Gait Recognition Using Fractal Scale and Wavelet Moments

Authors:
Guoying Zhao;Li Cui;Hua Li;Matti Pietikainen
Affiliations:
University of Oulu, Finland;Beijing Normal University, Beijing, China;Chinese Academy of Sciences, Beijing, China;University of Oulu, Finland
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Year:
2006

Citing 0
Cited 2

A New Combined Fractal Scale Descriptor for Gait Sequence

MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Gait recognition using wearable motion recording sensors

EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in biometric systems: a signal processing perspective

Quantified Score

Hi-index	0.00

Visualization

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.