Gait Recognition Based on Normalized Walk Cycles

  • Jan Sedmidubsky
  • Jakub Valcik
  • Michal Balazia
  • Pavel Zezula
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7432)


We focus on recognizing persons according to the way they walk. Our approach considers a human movement as a set of trajectories formed by specific anatomical landmarks, such as hips, feet, shoulders, or hands. The trajectories are used for the extraction of distance-time dependency signals that express how a distance between a pair of specific landmarks on the human body changes in time as the person walks. The collection of such signals characterizes a gait pattern of person’s walk. To determine the similarity of gait patterns, we propose several functions that compare various combinations of extracted signals. The gait patterns are compared on the level of individual walk cycles in order to increase the recognition effectiveness. The results evaluated on a 3D database of walking humans achieved the recognition rate up to 96 %.


Recognition Rate Video Frame Anatomical Landmark Gait Pattern Dynamic Time Warping 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    BenAbdelkader, C., Cutler, R., Davis, L.: Stride and cadence as a biometric in automatic person identification and verification. In: 5th International Conference on Automatic Face Gesture Recognition, pp. 372–377. IEEE (2002)Google Scholar
  2. 2.
    Berndt, D.J., Clifford, J.: Finding patterns in time series: a dynamic programming approach. In: Advances in Knowledge Discovery and Data Mining, pp. 229–248. American Association for Artificial Intelligence, Menlo Park (1996)Google Scholar
  3. 3.
    Bhanu, B., Han, J.: Human Recognition at a Distance in Video. In: Advances in Computer Vision and Pattern Recognition. Springer (2010)Google Scholar
  4. 4.
    Chen, C., Liang, J., Zhao, H., Hu, H., Tian, J.: Frame difference energy image for gait recognition with incomplete silhouettes. Pattern Recognition 30(11), 977–984 (2009)CrossRefGoogle Scholar
  5. 5.
    Cunado, D.: Automatic extraction and description of human gait models for recognition purposes. Computer Vision and Image Understanding 90(1), 1–41 (2003)CrossRefGoogle Scholar
  6. 6.
    Han, J., Bhanu, B.: Individual recognition using gait energy image. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(2), 316–322 (2006)CrossRefGoogle Scholar
  7. 7.
    Tanawongsuwan, R., Bobick, A.F.: Gait recognition from time-normalized joint-angle trajectories in the walking plane. In: International Conference on Computer Vision and Pattern Recognition (CVPR 2001), vol. 2(C), II–726–II–731 (2001)Google Scholar
  8. 8.
    Valcik, J., Sedmidubsky, J., Balazia, M., Zezula, P.: Identifying Walk Cycles for Human Recognition. In: Chau, M., Wang, G.A., Yue, W.T., Chen, H. (eds.) PAISI 2012. LNCS, vol. 7299, pp. 127–135. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    Wang, L., Ning, H., Tan, T., Hu, W.: Fusion of static and dynamic body biometrics for gait recognition. IEEE Transactions on Circuits and Systems for Video Technology 14(2), 149–158 (2004)CrossRefGoogle Scholar
  10. 10.
    Xue, Z., Ming, D., Song, W., Wan, B., Jin, S.: Infrared gait recognition based on wavelet transform and support vector machine. Pattern Recognition 43(8), 2904–2910 (2010)zbMATHCrossRefGoogle Scholar
  11. 11.
    Yoo, J.H., Hwang, D., Moon, K.Y., Nixon, M.S.: Automated human recognition by gait using neural network. In: Workshops on Image Processing Theory, Tools and Applications, pp. 1–6. IEEE (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jan Sedmidubsky
    • 1
  • Jakub Valcik
    • 1
  • Michal Balazia
    • 1
  • Pavel Zezula
    • 1
  1. 1.Masaryk UniversityBrnoCzech Republic

Personalised recommendations