Skip to main content
SpringerLink
Log in

Tree structure convolutional neural networks for gait-based gender and age classification

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Gender classification and age estimation are tasks in which humans excel. If gender and age of human can be recognized automatically from images, it will be very helpful in many applications such as intelligent surveillance, micromarketing, etc. We propose a framework for gender and age classification through gait analysis. Gait-based recognition is a feasible approach as the gait of human subject can still be perceived at a long distance. The spatio-temporal gait features are concisely represented as Gait Energy Image (GEI), which is then input to a tree structure convolutional neural network (CNN). We train and test the first model on a single-view gait dataset. Based on the tree structure CNN framework, we propose a larger model for gender and age classification with the multi-view gait dataset. Our models can achieve gender classification accuracy of 97.42% and 99.11% on single-view gait and multi-view gait respectively. We then use our model to perform age group estimation and binary (young and elder groups) classification. Also, our models can achieve the best performance in specific age estimation in terms of various numerical measures than various recently proposed methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability

Not applicable.

Code availability

Not applicable.

References

  1. CASIA Gait Database. http://www.sinobiometrics.com

  2. Chao H, He Y, Zhang J, Feng J (2019) GaitSet: regarding gait as a set for cross-view gait recognition. Proceedings of the AAAI Conference on Artificial Intelligence 33(1):8126–8133

    Article  Google Scholar 

  3. Dey EM, Khan M, Ali MH (2013) Computer vision-based gender detection from facial image. International Journal of Advanced Computer Science 3(8):428–433

    Google Scholar 

  4. Han J, Bhanu B (2006) Individual recognition using gait energy image. IEEE Trans Pattern Anal Mach Intell 28(2):316–322

    Article  Google Scholar 

  5. Harb H, Chen L (2003) Gender identification using a general audio classifier. Proceedings of International Conference on Multimedia and Expo, 733-736

  6. Hema M, Pitta S (2019) Human age classification based on gait parameters using a gait energy image projection model. Proceedings of the Third International Conference on Trends in Electronics and Informatics, 1163-1168

  7. Hu M, Wang Y (2009) A new approach for gender classification based on gait analysis. Proceedings of International Conference on Image and Graphics, 869-874

  8. Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. Proceedings of the 25th International Conference on Neural Information Processing Systems 1, 1097–1105

  9. Levi G, Hassncer T (2015) Age and gender classification using convolutional neural networks. Proceedings of IEEE on Computer Vision and Pattern Recognition Workshops, 34-42

  10. Li X, Makihara Y, Xu C, Yagi Y, Ren M (2018) Gait-based human age estimation using age group-dependent manifold learning and regression. Multimed Tools Appl 77(21):28333–28354

    Article  Google Scholar 

  11. Mansouri N, Issa MA, Jemaa YB (2018) Gait features fusion for efficient automatic age classification. IET Comput Vis 12(1):69–75

    Article  Google Scholar 

  12. Nabila M, Mohammed AI, Yousra BJ (2017) Gait-based human age classification using a silhouette model. IET Biometrics 7(2):116–124

    Article  Google Scholar 

  13. Russel NS, Selvaraj A (2021) Gender discrimination, age group classification and carried object recognition from gait energy image using fusion of parallel convolutional neural network. IET Image Process 15:239–251

    Article  Google Scholar 

  14. Sakata A, Takemura N, Yagi Y (2019) Gait-based age estimation using multi-stage convolutional neural network. IPSJ Transactions on Computer Vision and Applications 11(4):1–10

    Google Scholar 

  15. Sakata A, Makihara Y, Takemura N, Muramatsu D, Yagi Y (2020) How confident are you in your estimate of a human age? Uncertainty-aware gait-based age estimation by label distribution learning. Proceedings of IEEE International Joint Conference on Biometrics, 1-10

  16. Samangooei S, Bustard JD, Seely RD, Nixon MS, Carter JN (2011) Acquisition and analysis of a dataset comprising gait, ear, and semantic data. Multibiometrics for Human Identification, Cambridge University Press, Chapter 12

  17. Shiraga K, Makihara Y, Muramatsu D, Echigo T, Yagi Y (2016) Geinet: view-invariant gait recognition using a convolutional neural network. Proceedings of International Conference on Biometrics, 1-8

