Human identification at a distance by analysis of gait patterns extracted from video has recently become very popular research in biometrics. This paper presents multi-projections based approach to extract gait patterns for human recognition. Binarized silhouette of a motion object is represented by 1-D signals which are the basic image features called the distance vectors. The distance vectors are differences between the bounding box and silhouette, and extracted using four projections to silhouette. Based on normalized correlation on the distance vectors, gait cycle estimation is first performed to extract the gait cycle. Second, eigenspace transformation is applied to time-varying distance vectors and the statistical distance based supervised pattern classification is then performed in the lower-dimensional eigenspace for human identification. A fusion strategy developed is finally executed to produce final decision. Experimental results on four databases demonstrate that the right person in top two matches 100% of the times for the cases where training and testing sets corresponds to the same walking styles, and in top three-four matches 100% of the times for training and testing sets corresponds to the different walking styles.


Recognition Performance Gait Cycle Distance Vector Fusion Strategy Slow Walk 
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  1. 1.
    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
  2. 2.
    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
  3. 3.
    BenAbdelkader, C., Cutler, R.G., Davis, L.S.: Gait Recognition Using Image Self-Similarity. EURASIP Journal of Applied Signal Processing (April 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.
    Liu, Y., Collins, R.T., Tsin, T.: Gait Sequence Analysis using Frieze Patterns. In: Proc. of European Conf. on Computer Vision (2002)Google Scholar
  9. 9.
    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
  10. 10.
    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
  11. 11.
    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
  12. 12.
    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
  13. 13.
    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
  14. 14.
    Bazin, A.I., Nixon, M.S.: Gait Verification Using Probabilistic Methods. In: IEEE Workshop on Applications of Computer Vision (2005)Google Scholar
  15. 15.
    Phillips, P., et al.: Baseline Results for Challenge Problem of Human ID using Gait Analysis. In: Pro. 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., Dept. of Computer EngineeringKaradeniz Technical UniversityTrabzonTurkey

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