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Review: Single attribute and multi attribute facial gender and age estimation
Facial age and gender recognition have vital applications as consumer profile prediction, social media advertisement, human-computer interaction, image retrieval system, demographic profiling, customized advertisement systems, security and surveillance. This paper presents a study on Single Attribut...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9200214/ https://www.ncbi.nlm.nih.gov/pubmed/35729932 http://dx.doi.org/10.1007/s11042-022-12678-6 |
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author | Gupta, Sandeep Kumar Nain, Neeta |
author_facet | Gupta, Sandeep Kumar Nain, Neeta |
author_sort | Gupta, Sandeep Kumar |
collection | PubMed |
description | Facial age and gender recognition have vital applications as consumer profile prediction, social media advertisement, human-computer interaction, image retrieval system, demographic profiling, customized advertisement systems, security and surveillance. This paper presents a study on Single Attribute (Attribute: either Gender or Age) and Multi-Attribute (both Gender and Age) prediction model. We present a review for facial age estimation and gender classification methods based on conventional as well as deep learning approaches developed so far with analysis of their pros, cons and insights for future research. Moreover, this study also enlists the databases used for benchmarking results with their properties for both constrained and unconstrained environment. |
format | Online Article Text |
id | pubmed-9200214 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-92002142022-06-17 Review: Single attribute and multi attribute facial gender and age estimation Gupta, Sandeep Kumar Nain, Neeta Multimed Tools Appl Article Facial age and gender recognition have vital applications as consumer profile prediction, social media advertisement, human-computer interaction, image retrieval system, demographic profiling, customized advertisement systems, security and surveillance. This paper presents a study on Single Attribute (Attribute: either Gender or Age) and Multi-Attribute (both Gender and Age) prediction model. We present a review for facial age estimation and gender classification methods based on conventional as well as deep learning approaches developed so far with analysis of their pros, cons and insights for future research. Moreover, this study also enlists the databases used for benchmarking results with their properties for both constrained and unconstrained environment. Springer US 2022-06-15 2023 /pmc/articles/PMC9200214/ /pubmed/35729932 http://dx.doi.org/10.1007/s11042-022-12678-6 Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 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 | Article Gupta, Sandeep Kumar Nain, Neeta Review: Single attribute and multi attribute facial gender and age estimation |
title | Review: Single attribute and multi attribute facial gender and age estimation |
title_full | Review: Single attribute and multi attribute facial gender and age estimation |
title_fullStr | Review: Single attribute and multi attribute facial gender and age estimation |
title_full_unstemmed | Review: Single attribute and multi attribute facial gender and age estimation |
title_short | Review: Single attribute and multi attribute facial gender and age estimation |
title_sort | review: single attribute and multi attribute facial gender and age estimation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9200214/ https://www.ncbi.nlm.nih.gov/pubmed/35729932 http://dx.doi.org/10.1007/s11042-022-12678-6 |
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