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Feature integration for frontal gait recognition through contour image analysis

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Abstract

Biometric technology is advancing with gait recognition, which analyzes walking patterns to identify people. This pattern is derived without the direct participation of individuals, from a distance. Frontal gait data is highly valuable in confined spaces like narrow corridors, which is common in most buildings. Within this scope, this study introduces a successful approach to identify individuals in frontal-view gait sequences. By utilizing contour image and vertices, the proposed method obtains three differentiating feature vectors from the Gait Energy Image (GEI). Its efficient capture of spatial dynamics leads to improved gait recognition performance. The proposed approach’s effectiveness was evaluated using the widely used gait datasets such as CMU MoBo, CASIA A, and CASIA B. Through the experiments, it was proven that the proposed approach delivers promising outcomes and performs better than certain state-of-the-art approaches in recognition.

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Data Availability

No datasets were generated or analysed during the current study.

References

  1. Semwal, V.B., Raj, M., Nandi, G.C.: Biometric gait identification based on a multilayer perceptron. Robot. Auton. Syst. 65, 65–75 (2015)

    Article  MATH  Google Scholar 

  2. Anusha, R., Jaidhar, C.: Human gait recognition based on histogram of oriented gradients and haralick texture descriptor. Multimed. Tools Appl. 79(11), 8213–8234 (2020)

    Article  MATH  Google Scholar 

  3. Muramatsu, D., Makihara, Y., Yagi, Y.: View transformation model incorporating quality measures for cross-view gait recognition. IEEE Trans. Cybernet. 46(7), 1602–1615 (2016)

    Article  MATH  Google Scholar 

  4. Soriano, M., Araullo, A., Saloma, C.: Curve spreads-a biometric from front-view gait video. Pattern Recognit. Lett. 25(14), 1595–1602 (2004)

    Article  Google Scholar 

  5. Sivapalan, S., Chen, D., Denman, S., Sridharan, S., Fookes, C.: Gait energy volumes and frontal gait recognition using depth images. In: 2011 International Joint Conference on Biometrics (IJCB), IEEE, pp. 1–6 (2011)

  6. Chattopadhyay, P., Sural, S., Mukherjee, J.: Frontal gait recognition from occluded scenes. Pattern Recognit. Lett. 63, 9–15 (2015)

    Article  MATH  Google Scholar 

  7. Chattopadhyay, P., Roy, A., Sural, S., Mukhopadhyay, J.: Pose depth volume extraction from rgb-d streams for frontal gait recognition. J. Vis. Commun. Image Represent. 25(1), 53–63 (2014)

    Article  MATH  Google Scholar 

  8. Chattopadhyay, P., Sural, S., Mukherjee, J.: Frontal gait recognition from incomplete sequences using rgb-d camera. IEEE Trans. Inf. Forensics Secur. 9(11), 1843–1856 (2014)

    Article  MATH  Google Scholar 

  9. Ryu, J., Kamata, Si.: Front view gait recognition using spherical space model with human point clouds. In: 2011 18th IEEE International Conference on Image Processing, IEEE, pp. 3209–3212 (2011)

  10. Maity, S., Abdel-Mottaleb, M., Asfour, S.S.: Multimodal low resolution face and frontal gait recognition from surveillance video. Electronics 10(9), 1013 (2021)

    Article  MATH  Google Scholar 

  11. Deng, M., Fan, Z., Lin, P., Feng, X.: Human gait recognition based on frontal-view sequences using gait dynamics and deep learning. IEEE Trans. Multimed. 26, 117–126 (2023)

    Article  MATH  Google Scholar 

  12. Zhang, Z., Tran, L., Liu, F., Liu, X.: On learning disentangled representations for gait recognition. IEEE Trans. Pattern Anal. Mach. Intell. 44(1), 345–360 (2020)

    Article  MATH  Google Scholar 

  13. Deng, M., Zhong, Z., Zou, Y., Wang, Y., Wang, K., Liao, J.: Human gait recognition based on frontal-view walking sequences using multi-modal feature representations and learning. Neural Process. Lett. 56(2), 133 (2024)

    Article  MATH  Google Scholar 

  14. Sheshadri, M.G.H., Okade, M.: Kinect based frontal gait recognition using skeleton and depth derived features. In: 2020 National Conference on Communications (NCC), IEEE, pp. 1–5 (2020)

  15. Deng, M., Zou, Y., Zhu, W., Xing, M., Huang, Y., Yang, J.: Frontal-view gait recognition using discriminative dynamics feature representations and learning. J. Electr. Imaging 33(1), 013,025-013,025 (2024)

    Article  Google Scholar 

  16. Isaac, E.R., Elias, S., Rajagopalan, S., Easwarakumar, K.: Trait of gait: A survey on gait biometrics. arXiv preprint arXiv:1903.10744 (2019)

