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Neural Models for 3D Face Generation and Recognition

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dc.contributor.author El-Sayed, Ahmed
dc.contributor.author Sobh, Tarek
dc.contributor.author Mahmood, Ausif
dc.date.accessioned 2022-04-06T15:08:53Z
dc.date.available 2022-04-06T15:08:53Z
dc.date.issued 2022-04-06
dc.identifier.uri https://scholarworks.bridgeport.edu/xmlui/handle/123456789/4559
dc.description.abstract This work introduces a new technique for 3D point clouds generation using a neural modeling system to handle the differences caused by heterogeneous depth cameras, and to generate a new face canonical compact representation. The proposed system reduces the stored 3D dataset size, and if required, provides an accurate dataset regeneration. Furthermore, the system generates neural models for all gallery point clouds and stores these models to represent the faces in the recognition or verification processes. For the probe cloud to be verified, a new model is generated specifically for that particular cloud and is matched against prestored gallery model presentations to identify the query cloud. This work also introduces the utilization of Siamese deep neural network in 3D face verification using generated model representations as raw data for the deep network, and shows that the accuracy of the trained network is comparable to all published results on Bosphorus dataset. en_US
dc.language.iso en_US en_US
dc.subject Neural networks en_US
dc.subject Modeling en_US
dc.subject 3D face verification en_US
dc.title Neural Models for 3D Face Generation and Recognition en_US
dc.type Other en_US
dc.institute.department School of Engineering en_US
dc.institute.name University of Bridgeport en_US
dc.event.location Bridgeport, CT en_US
dc.event.name Faculty Research Day 2022 en_US

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