The University of Southampton
University of Southampton Institutional Repository

Model-based 3D gait biometrics

Model-based 3D gait biometrics
Model-based 3D gait biometrics
There have as yet been few gait biometrics approaches which use temporal 3D data. Clearly, 3D gait data conveys more information than 2D data and it is also the natural representation of human gait perceived by human. In this paper we explore the potential of using model-based methods in a 3D volumetric (voxel) gait dataset. We use a structural model including articulated cylinders with 3D Degrees of Freedom (DoF) at each joint to model the human lower legs. We develop a simple yet effective model-fitting algorithm using this gait model, correlation filter and a dynamic programming approach. Human gait kinematics trajectories are then extracted by fitting the gait model into the gait data. At each frame we generate a correlation energy map between the gait model and the data. Dynamic programming is used to extract the gait kinematics trajectories by selecting the most likely path in the whole sequence. We are successfully able to extract both gait structural and dynamics features. Some of the features extracted here are inherently unique to 3D data. Analysis on a database of 46 subjects each with 4 sample sequences, shows an encouraging correct classification rate and suggests that 3D features can contribute even more.
model-based 3D gait, biometrics, gait biometrics
Ariyanto, Gunawan
a36977d0-5857-4caa-8e3a-88d41f85c304
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Ariyanto, Gunawan
a36977d0-5857-4caa-8e3a-88d41f85c304
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12

Ariyanto, Gunawan and Nixon, Mark (2011) Model-based 3D gait biometrics. International Joint Conference on Biometrics 2011. 11 - 13 Oct 2011.

Record type: Conference or Workshop Item (Poster)

Abstract

There have as yet been few gait biometrics approaches which use temporal 3D data. Clearly, 3D gait data conveys more information than 2D data and it is also the natural representation of human gait perceived by human. In this paper we explore the potential of using model-based methods in a 3D volumetric (voxel) gait dataset. We use a structural model including articulated cylinders with 3D Degrees of Freedom (DoF) at each joint to model the human lower legs. We develop a simple yet effective model-fitting algorithm using this gait model, correlation filter and a dynamic programming approach. Human gait kinematics trajectories are then extracted by fitting the gait model into the gait data. At each frame we generate a correlation energy map between the gait model and the data. Dynamic programming is used to extract the gait kinematics trajectories by selecting the most likely path in the whole sequence. We are successfully able to extract both gait structural and dynamics features. Some of the features extracted here are inherently unique to 3D data. Analysis on a database of 46 subjects each with 4 sample sequences, shows an encouraging correct classification rate and suggests that 3D features can contribute even more.

Text
PID2013657.pdf - Other
Download (2MB)

More information

Published date: 11 October 2011
Additional Information: Event Dates: 11-13 October 2011
Venue - Dates: International Joint Conference on Biometrics 2011, 2011-10-11 - 2011-10-13
Keywords: model-based 3D gait, biometrics, gait biometrics
Organisations: Vision, Learning and Control

Identifiers

Local EPrints ID: 272936
URI: https://eprints.soton.ac.uk/id/eprint/272936
PURE UUID: 71b434bb-5f05-412f-a18f-1e48d0c7a948
ORCID for Mark Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 17 Oct 2011 11:42
Last modified: 05 Nov 2019 02:09

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×