Skip to main content
SpringerLink
Log in

Handcrafted Features for Human Gait Recognition: CASIA-A Dataset

  • Conference paper
  • First Online:
Artificial Intelligence and Data Science (ICAIDS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1673))

Included in the following conference series:

Abstract

Human Gait Recognition has become a burning research area because of its promising application in security enhancement. There are numerous state-of-the-art feature detectors and classifiers available for gait recognition. In this article, three popular feature descriptor algorithms that are Scale Invariant Feature Transform (SIFT), Speeded Up Robust Feature (SURF) and Shi-Tomasi edge corner detector are used for extracting the unique features of the gait images. At first, the experiment is done by using a single feature descriptor then a combination of these three is applied. Various classifiers like Decision Tree, Random Forest, MLP are used to make the class membership based on features. Maximum accuracy of 76.12%, by applying the Decision Tree classifier, 80.11% by Random Forest, and 74.25% by applying MLP has been achieved for the CASIA-A dataset. In this article authors have computed recognition rate, false-positive rate (FPR) and root mean squared error (RMSE) in all cases to compare the performance of features and classifiers considered in this article. The experimental results depict that a combination of these three feature descriptors is performing better than other existing state-of-the art-work.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Jun, K., LeeD, W., Lee, K., Lee, S., Kim, M.S.: Feature extraction using an RNN autoencoder for skeleton-based abnormal gait recognition. IEEE Access 8, 19196–19207 (2020). https://doi.org/10.1109/ACCESS.2020.2967845

    Article  Google Scholar 

  2. K, S., Wahid, F.F., G, R.: Statistical features from frame aggregation and differences for human gait recognition. Multimedia Tools and Applications 80(12), 18345–18364 (2021). https://doi.org/10.1007/s11042-021-10655-z

    Article  Google Scholar 

  3. Singh, J.P., Jain, S., Arora, S., Singh, U.P.: Vision-Based Gait Recognition: A Survey. IEEE Access 6, 70497–70527 (2018). https://doi.org/10.1109/access.2018.2879896

    Article  Google Scholar 

  4. Yazdi, H.S., Fariman, H.J., Roohi, J.: Gait recognition based on invariant leg classification using a neuro-fuzzy algorithm as the fusion method. ISRN Artificial Intelligence, 289721 (2012). https://doi.org/10.5402/2012/289721

  5. Tafazzoli, F., Bebis, G., Louis, S., Hussain, M.: Improving Human Gait Recognition Using Feature Selection. In: Bebis, G., et al. (eds.) ISVC 2014. LNCS, vol. 8888, pp. 830–840. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-14364-4_80

    Chapter  Google Scholar 

  6. Chaitanya, G. K., Sekhar, K. R. A human gait recognition against information theft in smartphone using residual convolutional neural network. Int. J. Adv. Comput. Sci. Appl. 11(5) (2020). https://doi.org/10.14569/ijacsa.2020.0110544

  7. Khan, M.A., et al.: Human gait analysis for osteoarthritis prediction: a framework of deep learning and kernel extreme *RVol**learning machine. Complex & Intelligent Systems. 1–19 (2021). https://doi.org/10.1007/s40747-020-00244-2

  8. Cheheb, I., Al-Maadeed, N., Al-Madeed S., Bouridane, A.: Investigating the use of autoencoders for gait-based person recognition. In: 2018 NASA/ESA Conference on Adaptive Hardware and Systems, AHS 2018, pp. 148–151 (2018). https://doi.org/10.1109/AHS.2018.8541447

  9. Mohammed, F. G., Eesee, W. K.: Human gait recognition using neural network multi-layer perceptron. J. Mech. Cont. Math. Sci. 14(3), pp. 234–244 (2019). https://doi.org/10.26782/jmcms.2019.06.00018

  10. Guo, H., et al.: Gait recognition based on the feature extraction of gabor filter and linear discriminant analysis and improved local coupled extreme learning machine. Math. Probl. Eng. 2020, 1–9 (2020). https://doi.org/10.1155/2020/5393058

    Article  Google Scholar 

  11. Nardo, D.F., Morbidoni, C., Cucchiarelli, A., Fioretti, S.: Recognition of gait phases with a single knee electrogoniometer: A deep learning approach. Electronics 9(2), 355 (2020). https://doi.org/10.3390/electronics9020355

    Article  Google Scholar 

  12. Si, W., Zhang, J., Li, Y.D., Tan, W., Shao, Y.F., Yang, G.L.: Remote identity verification using gait analysis and face recognition. Wirel. Commun. Mob. Comput. 2020, 1 (2020). https://doi.org/10.1155/2020/8815461

    Article  Google Scholar 

  13. Khan, M.H., Li, F., Farid, M.S., Grzegorzek, M.: Gait recognition using motion trajectory analysis. In: Kurzynski, M., Wozniak, M., Burduk, R. (eds.) CORES 2017. AISC, vol. 578, pp. 73–82. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-59162-9_8

    Chapter  Google Scholar 

  14. Hu, M., Wang, Y., Zhang, Z., Zhang, D., Little, J.J.: Incremental learning for video-based gait recognition with LBP flow. IEEE Trans. Cybernet. 43(1), 77–89 (2013). https://doi.org/10.1109/TSMCB.2012.2199310

