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Gait Verification Using Probabilistic Methods

Record type: Conference or Workshop Item (Poster)

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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Citation

Bazin, Alex I. and Nixon, Mark S. (2005) Gait Verification Using Probabilistic Methods At 7th IEEE Workshop on Applications of Computer Vision. , pp. 50-55.

More information

Published date: 2005
Additional Information: Event Dates: 08/01/2005
Venue - Dates: 7th IEEE Workshop on Applications of Computer Vision, 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

Catalogue record

Date deposited: 14 Jan 2005
Last modified: 18 Jul 2017 09:14

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Contributors

Author: Alex I. Bazin
Author: Mark S. Nixon

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