Automatic Gait Recognition via Model-Based Evidence Gathering
Automatic Gait Recognition via Model-Based Evidence Gathering
Recognising people by gait is of emergent interest. A new model-based moving feature extraction analysis is presented that automatically extracts and describes human gait for recognition. The gait signature is extracted directly from the evidence gathering process by using a Fourier series to describe the motion of the upper leg and apply temporal evidence gathering techniques to extract the moving model from a sequence of images. Performance has been confirmed by simulation and on a small subject database and verified using statistical analysis of the separation of clusters in feature space. Further, the technique can handle occlusion, of especial importance in gait as the human body is self-occluding. As such, a new technique has been developed to automatically extract and describe a moving articulated shape, the human leg, and shown its potential in gait as a biometric.
27-30
Cunado, David
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Nixon, Mark S.
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Carter, John N.
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O'Gorman, Larry
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Shellhammer, Steve
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October 1999
Cunado, David
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Nixon, Mark S.
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Carter, John N.
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O'Gorman, Larry
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Shellhammer, Steve
3d3e0a77-8df1-4e97-8b0e-af84010f3bf2
Cunado, David, Nixon, Mark S. and Carter, John N.
(1999)
Automatic Gait Recognition via Model-Based Evidence Gathering.
O'Gorman, Larry and Shellhammer, Steve
(eds.)
Proceedings AutoID '99: IEEE Workshop on Identification Advanced Technologies.
.
Record type:
Conference or Workshop Item
(Other)
Abstract
Recognising people by gait is of emergent interest. A new model-based moving feature extraction analysis is presented that automatically extracts and describes human gait for recognition. The gait signature is extracted directly from the evidence gathering process by using a Fourier series to describe the motion of the upper leg and apply temporal evidence gathering techniques to extract the moving model from a sequence of images. Performance has been confirmed by simulation and on a small subject database and verified using statistical analysis of the separation of clusters in feature space. Further, the technique can handle occlusion, of especial importance in gait as the human body is self-occluding. As such, a new technique has been developed to automatically extract and describe a moving articulated shape, the human leg, and shown its potential in gait as a biometric.
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More information
Published date: October 1999
Additional Information:
Organisation: IEEE and AIM
Venue - Dates:
Proceedings AutoID '99: IEEE Workshop on Identification Advanced Technologies, 1999-10-01
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 251948
URI: http://eprints.soton.ac.uk/id/eprint/251948
PURE UUID: 812ad8d7-d2ba-4c5e-b828-151ff8063414
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Date deposited: 19 Nov 1999
Last modified: 15 Mar 2024 02:34
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Contributors
Author:
David Cunado
Author:
John N. Carter
Editor:
Larry O'Gorman
Editor:
Steve Shellhammer
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