A New Combined Fractal Scale Descriptor for Gait Sequence

Authors:
Li Cui;Hua Li
Affiliations:
School of Mathematics Sciences, Beijing Normal University, Beijing 100875,;Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080,
Venue:
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2007

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Abstract

In this paper, we present a new combined fractal scale descriptor based on wavelet moments in gait recognition. This method is likely useful to general 2d objects pattern recognition. By introducing the Mallat algorithm of wavelet, it reduces the computational complexity compared with wavelet moments. Moreover, fractal scale has advantage on the self-similarity description of signals. And because it is based on wavelet moments, it is still translation, scale and rotation invariant, and have strongly anti-noise and occlusion handling performance. For completely decomposed signals, we get the new descriptor by combining the global and local fractal scale in each level. Experiments on a middle size database of gait sequences show that the new combined fractal scale method has simple computation and is an effective descriptor for 2-d objects.