Gait recognition using fractal scale and wavelet moments. Zhao, G.; Cui, L.; Li, H.; and Pietikäinen, M. In 2006. abstract bibtex Video-based gait recognition is a challenging
problem in computer vision. In this paper, fractal scale
wavelet analysis is applied to describe and
automatically recognize gait. Fractal scale based on
wavelet analysis represents the self-similarity of
signals, and improves the flexibility of wavelet
moments. Optimal wavelets based on generalized
multi-resolution analysis are used to improve the
recognition rate. Descriptors of fractal scale are
translation, scale and rotation invariant. Moreover, a
combination of fractal scale and wavelet moments
improves the recognition rate. Experiments show that
the proposed descriptor is efficient for gait recognition.
@inProceedings{
title = {Gait recognition using fractal scale and wavelet moments.},
type = {inProceedings},
year = {2006},
id = {ddf1ee2d-47c7-39c8-8fc2-02e8916cd291},
created = {2019-11-19T16:28:57.691Z},
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last_modified = {2019-11-19T16:32:43.188Z},
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notes = {Proc. 18th International Conference on Pattern Recognition (ICPR 2006), Hong Kong, 4: 4 p.},
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abstract = {Video-based gait recognition is a challenging
problem in computer vision. In this paper, fractal scale
wavelet analysis is applied to describe and
automatically recognize gait. Fractal scale based on
wavelet analysis represents the self-similarity of
signals, and improves the flexibility of wavelet
moments. Optimal wavelets based on generalized
multi-resolution analysis are used to improve the
recognition rate. Descriptors of fractal scale are
translation, scale and rotation invariant. Moreover, a
combination of fractal scale and wavelet moments
improves the recognition rate. Experiments show that
the proposed descriptor is efficient for gait recognition.},
bibtype = {inProceedings},
author = {Zhao, G and Cui, L and Li, H and Pietikäinen, M}
}