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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Netherlands
2022
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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. |
format | Online Article Text |
id | pubmed-8744573 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT sharmasahil 3dfacereconstructionindeeplearningeraasurvey AT kumarvijay 3dfacereconstructionindeeplearningeraasurvey |