Gait analysis for human identification in frequency domain

In this paper, we analyze the spatio-temporal human characteristic of moving silhouettes in frequency domain, and find key Fourier descriptors that have better discriminatory capability for recognition than the other Fourier descriptors. A large number of experimental results and analysis show that the proposed algorithm based on the key Fourier descriptors can not only greatly reduce the gait data dimensionality, but also lighten the computation cost, with a satisfactory CCR. Besides that, classification performance can be further improved using feature fusion.

[1]  Luis Enrique Sucar,et al.  Human silhouette recognition with Fourier descriptors , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[2]  TanTieniu,et al.  Silhouette Analysis-Based Gait Recognition for Human Identification , 2003 .

[3]  Aaron F. Bobick,et al.  A Multi-view Method for Gait Recognition Using Static Body Parameters , 2001, AVBPA.

[4]  Tieniu Tan,et al.  Fusion of static and dynamic body biometrics for gait recognition , 2003, IEEE Transactions on Circuits and Systems for Video Technology.

[5]  Tieniu Tan,et al.  Silhouette Analysis-Based Gait Recognition for Human Identification , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Sudeep Sarkar,et al.  The gait identification challenge problem: data sets and baseline algorithm , 2002, Object recognition supported by user interaction for service robots.

[7]  A. N. Rajagopalan,et al.  Gait-based recognition of humans using continuous HMMs , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[8]  Mark S. Nixon,et al.  On automated model-based extraction and analysis of gait , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[9]  Mark S. Nixon,et al.  Automatic Gait Recognition via Fourier Descriptors of Deformable Objects , 2003, AVBPA.

[10]  Mark S. Nixon,et al.  On a Large Sequence-Based Human Gait Database , 2004 .