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)|
|Journal||Proceedings - IEEE International Symposium on Circuits and Systems|
|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