Autor(es): dc.creatorDel Bimbo, Alberto-
Data de aceite: dc.date.accessioned2022-02-22T00:25:00Z-
Data de disponibilização: dc.date.available2022-02-22T00:25:00Z-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2019-06-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/ICB45273.2019.8987432-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/198598-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/198598-
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 handcrafted 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.descriptionSão Carlos Federal University - UFSCAR-
Descrição: dc.descriptionUNESP - São Paulo State University-
Descrição: dc.descriptionUniversity of Florence Media Integration and Communication Center-
Descrição: dc.descriptionUNESP - São Paulo State University-
Idioma: dc.languageen-
Relação: dc.relation2019 International Conference on Biometrics, ICB 2019-
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Título: dc.titleDeep Learning from 3DLBP Descriptors for Depth Image Based Face Recognition-
Aparece nas coleções:Repositório Institucional - Unesp

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