The University of Southampton
University of Southampton Institutional Repository

A practical technique for gait recognition on curved and straight trajectories

A practical technique for gait recognition on curved and straight trajectories
A practical technique for gait recognition on curved and straight trajectories
Many studies show the effectiveness of gait in surveillance and access control scenarios. However, appearance changes due to walking direction changes impose a challenge for gait recognition techniques that assume people only walk in a straight line. In this paper, the effect of walking along straight and curved path is studied by proposing a practical technique which is based on the three key frames in the start, middle and end of the gait cycle. The position of these frames is estimated in 3D space which is then used to estimate the local walking direction in the first and second part of the cycle. The technique used 3D volume sequences of the people to adapt to changes in the walking direction. The performance is evaluated using a newly collected dataset and the Kyushu University 4D Gait Dataset, containing people walking in straight lines and curves. With the proposed technique, we obtain a correct classification rate of 98% for matching straight with straight walking and 81% for matching straight with curved walking averaged over both datasets. The variation in walking patterns when a person walks along a straight or curved path is most likely to be responsible for the difference. In support of this, the recognition rate when matching curved with curved walking is 99% on our dataset.
Abdulsattar, Fatimah
ac510c08-4292-43f6-aa92-e636b8319b86
Carter, John
e05be2f9-991d-4476-bb50-ae91606389da
Abdulsattar, Fatimah
ac510c08-4292-43f6-aa92-e636b8319b86
Carter, John
e05be2f9-991d-4476-bb50-ae91606389da

Abdulsattar, Fatimah and Carter, John (2016) A practical technique for gait recognition on curved and straight trajectories. ICB-2016 The 9th IAPR International Conference on Biometrics, Sweden. 13 - 16 Jun 2016. 6 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

Many studies show the effectiveness of gait in surveillance and access control scenarios. However, appearance changes due to walking direction changes impose a challenge for gait recognition techniques that assume people only walk in a straight line. In this paper, the effect of walking along straight and curved path is studied by proposing a practical technique which is based on the three key frames in the start, middle and end of the gait cycle. The position of these frames is estimated in 3D space which is then used to estimate the local walking direction in the first and second part of the cycle. The technique used 3D volume sequences of the people to adapt to changes in the walking direction. The performance is evaluated using a newly collected dataset and the Kyushu University 4D Gait Dataset, containing people walking in straight lines and curves. With the proposed technique, we obtain a correct classification rate of 98% for matching straight with straight walking and 81% for matching straight with curved walking averaged over both datasets. The variation in walking patterns when a person walks along a straight or curved path is most likely to be responsible for the difference. In support of this, the recognition rate when matching curved with curved walking is 99% on our dataset.

Text
A Practical Technique for Gait Recognition on Curved and Straight Trajectories.pdf - Accepted Manuscript
Download (324kB)

More information

Accepted/In Press date: 14 March 2016
e-pub ahead of print date: 13 June 2016
Venue - Dates: ICB-2016 The 9th IAPR International Conference on Biometrics, Sweden, 2016-06-13 - 2016-06-16
Organisations: Vision, Learning and Control

Identifiers

Local EPrints ID: 400692
URI: https://eprints.soton.ac.uk/id/eprint/400692
PURE UUID: 13d31479-f04f-445e-a3a2-e2466c3c1d0f

Catalogue record

Date deposited: 23 Sep 2016 13:44
Last modified: 09 Jan 2018 17:51

Export record

Contributors

Author: Fatimah Abdulsattar
Author: John Carter

University divisions

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.

×