Gender and Age Detection using Deep Learning

Authors

  • Utkarsha Kumbhar  Department of Computer Engineering, Vishwakarma Institute of Technology, Pune, Maharashtra, India
  • Prof. A. S. Shingare  Department of Computer Engineering, Vishwakarma Institute of Technology, Pune, Maharashtra, India

DOI:

https://doi.org//10.32628/CSEIT2173128

Keywords:

CNN, Adience dataset, Feature extraction, neural network, deep learning

Abstract

For the past few years, gender and age detection has been an active area of study and researchers have been putting a lot of effort to contribute quality research in this area. Starting from preprocessing of data to building a model which gives high precision results is tedious task for researchers. There is a immense dormant field of study as it can be used in monitoring, surveillance, human-computer interaction and security. However, there is still a lack of the performance of existing methods on real live images. Many difficult tasks such as computer vision, speech recognition, and natural language processing are easily solved with deep learning. Therefore, the approach of deep learning remarkably growing and this also takes place in image classification. Therefore, to analyses and focuses on comparative study of different algorithms for gender and age recognition system to give elevated degree of precision is required.

References

  1. Arora, Shefali, and M. P. S. Bhatia, “A Robust Approach for Gender Recognition Using Deep Learning,” In 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1-6, 2018.
  2. Levi, Gil, and Tal Hassner, “Age and gender classification using convolutional neural networks.” In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp. 34-42, 2015.
  3. Wang, Xiaofeng, Azliza Mohd Ali, and Plamen Angelov, “Gender and age classification of human faces for automatic detection of anomalous human behaviour.” In 2017 3rd IEEE International Conference on Cybernetics (CYBCONF), pp. 1-6, 2017.
  4. Haseena, S., S. Bharathi, I. Padmapriya, and R. Lekhaa, “Deep Learning Based Approach for Gender Classification.” In 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), pp. 1396-1399, 2018.
  5. Ramdhani, B., Djamal, E.C. and Ilyas, R., “Convolutional Neural Networks Models for Facial Expression Recognition. In 2018 International Symposium on Advanced Intelligent Informatics (SAIN), pp. 96-101, August 2018.
  6. Tiagrajah V. Janahiraman1 and Prasantth Subramaniam (2019) Gender Classification Based on Asian Faces using Deep Learning. 2019 IEEE 9th International Conference on System Engineering and Technology (ICSET), 7 October 2019, Shah Alam, Malaysia
  7. Ericsdotter C and Ericsson A M 2001 Gender differences in vowel duration in read Swedish: Preliminary results Working Papers - Lund University Department of Linguistics 34 – 37.
  8. L. Best-Rowden, H. Han, C. Otto, B. F. Klare, A. K. Jain, Unconstrained face recognition: Identifying a person of interest from a media collection, Information Forensics and Security, IEEE Transactions on 9 (2014) 2144–2157.
  9. J. Orozco, O. Rudovic, J. Gonz`alez, M. Pantic, Hierarchical on-line appearance-based tracking for 3d head pose, eyebrows, lips, eyelids and irises, Image and Vision Computing 32 (2014) 14–26
  10. Arora, Shefali, and M. P. S. Bhatia, “A Robust Approach for Gender Recognition Using Deep Learning,” In 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1-6, 2018.
  11. Levi, Gil, and Tal Hassner, “Age and gender classification using convolutional neural networks.” In Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp. 34-42, 2015.
  12. Wang, Xiaofeng, Azliza Mohd Ali, and Plamen Angelov, “Gender and age classification of human faces for automatic detection of anomalous human behaviour.” In 2017 3rd IEEE International Conference on Cybernetics (CYBCONF), pp. 1-6, 2017.
  13. Haseena, S., S. Bharathi, I. Padmapriya, and R. Lekhaa, “Deep Learning Based Approach for Gender Classification.” In 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), pp. 1396-1399, 2018.
  14. Ramdhani, B., Djamal, E.C. and Ilyas, R., “Convolutional Neural Networks Models for Facial Expression Recognition. In 2018 International Symposium on Advanced Intelligent Informatics (SAIN), pp. 96-101, August 2018.
  15. E. Edinger, R. Enbar, and T. Hassner. Age and gender estimation of unfiltered faces. Trans. on Inform. Forensics and Security, 9(12), 2014
  16. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In Neural Inform. Process. Syst., pages 1097–1105, 2012.
  17. K. Chatfield, K. Simonyan, A. Vedaldi, and A. Zisserman. Return of the devil in the details: Delving deep into convolutional nets. arXiv preprint arXiv:1405.3531, 2014

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2021-06-30

Issue

Volume 7, Issue 3, May-June-2021

Section

Research Articles

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Copyright (c) IJSRCSEIT

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Utkarsha Kumbhar, Prof. A. S. Shingare, " Gender and Age Detection using Deep Learning, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 3, pp.604-610, May-June-2021. Available at doi : https://doi.org/10.32628/CSEIT2173128