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A New Combined Fractal Scale Descriptor for Gait Sequence

  • Conference paper
Book cover Machine Learning and Data Mining in Pattern Recognition (MLDM 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4571))

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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.

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Authors and Affiliations

  1. School of Mathematics Sciences, Beijing Normal University, Beijing 100875,  

    Li Cui

  2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080,  

    Hua Li

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  1. Li Cui
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  2. Hua Li
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Petra Perner

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© 2007 Springer-Verlag Berlin Heidelberg

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Cui, L., Li, H. (2007). A New Combined Fractal Scale Descriptor for Gait Sequence. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2007. Lecture Notes in Computer Science(), vol 4571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73499-4_46

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  • DOI: https://doi.org/10.1007/978-3-540-73499-4_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73498-7

  • Online ISBN: 978-3-540-73499-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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