Advances in Intelligent Systems and Computing, pages 30-40
Age and Gender Recognition on Imbalanced Dataset of Face Images with Deep Learning
Publication type: Book Chapter
Publication date: 2020-06-22
Quartile SCImago
— Quartile WOS
—
Impact factor: —
ISSN: 21945357
Abstract
The paper describes usage of deep neural networks based on ResNet and Xception architectures for recognition of age and gender of imbalanced dataset of face images. Described dataset collection process from open sources. Training sample contains more than 210000 images. Testing sample have more 1700 special selected face images with different ages and genders. Training data has imbalanced number of images per class. Accuracy for gender classification and mean absolute error for age estimation are used to analyze results quality. Age recognition is described as classification task with 101 classes. Gender recognition is solved as classification task with two categories. Paper contains analysis of different approaches to data balancing and their influence to recognition results. The computing experiment was carried out on a graphics processor using NVidia CUDA technology. The average recognition time per image is estimated for different deep neural networks. Obtained results can be used in software for public space monitoring, collection of visiting statistics etc.
Citations by journals
|
1
|
|
|
Expert Systems with Applications
|
Expert Systems with Applications
1 publication, 50%
|
|
1
|
Citations by publishers
|
1
|
|
|
Elsevier
|
Elsevier
1 publication, 50%
|
|
IEEE
|
IEEE
1 publication, 50%
|
|
1
|
- We do not take into account publications that without a DOI.
- Statistics recalculated only for publications connected to researchers, organizations and labs registered on the platform.
- Statistics recalculated weekly.
{"yearsCitations":{"type":"bar","data":{"show":true,"labels":[2022,2023],"ids":[0,0],"codes":[0,0],"imageUrls":["",""],"datasets":[{"label":"Citations number","data":[1,1],"backgroundColor":["#3B82F6","#3B82F6"],"percentage":["50","50"],"barThickness":null}]},"options":{"indexAxis":"x","maintainAspectRatio":true,"scales":{"y":{"ticks":{"precision":0,"autoSkip":false,"font":{"family":"Montserrat"},"color":"#000000"}},"x":{"ticks":{"stepSize":1,"precision":0,"font":{"family":"Montserrat"},"color":"#000000"}}},"plugins":{"legend":{"position":"top","labels":{"font":{"family":"Montserrat"},"color":"#000000"}},"title":{"display":true,"text":"Citations per year","font":{"size":24,"family":"Montserrat","weight":600},"color":"#000000"}}}},"journals":{"type":"bar","data":{"show":true,"labels":["Expert Systems with Applications"],"ids":[7342],"codes":[0],"imageUrls":["\/storage\/images\/resized\/GDnYOu1UpMMfMMRV6Aqle4H0YLLsraeD9IP9qScG_medium.webp"],"datasets":[{"label":"","data":[1],"backgroundColor":["#3B82F6"],"percentage":[50],"barThickness":13}]},"options":{"indexAxis":"y","maintainAspectRatio":false,"scales":{"y":{"ticks":{"precision":0,"autoSkip":false,"font":{"family":"Montserrat"},"color":"#000000"}},"x":{"ticks":{"stepSize":null,"precision":0,"font":{"family":"Montserrat"},"color":"#000000"}}},"plugins":{"legend":{"position":"top","labels":{"font":{"family":"Montserrat"},"color":"#000000"}},"title":{"display":true,"text":"Journals","font":{"size":24,"family":"Montserrat","weight":600},"color":"#000000"}}}},"publishers":{"type":"bar","data":{"show":true,"labels":["Elsevier","IEEE"],"ids":[17,6953],"codes":[0,0],"imageUrls":["\/storage\/images\/resized\/GDnYOu1UpMMfMMRV6Aqle4H0YLLsraeD9IP9qScG_medium.webp","\/storage\/images\/resized\/6scCJegesojp2jubwY3uKCzTAmgsaH2GIFlg6Hfk_medium.webp"],"datasets":[{"label":"","data":[1,1],"backgroundColor":["#3B82F6","#3B82F6"],"percentage":[50,50],"barThickness":13}]},"options":{"indexAxis":"y","maintainAspectRatio":false,"scales":{"y":{"ticks":{"precision":0,"autoSkip":false,"font":{"family":"Montserrat"},"color":"#000000"}},"x":{"ticks":{"stepSize":null,"precision":0,"font":{"family":"Montserrat"},"color":"#000000"}}},"plugins":{"legend":{"position":"top","labels":{"font":{"family":"Montserrat"},"color":"#000000"}},"title":{"display":true,"text":"Publishers","font":{"size":24,"family":"Montserrat","weight":600},"color":"#000000"}}}}}
Metrics
Cite this
GOST |
RIS |
BibTex
Cite this
GOST
Copy
Yudin D., Shchendrygin M., Dolzhenko A. V. Age and Gender Recognition on Imbalanced Dataset of Face Images with Deep Learning // Advances in Intelligent Systems and Computing. 2020. pp. 30-40.
GOST all authors (up to 50)
Copy
Yudin D., Shchendrygin M., Dolzhenko A. V. Age and Gender Recognition on Imbalanced Dataset of Face Images with Deep Learning // Advances in Intelligent Systems and Computing. 2020. pp. 30-40.
Cite this
RIS
Copy
TY - GENERIC
DO - 10.1007/978-3-030-50097-9_4
UR - https://doi.org/10.1007%2F978-3-030-50097-9_4
TI - Age and Gender Recognition on Imbalanced Dataset of Face Images with Deep Learning
T2 - Advances in Intelligent Systems and Computing
AU - Yudin, D.
AU - Shchendrygin, Maksim
AU - Dolzhenko, Alexandr V
PY - 2020
DA - 2020/06/22 00:00:00
PB - Springer Nature
SP - 30-40
SN - 2194-5357
ER -
Cite this
BibTex
Copy
@incollection{2020_Yudin,
author = {D. Yudin and Maksim Shchendrygin and Alexandr V Dolzhenko},
title = {Age and Gender Recognition on Imbalanced Dataset of Face Images with Deep Learning},
publisher = {Springer Nature},
year = {2020},
pages = {30--40},
month = {jun}
}