Abstract
Gait recognition is an attractive biometric as it is unobtrusive and can be used for recognition from a distance. A number of methods have been proposed by different researchers in the recent past for this purpose. Most of these methods analyze gait as a linear and stationary signal. However, recent research shows that gait is nonlinear and non-stationary. Hence, linear analysis would be insufficient for analysis of gait. In this paper, we present a novel recognition algorithm that derives the feature vector by performing nonlinear, non-stationary analysis of gait using a technique called empirical mode decomposition. We test the algorithm with both noise-free and noisy gait sequences and demonstrate its applicability.
Original language | English (US) |
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Pages (from-to) | II117-II120 |
Journal | Proceedings - IEEE International Symposium on Circuits and Systems |
Volume | 2 |
State | Published - Sep 7 2004 |
Event | 2004 IEEE International Symposium on Circuits and Systems - Proceedings - Vancouver, BC, Canada Duration: May 23 2004 → May 26 2004 |
ASJC Scopus subject areas
- Electrical and Electronic Engineering