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Gender Classification and Age Estimation using Neural Networks: A Survey
by
Gangesh Trivedi,
Nitin N. Pise
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 176 - Number 23 |
| Year of Publication: 2020 |
| Authors: Gangesh Trivedi, Nitin N. Pise |
10.5120/ijca2020920251
|
Gangesh Trivedi, Nitin N. Pise . Gender Classification and Age Estimation using Neural Networks: A Survey. International Journal of Computer Applications. 176, 23 ( May 2020), 34-41. DOI=10.5120/ijca2020920251
@article{
10.5120/ijca2020920251,
author = {
Gangesh Trivedi,
Nitin N. Pise
},
title = { Gender Classification and Age Estimation using Neural Networks: A Survey },
journal = {
International Journal of Computer Applications
},
issue_date = { May 2020 },
volume = { 176 },
number = { 23 },
month = { May },
year = { 2020 },
issn = { 0975-8887 },
pages = {
34-41
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume176/number23/31341-2020920251/
},
doi = { 10.5120/ijca2020920251 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:43:19.751709+05:30
%A Gangesh Trivedi
%A Nitin N. Pise
%T Gender Classification and Age Estimation using Neural Networks: A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 176
%N 23
%P 34-41
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Researchers have shown more interest in soft biometrics area to fill the commination gaps between humans and machines with the growth of real-world application has increased day to day life. Soft-biometric consists of age, gender, ethnicity, height, facial measurements and etc. This paper contains a detail discussion about the contribution of the researchers in the area of gender classification and age estimation using neural networking. Most of the work is done using Convolutional neural networks and auto encoders. Various elements related to neural network model such as dataset, findings, calculative metrics and results are embraced for effortless interpretation of tabular correlation research. Finally, the authors summarize germane tasks for future various research aspects.
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Index Terms
Computer Science
Information Sciences
Keywords