Age and Gender Identification Using Neural Networks

Authors

  • Keerthi Gayatri Department of Computer Science and Engineering, PACE Institute of Technology & Sciences, Vallur, Ongole, Andhra Pradesh, India Author
  • MuttumVenkata Yamini Department of Computer Science and Engineering, PACE Institute of Technology & Sciences, Vallur, Ongole, Andhra Pradesh, India Author
  • Ukkadapu Thanmayee Department of Computer Science and Engineering, PACE Institute of Technology & Sciences, Vallur, Ongole, Andhra Pradesh, India Author
  • Medikonda Bhagya Jyothi Department of Computer Science and Engineering, PACE Institute of Technology & Sciences, Vallur, Ongole, Andhra Pradesh, India Author
  • M Srinivasa Rao Department of Computer Science and Engineering, PACE Institute of Technology & Sciences, Vallur, Ongole, Andhra Pradesh, India Author
  • D Janardhan Reddy Department of Computer Science and Engineering, PACE Institute of Technology & Sciences, Vallur, Ongole, Andhra Pradesh, India Author

Keywords:

Age, Gender Identification, Neural Networks

Abstract

Face recognition is still very challenging  and complex problem. This problem can be credited as  large intra-personal variations and large inter personal  similarity. Facial recognition is the application of  biometric breakthroughs that can observe or verify a  person by observing and examining designs based on the  individual's shape. Despite the increased interest in other  applications, face recognition is still mostly utilized for  well-being. Generally, advancements in face recognition  are worthwhile since they may have a wide range of legal  applications and commercial uses.  

Downloads

Download data is not yet available.

References

E. Agustsson, R. Timofte, S. Escalera, X. Baro, I. Guyon, and R. Rothe, “Apparent and real age estimation in still images with deep residual regressors on appa-real database,” in Proceedings of the 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), Biometrics Wild, Bwild, Washington, DC, USA, pp. 87–94, June 2017.

K. Zhang, C. Gao, L. Guo et al., “Age group and gender estimation in the wild with deep RoR architecture,” IEEE Access, vol. 5, pp. 22492–22503, 2017.

A. Kuehlkamp, “Age estimation from face images,” in Proceedings of the 6th IAPR International Conference on Biometrics (ICB), pp. 1–10, Madrid, Spain, June 2013.

V. Carletti, A. S. Greco, G. Percannella, M. Vento, and I. Fellow, “Age from faces in the deep learning revolution,” IEEE Transactions on Pattern Analysis and Machine Intelligence, p. 1, 2019.

B. Bin Gao, H. Y. Zhou, J. Wu, and X. Geng, “Age estimation using expectation of label distribution learning,” in Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, pp. 712–718, Stockholm, Sweden, July 2018.

R. C. Malli, M. Aygun, and H. K. Ekenel, “Apparent age estimation using ensemble of deep learning models,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 714–721, Las Vegas, NV, USA, June 2016.

G. Antipov, M. Baccouche, S. A. Berrani, and J. L. Dugelay, “Apparent age estimation from face images combining general and children-specialized deep learning models,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 801–809, Las Vegas, NV, USA, June 2016.

G. Antipov, M. Baccouche, S. A. Berrani, and J. L. Dugelay, “Effective training of convolutional neural networks for facebased gender and age prediction,” Pattern Recognition, vol. 72, pp. 15–26, 2017.

PrasaduPeddi (2019), Data Pull out and facts unearthing in biological Databases, International Journal of Techno Engineering, Vol. 11, issue 1, pp: 25-32

R. Rothe, R. Timofte, and L. Van Gool, “Deep expectation of real and apparent age from a single image without facial landmarks,” International Journal of Computer Vision, vol. 126, no. 2–4, pp. 144–157, 2018.

H. Han and A. K. Jain, “Age, gender and race estimation from unconstrained face images,” MSU Technical Report, MSUCSE-14-5, Michigan State University, East Lansing, MI, USA, 2014.

Downloads

Published

2022-04-30

How to Cite

Age and Gender Identification Using Neural Networks . (2022). International Journal of Innovative Research in Engineering & Management, 9(2), 644–647. Retrieved from https://acspublisher.com/journals/index.php/ijirem/article/view/11207