Gait Verification Using Probabilistic Methods
Gait Verification Using Probabilistic Methods
In this paper we describe a novel method for gait based identity verification based on Bayesian classification. The verification task is reduced to a two class problem (Client or Impostor) with logistic functions constructed to provide probability estimates of intra-class (Client) and inter-class (Impostor) likelihoods. These likelihoods are combined using Bayes rule and thresholded to provide a decision boundary. Since the outputs of the classifier are probabilities they are particularly well suited for use without modification in classifier fusion schemes. On tests using 1664 examples from 100 clients and 100 impostors the Bayesian method achieved an equal error rate of 7.3%. The improvement over a Euclidean distance classifier was shown to be statistically significant at the 5% level using McNemar’s test.
Gait, Logistic Function, Bayesian
50-55
Bazin, Alex I.
feead1f3-0fc6-4a1e-b089-f62361614633
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
2005
Bazin, Alex I.
feead1f3-0fc6-4a1e-b089-f62361614633
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Bazin, Alex I. and Nixon, Mark S.
(2005)
Gait Verification Using Probabilistic Methods.
7th IEEE Workshop on Applications of Computer Vision, Breckenridge, CO.
.
Record type:
Conference or Workshop Item
(Poster)
Abstract
In this paper we describe a novel method for gait based identity verification based on Bayesian classification. The verification task is reduced to a two class problem (Client or Impostor) with logistic functions constructed to provide probability estimates of intra-class (Client) and inter-class (Impostor) likelihoods. These likelihoods are combined using Bayes rule and thresholded to provide a decision boundary. Since the outputs of the classifier are probabilities they are particularly well suited for use without modification in classifier fusion schemes. On tests using 1664 examples from 100 clients and 100 impostors the Bayesian method achieved an equal error rate of 7.3%. The improvement over a Euclidean distance classifier was shown to be statistically significant at the 5% level using McNemar’s test.
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Published date: 2005
Additional Information:
Event Dates: 08/01/2005
Venue - Dates:
7th IEEE Workshop on Applications of Computer Vision, Breckenridge, CO, 2005-01-08
Keywords:
Gait, Logistic Function, Bayesian
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 260271
URI: http://eprints.soton.ac.uk/id/eprint/260271
PURE UUID: 1096cb53-e5fe-4fc8-ac84-f73ac442d8b0
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Date deposited: 14 Jan 2005
Last modified: 15 Mar 2024 02:35
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Contributors
Author:
Alex I. Bazin
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