Abstract:
We consider the use of nonstationarity in the distribution of feature relationships over time for walking gait-based recognition. We statistically model the features of a...Show MoreMetadata
Abstract:
We consider the use of nonstationarity in the distribution of feature relationships over time for walking gait-based recognition. We statistically model the features of a person by computing the distribution of the relations among the features, rather than the features themselves. These relational distributions of feature relations are represented as points in a space of probability functions. Our database presently consists of twenty subjects walking outdoors along three different paths at 0/spl deg/ (frontal-parallel), 22/spl deg/ and 45/spl deg/ with respect to the image plane and walking in both directions, left to right and right to left. We performed statistical tests to demonstrate that variations between persons are statistically more significant than the variations due to walking angles and walking directions. We also present identification results on people walking at different directions and different angles.
Published in: 2002 International Conference on Pattern Recognition
Date of Conference: 11-15 August 2002
Date Added to IEEE Xplore: 10 December 2002
Print ISBN:0-7695-1695-X
Print ISSN: 1051-4651