Human identification based on gait paths

Authors:
Adam Šwitoński;Andrzej Polański;Konrad Wojciechowski
Affiliations:
Polish-Japanese Institute of Information Technology, Bytom, Poland and Silesian Ubiversity of Technology, ul. Akademicka, Gliwice, Poland;Polish-Japanese Institute of Information Technology, Bytom, Poland and Silesian Ubiversity of Technology, ul. Akademicka, Gliwice, Poland;Polish-Japanese Institute of Information Technology, Bytom, Poland and Silesian Ubiversity of Technology, ul. Akademicka, Gliwice, Poland
Venue:
ACIVS'11 Proceedings of the 13th international conference on Advanced concepts for intelligent vision systems
Year:
2011

Citing 9
Cited 1

Instance-Based Learning Algorithms

Machine Learning
Motion graphs

Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Silhouette Analysis-Based Gait Recognition for Human Identification

IEEE Transactions on Pattern Analysis and Machine Intelligence
The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Letters: View-independent person identification from human gait

Neurocomputing
Articulated body motion tracking by combined particle swarm optimization and particle filtering

ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part I
Estimating continuous distributions in Bayesian classifiers

UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Biometric gait recognition

ASB'03 Proceedings of the 1st international conference on Advanced Studies in Biometrics

Gait identification based on MPCA reduction of a video recordings data

ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Gait paths are spatial trajectories of selected body points during person's walk. We have proposed and evaluated features extracted from gait paths for the task of person identification. We have used the following gait paths: skeleton root element, feet, hands and head. In our motion capture laboratory we have collected human gait database containing 353 different motions of 25 actors. We have proposed four approaches to extract features from motion clips: statistical, histogram, Fourier transform and timeline We have prepared motion filters to reduce the impact of the actor's location and actor's height on the gait path. We have applied supervised machine learning techniques to classify gaits described by the proposed feature sets. We have prepared scenarios of the features selections for every approach and iterated classification experiments. On the basis of obtained classifications results we have discovered most remarkable features for the identification task. We have achieved almost 97% identification accuracy for normalized paths.