Personal Identification Using Footstep Based on Wavelets

Abstract

The characteristics of a footstep are determined by the gait, the footwear and the floor. Accurate footstep analysis would be useful in various applications, home security service, surveillance and understanding of human action since the gait expresses personality, age and gender. The feasibility of personal identification has been confirmed by using the feature parameter of footsteps, however, it is necessary to use more effective parameters since the recognition rate of this method decreases as the number of subjects increases. In this paper, wavelet transform is applied to feature extraction from footsteps. In audio classification, Fourier and wavelet transform are used to extract the feature of audio signals. Results show that the parameter proposed herein yields effective and practical personal identification