A New Approach for Human Identification Using Gait Recognition

  • Murat Ekinci
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3982)


Recognition of a person from gait is a biometric of increasing interest. This paper presents a new approach on silhouette representation to extract gait patterns for human recognition. Silhouette shape of a motion object is first represented by four 1-D signals which are the basic image features called the distance vectors. The distance vectors are differences between the bounding box and silhouette. Second, eigenspace transformation based on Principal Component Analysis is applied to time-varying distance vectors and the statistical distance based supervised pattern classification is then performed in the lower-dimensional eigenspace for recognition. A fusion task is finally executed to produce final decision. Experimental results on three databases show that the proposed method is an effective and efficient gait representation for human identification, and the proposed approach achieves highly competitive performance with respect to the published gait recognition approaches.


Recognition Performance Gait Cycle Gesture Recognition Distance Vector Slow Walk 
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.
    Wang, L., Tan, T., Ning, H., Hu, W.: Silhouette Analysis-Based Gait Recognition for Human Identification. IEEE Trans. on PAMI 25(12) (December 2003)Google Scholar
  2. 2.
    BenAbdelkader, C., Cutler, R.G., Davis, L.S.: Gait Recognition Using Image Self-Similarity. EURASIP Journal of Applied Signal Processing (April 2004)Google Scholar
  3. 3.
    Veres, G.V., et al.: What image information is important in silhouette-based gait recognition? In: Proc. IEEE Conf. on Computer Vision and Pattern Recognition (2004)Google Scholar
  4. 4.
    Huang, P., Harris, C., Nixon, M.: Human Gait Recognition in Canonical Space Using Temporal Templates. In: IEE Proc. Vision Image and Signal Proc. Conf. (1999)Google Scholar
  5. 5.
    Ekinci, M., Gedikli, E.: Background Estimation Based People Detection and Tracking for Video Surveillance. In: Yazıcı, A., Şener, C. (eds.) ISCIS 2003. LNCS, vol. 2869, pp. 421–429. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  6. 6.
    Sarkar, S., et al.: The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis. IEEE Trans. on Pat. Anal. and Mach. Intell. 27(2) (2005)Google Scholar
  7. 7.
    Kale, A., et al.: Identification of Humans Using Gait. IEEE Trans. on Image Processing 13(9) (September 2004)Google Scholar
  8. 8.
    Lee, L., Grimson, W.: Gait Analysis for Recognition and Classification. In: Proc. IEEE, Int. Conference on Automatic Face and Gesture Recognition, pp. 155–162 (2002)Google Scholar
  9. 9.
    Liu, Y., Collins, R.T., Tsin, T.: Gait Sequence Analysis using Frieze Patterns. In: Proc. of European Conf. on Computer Vision (2002)Google Scholar
  10. 10.
    BenAbdelkader, C., et al.: Stride and Cadence as a Biometric in Automatic Person Identification and Verification. In: Proc. Int. Conf. Aut. Face and Gesture Recog. (2002)Google Scholar
  11. 11.
    Collins, R., Gross, R., Shi, J.: Silhouette-Based Human Identification from Body Shape and Gait. In: Proc. Int. Conf. Automatic Face and Gesture Recognition (2002)Google Scholar
  12. 12.
    Phillips, J., et al.: The FERET Evaluation Methodology for Face recognition Algorithm. IEEE Trans. Pattern Analysis and Machine Intell. 22(10) (October 2000)Google Scholar
  13. 13.
    Gross, R., Shi, J.: The CMU motion of body (MOBO) database. Tech. Rep. CMU-RI-TR-01-18, Robotics Institute, Carnegie Mellon University (June 2001)Google Scholar
  14. 14.
    Ekinci, M., Gedikli, E.: A Novel Approach on Silhouette Based Human Motion Analysis for Gait Recognition. In: Bebis, G., Boyle, R., Koracin, D., Parvin, B. (eds.) ISVC 2005. LNCS, vol. 3804, pp. 219–226. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    Bazin, A.I., Nixon, M.S.: Gait Verification Using Probabilistic Methods. In: IEEE Workshop on Applications of Computer Vision (2005)Google Scholar
  16. 16.
    Phillips, P., et al.: Baseline Results for Challenge Problem of Human ID using Gait Analysis. In: Proc. Int. Conf. Automatic Face and Gesture Recognition (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Murat Ekinci
    • 1
  1. 1.Computer Vision Lab, Department of Computer EngineeringKaradeniz Technical UniversityTrabzonTurkey

Personalised recommendations