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GRA_Net: a deep learning model for classification of age and gender from facial images

The problem of gender and age identification has been addressed by many researchers, however, the attention given to it compared to the other related problems of face recognition in particular is far less. The success achieved in this domain has not seen much improvement compared to the other face r...

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Main Authors: Garain, Avishek, Ray, Biswarup, Singh, Pawan Kumar, Ahmadian, Ali, Senu, Norazak, Sarkar, Ram
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers 2021
Online Access:http://psasir.upm.edu.my/id/eprint/97469/1/ABSTRACT.pdf
http://psasir.upm.edu.my/id/eprint/97469/
https://ieeexplore.ieee.org/document/9446083
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spelling my.upm.eprints.974692022-07-27T06:30:09Z http://psasir.upm.edu.my/id/eprint/97469/ GRA_Net: a deep learning model for classification of age and gender from facial images Garain, Avishek Ray, Biswarup Singh, Pawan Kumar Ahmadian, Ali Senu, Norazak Sarkar, Ram The problem of gender and age identification has been addressed by many researchers, however, the attention given to it compared to the other related problems of face recognition in particular is far less. The success achieved in this domain has not seen much improvement compared to the other face recognition problems. Any language in the world has a separate set of words and grammatical rules when addressing people of different ages. The decision associated with its usage, relies on our ability to demarcate these individual characteristics like gender and age from the facial appearances at one glance. With the rapid usage of Artificial Intelligence (AI) based systems in different fields, we expect that such decision making capability of these systems match as much as to the human capability. To this end, in this work, we have designed a deep learning based model, called GRA_Net (Gated Residual Attention Network), for the prediction of age and gender from the facial images. This is a modified and improved version of Residual Attention Network where we have included the concept of Gate in the architecture. Gender identification is a binary classification problem whereas prediction of age is a regression problem. We have decomposed this regression problem into a combination of classification and regression problems for achieving better accuracy. Experiments have been done on five publicly available standard datasets namely FG-Net, Wikipedia, AFAD, UTKFAce and AdienceDB. Obtained results have proven its effectiveness for both age and gender classification, thus making it a proper candidate for the same against any other state-of-the-art methods. Institute of Electrical and Electronics Engineers 2021 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/97469/1/ABSTRACT.pdf Garain, Avishek and Ray, Biswarup and Singh, Pawan Kumar and Ahmadian, Ali and Senu, Norazak and Sarkar, Ram (2021) GRA_Net: a deep learning model for classification of age and gender from facial images. IEEE Access, 9. 85672 - 85689. ISSN 2169-3536 https://ieeexplore.ieee.org/document/9446083 10.1109/ACCESS.2021.3085971
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description The problem of gender and age identification has been addressed by many researchers, however, the attention given to it compared to the other related problems of face recognition in particular is far less. The success achieved in this domain has not seen much improvement compared to the other face recognition problems. Any language in the world has a separate set of words and grammatical rules when addressing people of different ages. The decision associated with its usage, relies on our ability to demarcate these individual characteristics like gender and age from the facial appearances at one glance. With the rapid usage of Artificial Intelligence (AI) based systems in different fields, we expect that such decision making capability of these systems match as much as to the human capability. To this end, in this work, we have designed a deep learning based model, called GRA_Net (Gated Residual Attention Network), for the prediction of age and gender from the facial images. This is a modified and improved version of Residual Attention Network where we have included the concept of Gate in the architecture. Gender identification is a binary classification problem whereas prediction of age is a regression problem. We have decomposed this regression problem into a combination of classification and regression problems for achieving better accuracy. Experiments have been done on five publicly available standard datasets namely FG-Net, Wikipedia, AFAD, UTKFAce and AdienceDB. Obtained results have proven its effectiveness for both age and gender classification, thus making it a proper candidate for the same against any other state-of-the-art methods.
format Article
author Garain, Avishek
Ray, Biswarup
Singh, Pawan Kumar
Ahmadian, Ali
Senu, Norazak
Sarkar, Ram
spellingShingle Garain, Avishek
Ray, Biswarup
Singh, Pawan Kumar
Ahmadian, Ali
Senu, Norazak
Sarkar, Ram
GRA_Net: a deep learning model for classification of age and gender from facial images
author_facet Garain, Avishek
Ray, Biswarup
Singh, Pawan Kumar
Ahmadian, Ali
Senu, Norazak
Sarkar, Ram
author_sort Garain, Avishek
title GRA_Net: a deep learning model for classification of age and gender from facial images
title_short GRA_Net: a deep learning model for classification of age and gender from facial images
title_full GRA_Net: a deep learning model for classification of age and gender from facial images
title_fullStr GRA_Net: a deep learning model for classification of age and gender from facial images
title_full_unstemmed GRA_Net: a deep learning model for classification of age and gender from facial images
title_sort gra_net: a deep learning model for classification of age and gender from facial images
publisher Institute of Electrical and Electronics Engineers
publishDate 2021
url http://psasir.upm.edu.my/id/eprint/97469/1/ABSTRACT.pdf
http://psasir.upm.edu.my/id/eprint/97469/
https://ieeexplore.ieee.org/document/9446083
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score 13.211869

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