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20 June 2024
Reseach Article

Convolutional Neural Networks for Prediction of Age and Gender

by Sonika Malik, Suyash Awasthy, Shivansh Sondhi, Rachit Singh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 29
Year of Publication: 2023
Authors: Sonika Malik, Suyash Awasthy, Shivansh Sondhi, Rachit Singh
10.5120/ijca2023923043

Sonika Malik, Suyash Awasthy, Shivansh Sondhi, Rachit Singh . Convolutional Neural Networks for Prediction of Age and Gender. International Journal of Computer Applications. 185, 29 ( Aug 2023), 40-45. DOI=10.5120/ijca2023923043

@article{ 10.5120/ijca2023923043,
author = { Sonika Malik, Suyash Awasthy, Shivansh Sondhi, Rachit Singh },
title = { Convolutional Neural Networks for Prediction of Age and Gender },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2023 },
volume = { 185 },
number = { 29 },
month = { Aug },
year = { 2023 },
issn = { 0975-8887 },
pages = { 40-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number29/32878-2023923043/ },
doi = { 10.5120/ijca2023923043 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:27:24.083369+05:30
%A Sonika Malik
%A Suyash Awasthy
%A Shivansh Sondhi
%A Rachit Singh
%T Convolutional Neural Networks for Prediction of Age and Gender
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 29
%P 40-45
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Automatic age and gender prediction from face images has recently attracted important attention due to its wide range of operations in multitudinous facial analyses. We show in this study that exercising the Caffe Model Architecture of Deep Learning Framework; we were suitable to greatly enhance age and gender recognition by learning representations using deep convolutional neural networks (CNN). The designed methodology preprocesses the input image before performing point birth using the convolutional neural network (CNN) strategy. This network excerpts dimensional characteristics from the source face image, followed by the point selection strategy. The proposed system is estimated using bracket rate, perfection, and recall using Adience dataset, and real-world images parade excellent performance by achieving good prediction results and calculation time.

References
  1. Age and Gender Classification Using CNN: Gil Levi and Tal Hassner Department of Mathematics and Computer Science the Open University of Israel 2016
  2. A Fusion-based Gender Recognition Method Using Facial Images: Benyamin Ghojogh, Saeed Bagheri Shouraki, Hoda Mohammadzade*, Ensieh Iran Mehr 2017
  3. Age and Gender Prediction from Face Images Using CNN: Koichi Ito, Hiroya Kawai, Takehisa Okano, Takafumi Aoki 2018
  4. Age and Gender Prediction using Deep CNN: Insha Rafique, Awais Hamid, Sheraz Naseer, Muhammad Asad, Muhammad Awais, Talha Yasir 2019
  5. Facial Data Based Deep Learning: Emotion,Age and Gender Prediction: Abhijit Roy 2020
  6. CNN Based Features for Age and Gender Estimation and Gender Classification: Mohammed Kamel Benkaddour University Kasdi Marbah, Department of Computer Science and Information Technology FNTIC Faculty, Ouargla, Algeria 2020
  7. Prediction of Age and Gender Based on Human Face Images Based on Deep Learning Algorithm: S. Haseena, S. Saroja, R. Madavan, Alagar Karthick, Bhaskar Pant, Melkamu Kifetew 2022
  8. Y. Kumar, A. Koul, P. S. Sisodia et al., “Heart failure detection using quantum-enhanced machine learning and traditional machine learning techniques for Internet of artificially intelligent medical things,” Wireless Communications and Mobile Computing 2021.
  9. J. Shafi, M. S. Obaidat, P. V. Krishna, B. Sadoun, M. Pounambal, and J. Gitanjali, “Prediction of heart abnormalities using deep learning model and wearable devices in smart health homes,” Multimedia Tools and Applications 2022.
  10. G. Guo and G. Mu, “A framework for joint estimation of age, gender and ethnicity on a large database,” Image and Vision Computing, vol. 32, no. 10, pp. 761–770 2014.
  11. C. Szegedy, W. Liu, Y. Jia et al., “Going deeper with convolutions,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA 2015.
  12. S. G. Bhele and V. H. Mankar, “A review paper on face recognition techniques,” International Journal of Advanced Research in Computer Engineering & Technology 2012.
  13. S. Chidambaram, S. S. Ganesh, A. Karthick, P. Jayagopal, B. Balachander, and S. Manoharan, “Diagnosing breast cancer based on the adaptive neuro-fuzzy inference system,” Computational and Mathematical Methods in Medicine 2022.
  14. D. A. Asanov, Algorithms and Methods in Recommender Systems, Berlin Institute of Technology, Berlin, Germany 2011.
  15. H. Sikkandar and R. Thiyagarajan, “Soft biometrics-based face image retrieval using improved grey wolf optimization,” IET Image Processing 2020.
Index Terms

Computer Science
Information Sciences

Keywords

CNN Caffe architecture Gender Age Deep learning