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Covariate Analysis for View-point Independent Gait Recognition

Covariate Analysis for View-point Independent Gait Recognition
Covariate Analysis for View-point Independent Gait Recognition
Many studies have shown that gait can be deployed as a biometric. Few of these have addressed the effects of view-point and covariate factors on the recognition process. We describe the first analysis which combines view-point invariance for gait recognition which is based on a model-based pose estimation approach from a single un-calibrated camera. A set of experiments are carried out to explore how such factors including clothing, carrying conditions and view-point can affect the identification process using gait. Based on a covariate-based probe dataset of over 270 samples, a recognition rate of 73.4% is achieved using the KNN classifier. This confirms that people identification using dynamic gait features is still perceivable with better recognition rate even under the different covariate factors. As such, this is an important step in translating research from the laboratory to a surveillance environment.
Bouchrika, Imed
240fa05b-aed2-400a-a683-b4c0d20f2f68
Goffredo, Michela
21a346d2-8ce6-46b7-883f-89a2c584afc7
Carter, John
e05be2f9-991d-4476-bb50-ae91606389da
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Bouchrika, Imed
240fa05b-aed2-400a-a683-b4c0d20f2f68
Goffredo, Michela
21a346d2-8ce6-46b7-883f-89a2c584afc7
Carter, John
e05be2f9-991d-4476-bb50-ae91606389da
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12

Bouchrika, Imed, Goffredo, Michela, Carter, John and Nixon, Mark (2009) Covariate Analysis for View-point Independent Gait Recognition. The 3rd IAPR/IEEE International Conference on Biometrics, Italy.

Record type: Conference or Workshop Item (Paper)

Abstract

Many studies have shown that gait can be deployed as a biometric. Few of these have addressed the effects of view-point and covariate factors on the recognition process. We describe the first analysis which combines view-point invariance for gait recognition which is based on a model-based pose estimation approach from a single un-calibrated camera. A set of experiments are carried out to explore how such factors including clothing, carrying conditions and view-point can affect the identification process using gait. Based on a covariate-based probe dataset of over 270 samples, a recognition rate of 73.4% is achieved using the KNN classifier. This confirms that people identification using dynamic gait features is still perceivable with better recognition rate even under the different covariate factors. As such, this is an important step in translating research from the laboratory to a surveillance environment.

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Published date: 2009
Venue - Dates: The 3rd IAPR/IEEE International Conference on Biometrics, Italy, 2009-01-01
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 267034
URI: https://eprints.soton.ac.uk/id/eprint/267034
PURE UUID: 3ada2fa1-7bdb-4fd3-89b9-3db7b0173f9f
ORCID for Mark Nixon: ORCID iD orcid.org/0000-0002-9174-5934

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Date deposited: 15 Jan 2009 11:29
Last modified: 06 Jun 2018 13:18

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