@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},
 file_attached = {false},
 profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},
 group_id = {28b2996c-b80f-3c26-be71-695caf7040ac},
 last_modified = {2019-11-19T16:32:43.188Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {mvg:728},
 source_type = {inproceedings},
 notes = {Proc. 18th International Conference on Pattern Recognition (ICPR 2006), Hong Kong, 4: 4 p.},
 folder_uuids = {8292f5ec-1c57-4113-a303-25778e695f8c},
 private_publication = {false},
 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}
}

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