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3D Face Reconstruction in Deep Learning Era: A Survey

3D face reconstruction is the most captivating topic in biometrics with the advent of deep learning and readily available graphical processing units. This paper explores the various aspects of 3D face reconstruction techniques. Five techniques have been discussed, namely, deep learning, epipolar geo...

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Detalles Bibliográficos
Autores principales: Sharma, Sahil, Kumar, Vijay
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8744573/
https://www.ncbi.nlm.nih.gov/pubmed/35035213
http://dx.doi.org/10.1007/s11831-021-09705-4
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author Sharma, Sahil
Kumar, Vijay
author_facet Sharma, Sahil
Kumar, Vijay
author_sort Sharma, Sahil
collection PubMed
description 3D face reconstruction is the most captivating topic in biometrics with the advent of deep learning and readily available graphical processing units. This paper explores the various aspects of 3D face reconstruction techniques. Five techniques have been discussed, namely, deep learning, epipolar geometry, one-shot learning, 3D morphable model, and shape from shading methods. This paper provides an in-depth analysis of 3D face reconstruction using deep learning techniques. The performance analysis of different face reconstruction techniques has been discussed in terms of software, hardware, pros and cons. The challenges and future scope of 3d face reconstruction techniques have also been discussed.
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spelling pubmed-87445732022-01-10 3D Face Reconstruction in Deep Learning Era: A Survey Sharma, Sahil Kumar, Vijay Arch Comput Methods Eng Review Article 3D face reconstruction is the most captivating topic in biometrics with the advent of deep learning and readily available graphical processing units. This paper explores the various aspects of 3D face reconstruction techniques. Five techniques have been discussed, namely, deep learning, epipolar geometry, one-shot learning, 3D morphable model, and shape from shading methods. This paper provides an in-depth analysis of 3D face reconstruction using deep learning techniques. The performance analysis of different face reconstruction techniques has been discussed in terms of software, hardware, pros and cons. The challenges and future scope of 3d face reconstruction techniques have also been discussed. Springer Netherlands 2022-01-10 2022 /pmc/articles/PMC8744573/ /pubmed/35035213 http://dx.doi.org/10.1007/s11831-021-09705-4 Text en © The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Review Article
Sharma, Sahil
Kumar, Vijay
3D Face Reconstruction in Deep Learning Era: A Survey
title 3D Face Reconstruction in Deep Learning Era: A Survey
title_full 3D Face Reconstruction in Deep Learning Era: A Survey
title_fullStr 3D Face Reconstruction in Deep Learning Era: A Survey
title_full_unstemmed 3D Face Reconstruction in Deep Learning Era: A Survey
title_short 3D Face Reconstruction in Deep Learning Era: A Survey
title_sort 3d face reconstruction in deep learning era: a survey
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8744573/
https://www.ncbi.nlm.nih.gov/pubmed/35035213
http://dx.doi.org/10.1007/s11831-021-09705-4
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