    Article  Google Scholar 

  15. Bansal, M., Kumar, M., Kumar, M.: 2D object recognition: a comparative analysis of SIFT, SURF and ORB feature descriptors. Multim. Tools Appl. 80(12), 18839–18857 (2021). https://doi.org/10.1007/s11042-021-10646-0

    Article  Google Scholar 

  16. Chhabra, P., Garg, N.K., Kumar, M.: Content-based image retrieval system using ORB and SIFT features. Neural Comput. Appl. 32(7), 2725–2733 (2018). https://doi.org/10.1007/s00521-018-3677-9

    Article  Google Scholar 

  17. Huang, M., Mu, Z., Zeng, H., Huang, H.: A Novel approach for interest point detection via laplacian-of-bilateral filter. J. Sens. 2015, 1–9 (2015). https://doi.org/10.1155/2015/685154

    Article  Google Scholar 

  18. Saini, A., Singh, H.: Enhanced human identity and gender recognition from gait sequences using SVM and MDA. Int. J. Comput. Appl. 119(2), 6–9 (2015). https://doi.org/10.5120/21037-3358

    Article  Google Scholar 

  19. Htun KZ., & Zaw SMM. (2018). Human identification system based on statistical gait features. In: 2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS). doi:https://doi.org/10.1109/icis.2018.8466396

  20. Kadhim, H. A., Araheemah, W. A.: A method to improve corner detectors (Harris, Shi-Tomasi & FAST) using adaptive contrast enhancement filter. Period. Eng. Nat. Sci. 8(1), 508–515 (2020). https://doi.org/10.21533/pen.v8i1.1158.g532

  21. Pribyl, B., Chalmers, A., Zemck, P.: Feature point detection under extreme lighting conditions. In: Proceedings—SCCG 2012: 28th Spring Conference on Computer Graphics, pp. 143–150 (2012). https://doi.org/10.1145/2448531.2448550

  22. Kumar, M., Chhabra, P., Garg, N.K.: An efficient content based image retrieval system using BayesNet and K-NN. Multimedia Tools and Applications 77(16), 21557–21570 (2018). https://doi.org/10.1007/s11042-017-5587-8

    Article  Google Scholar 

  23. Long, T.: Locality preserving projection via deep neural network. In: 2019 International Joint Conference on Neural Networks (IJCNN), July, pp. 1–8 (2019).

    Google Scholar 

  24. Strukova, O. V., Myasnikov, E. V. The choice of methods for the construction of PCA-based features and the selection of SVM parameters for person identification by gait. J. Phys.: Conf. Ser. 1368(3) (2019). https://doi.org/10.1088/1742-6596/1368/3/032001

  25. Tang, Y., Rose, R.: A study of using locality preserving projections for feature extraction in speech recognition. In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing—Proceedings, May, pp. 1569–1572 (2008). https://doi.org/10.1109/ICASSP.2008.4517923

  26. Naik, S., Virani, H.G.: Human gait recognition using BPN and MLP. Int. J. Innov. Res. Sci. Technol. 1(11), 440–446 (2015)

    Google Scholar 

  27. Jalmi, S. K.: MLP based gait recognition technique. Int. J. Eng. Res. Technol. 10, 8 (2021)

    Google Scholar 

  28. Kececi, A., Yildirak, A., Ozyazici, K., Ayluctarhan, G., Agbulut, O., Zincir, I.: Implementation of machine learning algorithms for gait recognition. Engineering Science and Technology, an International Journal 23(4), 931–937 (2020). https://doi.org/10.1016/j.jestch.2020.01.005

    Article  Google Scholar 

  29. Gupta, A., Jadhav, A., Jadhav, S., Thengade, A.: Human gait analysis based on decision tree, random forest and KNN algorithms. In: Iyer, B., Rajurkar, A.M., Gudivada, V. (eds.) Applied Computer Vision and Image Processing. AISC, vol. 1155, pp. 283–289. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-4029-5_28

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computational Sciences, Maharaja Ranjit Singh Punjab Technical University, Bathinda, Punjab, India

    Veenu Rani & Munish Kumar

  2. IT Enabled Services, Maharaja Ranjit Singh Punjab Technical University, Bathinda, Punjab, India

    Bhupinder Singh

Authors
  1. Veenu Rani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Munish Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bhupinder Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Munish Kumar .

Editor information

Editors and Affiliations

  1. Sreyas Institute of Engineering and Technology, Hyderabad, India

    Ashwani Kumar

  2. University of Maribor, Maribor, Slovenia

    Iztok Fister Jr.

  3. Jaypee University of Information Technology, Solan, India

    P. K. Gupta

  4. LGF CNRS UMR 5307, MINES, Saint-Etienne, France

    Johan Debayle

  5. University of North Florida, Jacksonville, FL, USA

    Zuopeng Justin Zhang

  6. King Khalid University, Abha, Saudi Arabia

    Mohammed Usman

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Rani, V., Kumar, M., Singh, B. (2022). Handcrafted Features for Human Gait Recognition: CASIA-A Dataset. In: Kumar, A., Fister Jr., I., Gupta, P.K., Debayle, J., Zhang, Z.J., Usman, M. (eds) Artificial Intelligence and Data Science. ICAIDS 2021. Communications in Computer and Information Science, vol 1673. Springer, Cham. https://doi.org/10.1007/978-3-031-21385-4_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-21385-4_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21384-7

  • Online ISBN: 978-3-031-21385-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation