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Biometrics: Going 3D

Biometrics have been used to identify humans since the 19th century. Over time, these biometrics became 3D. The main reason for this was the growing need for more features in the images to create more reliable identification models. This work is a comprehensive review of 3D biometrics since 2011 and...

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Autores principales: Samatas, Gerasimos G., Papakostas, George A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9460341/
https://www.ncbi.nlm.nih.gov/pubmed/36080821
http://dx.doi.org/10.3390/s22176364
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author Samatas, Gerasimos G.
Papakostas, George A.
author_facet Samatas, Gerasimos G.
Papakostas, George A.
author_sort Samatas, Gerasimos G.
collection PubMed
description Biometrics have been used to identify humans since the 19th century. Over time, these biometrics became 3D. The main reason for this was the growing need for more features in the images to create more reliable identification models. This work is a comprehensive review of 3D biometrics since 2011 and presents the related work, the hardware used and the datasets available. The first taxonomy of 3D biometrics is also presented. The research was conducted using the Scopus database. Three main categories of 3D biometrics were identified. These were face, hand and gait. The corresponding percentages for these categories were 74.07%, 20.37% and 5.56%, respectively. The face is further categorized into facial, ear, iris and skull, while the hand is divided into fingerprint, finger vein and palm. In each category, facial and fingerprint were predominant, and their respective percentages were 80% and 54.55%. The use of the 3D reconstruction algorithms was also determined. These were stereo vision, structure-from-silhouette (SfS), structure-from-motion (SfM), structured light, time-of-flight (ToF), photometric stereo and tomography. Stereo vision and SfS were the most commonly used algorithms with a combined percentage of 51%. The state of the art for each category and the available datasets are also presented. Finally, multimodal biometrics, generalization of 3D reconstruction algorithms and anti-spoofing metrics are the three areas that should attract scientific interest for further research. In addition, the development of devices with 2D/3D capabilities and more publicly available datasets are suggested for further research.
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spelling pubmed-94603412022-09-10 Biometrics: Going 3D Samatas, Gerasimos G. Papakostas, George A. Sensors (Basel) Review Biometrics have been used to identify humans since the 19th century. Over time, these biometrics became 3D. The main reason for this was the growing need for more features in the images to create more reliable identification models. This work is a comprehensive review of 3D biometrics since 2011 and presents the related work, the hardware used and the datasets available. The first taxonomy of 3D biometrics is also presented. The research was conducted using the Scopus database. Three main categories of 3D biometrics were identified. These were face, hand and gait. The corresponding percentages for these categories were 74.07%, 20.37% and 5.56%, respectively. The face is further categorized into facial, ear, iris and skull, while the hand is divided into fingerprint, finger vein and palm. In each category, facial and fingerprint were predominant, and their respective percentages were 80% and 54.55%. The use of the 3D reconstruction algorithms was also determined. These were stereo vision, structure-from-silhouette (SfS), structure-from-motion (SfM), structured light, time-of-flight (ToF), photometric stereo and tomography. Stereo vision and SfS were the most commonly used algorithms with a combined percentage of 51%. The state of the art for each category and the available datasets are also presented. Finally, multimodal biometrics, generalization of 3D reconstruction algorithms and anti-spoofing metrics are the three areas that should attract scientific interest for further research. In addition, the development of devices with 2D/3D capabilities and more publicly available datasets are suggested for further research. MDPI 2022-08-24 /pmc/articles/PMC9460341/ /pubmed/36080821 http://dx.doi.org/10.3390/s22176364 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Samatas, Gerasimos G.
Papakostas, George A.
Biometrics: Going 3D
title Biometrics: Going 3D
title_full Biometrics: Going 3D
title_fullStr Biometrics: Going 3D
title_full_unstemmed Biometrics: Going 3D
title_short Biometrics: Going 3D
title_sort biometrics: going 3d
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9460341/
https://www.ncbi.nlm.nih.gov/pubmed/36080821
http://dx.doi.org/10.3390/s22176364
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