Walker Recognition Without Gait Cycle Estimation
Most of gait recognition algorithms involve walking cycle estimation to accomplish signature matching. However, we may be plagued by two cycle-related issues when developing real-time gait-based walker recognition systems. One is accurate cycle evaluation, which is computation intensive, and the other is the inconvenient acquisition of long continuous sequences of gait patterns, which are essential to the estimation of gait cycles. These drive us to address the problem of distant walker recognition from another view toward gait, in the hope of detouring the step of gait cycle estimation. This paper proposes a new gait representation, called normalized dual-diagonal projections (NDDP), to characterize walker signatures and employs a normal distribution to approximately describe the variation of each subject’s gait signatures in the statistical sense. We achieve the recognition of unknown gait features in a simplified Bayes framework after reducing the dimension of raw gait signatures based on linear subspace projections. Extensive experiments demonstrate that our method is effective and promising.
KeywordsGait recognition cycle estimation PCA LDA
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- 1.Collins, R., Gross, R., Shi, J.: Silhouette-based human identification from body shape and gait. In: Proc. Automatic Face and Gesture Recognition, pp. 366–371 (2002)Google Scholar
- 3.Cunado, D., Nixon, M., Carter, J.: Automatic extraction and description of human gait model for recognition purposes. CVIU 90(1), 1–41 (2003)Google Scholar
- 5.Han, J., Bhanu, B.: Statistical feature fusion for gait-based human recognition. Proc. CVPR (2004)Google Scholar
- 8.Liu, Z., Sarkar, S.: Simplest representation yet for gait recognition: Averaged silhouette. Proc. ICPR (2004)Google Scholar
- 10.Sarkar, S., Philips, P., Liu, Z., Vega, I., Grother, P., Bowyer, K.: The human gait challenge problem: data sets, performance and analysis. PAMI 27(2), 162–177 (2005)Google Scholar
- 11.Tan, D., Huang, K., Yu, S., Tan, T.: Efficient night gait recognition based on template matching. In: Proc. ICPR, pp. 1000–1003 (2006)Google Scholar
- 12.Urtasun, R., Fua, P.: 3d tracking for gait characterization and recognition. In: Proc. Automatic Face and Gesture Recognition, pp. 17–22 (2004)Google Scholar
- 13.Veeraraghavan, A., Roy-Chowdhury, A., Chellappa, R.: Matching shape sequences in video with applications in human movement analysis. PAMI 27(12), 1896–1909 (2005)Google Scholar
- 14.Veres, G., Gordon, L., Carter, J., Nixon, M.: What image information is important in silhouette-based gait recognition? In: Proc. CVPR (2004)Google Scholar
- 15.Wang, L., Tan, T., Hu, W., Ning, H.: Silhouette analysis-based gait recognition for human identification. PAMI 25(12), 1505–1518 (2003)Google Scholar