New Area Measures for Automatic Gait Recognition
New Area Measures for Automatic Gait Recognition
A biometric is a measure of some human characteristic that can be used to distinguish between individuals. Gait is a new biometric aimed at recognising someone by the way they walk. It is not immediately apparent that gait is a unique biometric, however even from Shakespearian times there has been reference to gait recognition. In The Tempest [Act 4, Scene 1], Ceres observes "High'st Queen of state, Great Juno comes; I know her by her gait?". Gait has several notable advantages over other biometrics such as fingerprints. It is a non-invasive technique, meaning that the subject need not even know they are being recognised. Gait also allows recognition from a great distance where other biometrics such as face recognition might fail. A bank robber can disguise their gait less easily than their face, in fact disguising one's gait only has the effect of making oneself look more suspicious! We describe a new technique for recognising gait, which we call gait masks. Essentially, gait masks are used to derive information from a sequence of silhouettes. This information is directly related to the gait of the subject. The table below shows example gait masks and silhouettes from the sequences to which they are applied. The gait masks measure how the silhouette changes over time in a chosen region of the body. These area changes are intimately related to the nature of gait. Application shows the sinusoidal nature of the output from a vertical line mask. The peaks in the graph correspond to when the legs are closest together, and the dips represent when the legs are at furthest flexion. Using Canonical Analysis it is possible to use this output for recognition purposes. Initial results are promising with a correct recognition rate of over 80% on a small database. Future work will concentrate on combining results from different gait masks with the aim of increasing the overall recognition rate. In addition, the performance of the system on a much larger database of subjects will be evaluated.
Foster, Jeff P.
c917988b-b903-4845-9e64-6412e7ac33ab
Prugel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
January 2001
Foster, Jeff P.
c917988b-b903-4845-9e64-6412e7ac33ab
Prugel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Foster, Jeff P., Prugel-Bennett, Adam and Nixon, Mark S.
(2001)
New Area Measures for Automatic Gait Recognition.
BMVA Workshop Understanding Visual Behaviour.
Record type:
Conference or Workshop Item
(Poster)
Abstract
A biometric is a measure of some human characteristic that can be used to distinguish between individuals. Gait is a new biometric aimed at recognising someone by the way they walk. It is not immediately apparent that gait is a unique biometric, however even from Shakespearian times there has been reference to gait recognition. In The Tempest [Act 4, Scene 1], Ceres observes "High'st Queen of state, Great Juno comes; I know her by her gait?". Gait has several notable advantages over other biometrics such as fingerprints. It is a non-invasive technique, meaning that the subject need not even know they are being recognised. Gait also allows recognition from a great distance where other biometrics such as face recognition might fail. A bank robber can disguise their gait less easily than their face, in fact disguising one's gait only has the effect of making oneself look more suspicious! We describe a new technique for recognising gait, which we call gait masks. Essentially, gait masks are used to derive information from a sequence of silhouettes. This information is directly related to the gait of the subject. The table below shows example gait masks and silhouettes from the sequences to which they are applied. The gait masks measure how the silhouette changes over time in a chosen region of the body. These area changes are intimately related to the nature of gait. Application shows the sinusoidal nature of the output from a vertical line mask. The peaks in the graph correspond to when the legs are closest together, and the dips represent when the legs are at furthest flexion. Using Canonical Analysis it is possible to use this output for recognition purposes. Initial results are promising with a correct recognition rate of over 80% on a small database. Future work will concentrate on combining results from different gait masks with the aim of increasing the overall recognition rate. In addition, the performance of the system on a much larger database of subjects will be evaluated.
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More information
Published date: January 2001
Additional Information:
http://www.bmva.ac.uk/meetings/meetings/01/24jan01/index.html. Organisation: British Machine Vision Association
Venue - Dates:
BMVA Workshop Understanding Visual Behaviour, 2001-01-01
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 254254
URI: http://eprints.soton.ac.uk/id/eprint/254254
PURE UUID: 941827f3-4bab-40a5-b176-55a6c2b83bfd
Catalogue record
Date deposited: 20 Nov 2003
Last modified: 11 Dec 2021 02:38
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Contributors
Author:
Jeff P. Foster
Author:
Adam Prugel-Bennett
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