  18. Takemura N, Makihara Y, Muramatsu D, Echigo T, Yagi Y (2018) Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition. IPSJ Transactions on Computer Vision and Applications 10(4):1–14

    Google Scholar 

  19. The OU-ISIR Gait Database Large Population Dataset with Age. http://www.am.sanken.osaka-u.ac.jp/BiometricDB/GaitLPAge.html

  20. The OU-ISIR Gait Database Multi-View Large Population Dataset. http://www.am.sanken.osaka-u.ac.jp/BiometricDB/GaitMVLP.html

  21. Tian Q, Chen S (2018) Joint gender classification and age estimation by nearly orthogonalizing their semantic spaces. Image Vis Comput 69:9–21

    Article  Google Scholar 

  22. Xu C, Makihara Y, Ogi G, Li X, Yagi Y, The JL (2017) OU-ISIR gait database comprising the large population dataset with age and performance evaluation of age estimation. IPSJ Transactions on Computer Vision and Applications 9(24):1–14

    Google Scholar 

  23. Xu C, Makihara Y, Yagi Y, Lu J (2019) Gait-based age progression/regression: a baseline and performance evaluation by age group classification and cross-age gait identification. Mach Vis Appl 30:629–644

    Article  Google Scholar 

  24. Xu C, Makihara Y, Li X, Yagi Y, Lu J (2020) Gait recognition from a single image using a phase-aware gait cycle reconstruction network. Proceedings of European Conference on Computer Vision, 386-403

  25. Xu C, Makihara Y, Liao R, Niitsuma H, Li X, Yagi Y, Lu J (2021) Real-time gait-based age estimation and gender classification from a single image. Proceedings of the IEEE/CVF Conference on Applications of Computer Vision, 3460-3470

  26. Yam CY, Nixon MS, Carter JN (2002) Gait recognition by walking and running: a model-based approach. Proceedings of Asian Conference on Computer Vision, 1–6

  27. Yu S, Tan T, Huang K, Jia K, Wu X (2009) A study on gait-based gender classification. IEEE Trans Image Process 18(8):1905–1910

    Article  MathSciNet  MATH  Google Scholar 

  28. Zaghbani S, Boujneh N, Bouhlel MS (2018) Age estimation using deep learning. Comput Electr Eng 68:337–347

    Article  Google Scholar 

  29. Zhang D, Wang Y (2008) Gender recognition based on fusion of face and gait information. Proceeding of International Conference on Machine Learning and Cybernetics, 62-67

  30. Zhang D, Wang Y, Bhanu B (2010) Age classification based on gait using HMM. Proceedings of International Conference on Pattern Recognition, 3834–3837

  31. Zhang C, Liu W, Ma H, Fu H (2016) Siamese neural network based gait recognition for human identification. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, 2832-2836

Download references

Acknowledgements

This work is supported by Hong Kong Innovation and Technology Commission (InnoHK Project CIMDA). The authors would like to thank the reviewers for their comments and suggestions. We gratefully acknowledge Mr. T. M. Leung for his participation in revising the manuscript.

Funding

The work described in this paper was supported by Hong Kong Innovation and Technology Commission (InnoHK Project CIMDA).

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Hong Kong, Hong Kong

    L. K. Lau & Kwok Chan

Authors
  1. L. K. Lau
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kwok Chan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

L K Lau: Investigation, Methodology, Software, Validation, Writing- Original draft preparation.

K L Chan: Conceptualization, Methodology, Supervision, Writing- Original draft preparation, Writing- Reviewing and Editing.

Corresponding author

Correspondence to Kwok Chan.

Ethics declarations

Ethics approval

Not applicable.

Consent to participate

Mr. L K Lau and I have participated in the research and writing of this paper.

Consent for publication

Mr. L K Lau and I have jointly written this paper and we would like it to be considered for publication.

Conflicts of interest/competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lau, L.K., Chan, K. Tree structure convolutional neural networks for gait-based gender and age classification. Multimed Tools Appl 82, 2145–2164 (2023). https://doi.org/10.1007/s11042-022-13186-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-13186-3

Keywords

Search

Navigation