A New Representation for Human Gait Recognition: Motion Silhouettes Image (MSI)

  • Toby H. W. Lam
  • Raymond S. T. Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)


Recently, gait recognition for human identification has received substantial attention from biometrics researchers. Compared with other biometrics, it is more difficult to disguise. In addition, gait can be captured in a distance by using low-resolution capturing devices. In this paper, we proposed a new representation for human gait recognition which is called Motion Silhouettes Image (MSI). MSI is a grey-level image which embeds the critical spatio-temporal information. Experiments showed that MSI has a high discriminative power for gait recognition. The recognition rate is around 87% in SOTON dataset by using MSI for recognition. The recognition rate is quite promising. In addition, MSI can also reduce the storage size of the dataset. After using MSI, the storage size of SOTON has reduced to 4.2MB.


Recognition Rate Kernel Principle Component Analysis High Discriminative Power Storage Size Gait Recognition 
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.


  1. 1.
    Murray, M.P., Drought, A.B., Kory, R.C.: Walking patterns of normal men. Journal of Bone and Joint Surgery 46-A(2), 335–360Google Scholar
  2. 2.
    Bobick, A.F., Davis, J.W.: The recognition of human movement using temporal templates. IEEE Trans. on PAMI 23(3), 257–267 (2001)Google Scholar
  3. 3.
    Shutler, J.D., Grant, M.G., Nixon, M.S., Carter, J.N.: On a Large Sequence-Based Human Gait Database. In: Proc. 4th International Conference on Recent Advances in Soft Computing, Nottingham (UK), pp. 66–71 (2002)Google Scholar
  4. 4.
    Murase, H., Sakai, R.: Moving object recognition in eigenspace representation: gait analysis and lip reading. Pattern Recognition Letters 17, 155–162 (1996)CrossRefGoogle Scholar
  5. 5.
    Turk, M., Pentland, A.: Face Recognition using Eigenfaces. In: Proceedings of the Computer Vision and Pattern Recognition (1991)Google Scholar
  6. 6.
    Huang, P.S., Harris, C.J., Nixon, M.S.: Human Gait Recognition in Canonical Space Using Temporal Templates. IEE Proceedings - Vision, Image and Signal Processing 146(2), 93–100 (1999)CrossRefGoogle Scholar
  7. 7.
    Wang, L., Tan, T.: Silhouette Analysis-Based Gait Recognition for Human Identification. IEEE Trans on PAMI 25(12), 1505–1518 (2003)Google Scholar
  8. 8.
    Phillips, J., Moon, H., Rizvi, S., Rause, P.: The FERET Evaluation Methodology for Face Recognition Algorithms. IEEE Trans. PAMI 22(8), 1090–1104 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Toby H. W. Lam
    • 1
  • Raymond S. T. Lee
    • 1
  1. 1.Department of ComputingThe Hong Kong Polytechnic UniversityKowloon, Hong Kong

Personalised recommendations