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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers
2021
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| Online Access: | http://psasir.upm.edu.my/id/eprint/97469/1/ABSTRACT.pdf |
| _version_ | 1825937873238491136 |
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| author | Garain, Avishek Ray, Biswarup Singh, Pawan Kumar Ahmadian, Ali Senu, Norazak Sarkar, Ram |
| author_facet | Garain, Avishek Ray, Biswarup Singh, Pawan Kumar Ahmadian, Ali Senu, Norazak Sarkar, Ram |
| author_sort | Garain, Avishek |
| collection | UPM |
| 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. |
| first_indexed | 2024-03-06T11:06:01Z |
| format | Article |
| id | upm.eprints-97469 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T11:06:01Z |
| publishDate | 2021 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | 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 |
| 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 |
| title | 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_short | 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 |
| url | http://psasir.upm.edu.my/id/eprint/97469/1/ABSTRACT.pdf |
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