  17. Parashar, A., Shekhawat, R.S., Ding, W., Rida, I.: Intra-class variations with deep learning-based gait analysis: a comprehensive survey of covariates and methods. Neurocomputing 505, 315–338 (2022)

    Article  MATH  Google Scholar 

  18. Xu, D., Zhou, H., Quan, W., Jiang, X., Liang, M., Li, S., Ugbolue, U.C., Baker, J.S., Gusztav, F., Ma, X., et al.: A new method proposed for realizing human gait pattern recognition: inspirations for the application of sports and clinical gait analysis. Gait Posture 107, 293–305 (2024)

    Article  Google Scholar 

  19. Anusha, R., Jaidhar, C.: Speed-invariant gait recognition using correlation factor lists for classroom attendance systems. In: International Conference on Machine Learning, Image Processing, Network Security and Data Sciences, Springer, pp. 281–290 (2023)

  20. Rani, V., Kumar, M.: Human gait recognition: a systematic review. Multimed. Tools Appl. 82(24), 37,003-37,037 (2023)

    Article  MATH  Google Scholar 

  21. Maple, C.: Geometric design and space planning using the marching squares and marching cube algorithms. In: Proceedings. 2003 International Conference on Geometric Modeling and Graphics, IEEE, pp. 90–95 (2003)

  22. Sayeed, F., Hanmandlu, M.: Properties of information sets and information processing with an application to face recognition. Knowl. Inf. Syst. 52(2), 485–507 (2017)

    Article  MATH  Google Scholar 

  23. Huang, C.C., Hsu, C.C., Liao, H.Y., Yang, S.H., Wang, L.L., Chen, S.Y.: Frontal gait recognition based on spatio-temporal interest points. J. Chin. Inst. Eng. 39(8), 997–1002 (2016)

    Article  MATH  Google Scholar 

  24. Zheng, S.: (Accessed 27 Jul 2017) CASIA Gait Database. URL http://www.sinobiometrics.com

  25. Alotaibi, M., Mahmood, A.: Improved gait recognition based on specialized deep convolutional neural network. Comput. Vis. Image Underst. 164, 103–110 (2017)

    Article  MATH  Google Scholar 

  26. Choudhury, S.D., Tjahjadi, T.: Robust view-invariant multiscale gait recognition. Pattern Recognit. 48(3), 798–811 (2015)

    Article  MATH  Google Scholar 

  27. Isaac, E.R., Elias, S., Rajagopalan, S., Easwarakumar, K.: View-invariant gait recognition through genetic template segmentation. IEEE Signal Process. Lett. 24(8), 1188–1192 (2017)

    Article  Google Scholar 

  28. Rida, I., Jiang, X., Marcialis, G.L.: Human body part selection by group lasso of motion for model-free gait recognition. IEEE Signal Process. Lett. 23(1), 154–158 (2016)

    Article  MATH  Google Scholar 

  29. Anusha, R., Jaidhar, C.D.: Frontal gait recognition based on hierarchical centroid shape descriptor and similarity measurement. In: 2019 International Conference on Data Science and Engineering (ICDSE), IEEE, pp 71–76 (2019)

  30. Ye, B., Wen, Y.: A new gait recognition method based on body contour. In: 2006 9th International Conference on Control, Automation, Robotics and Vision, IEEE, pp 1–6 (2006)

  31. Wang, L., Ning, H., Hu, W., Tan, T.: Gait recognition based on procrustes shape analysis. In: Proceedings. International Conference on Image Processing, IEEE, vol 3, pp III–III (2002)

  32. Liu, L., Yin, Y., Qin, W., Li, Y.: Gait recognition based on outermost contour. Int. J. Comput. Intell. Syst. 4(5), 1090–1099 (2011)

    MATH  Google Scholar 

  33. Lee, C.P., Tan, A.W., Tan, S.C.: Gait recognition via optimally interpolated deformable contours. Pattern Recognit. Lett. 34(6), 663–669 (2013)

    Article  MATH  Google Scholar 

  34. Gross, R., Shi, J.: The cmu motion of body (mobo) database. Tech. Rep. CMU-RI-TR-01-18, Carnegie Mellon University, Pittsburgh, PA (2001)

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Acknowledgements

The authors acknowledge the invaluable contributions of the CASIA [24] and CMU MoBo [34] teams in making the gait datasets available.

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A.R - Conducted experiment and wrote the main manuscript. S.CK - Prepared figures and reviewed the manuscript.

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Correspondence to R. Anusha.

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Anusha, R., Sunil, C.K. Feature integration for frontal gait recognition through contour image analysis. SIViP 19, 26 (2025). https://doi.org/10.1007/s11760-024-03655-7

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  • DOI: https://doi.org/10.1007/s11760-024-03655-7

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