Bazin, Alex I. and Nixon, Mark S.
Gait Verification Using Probabilistic Methods
At 7th IEEE Workshop on Applications of Computer Vision.
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.
Conference or Workshop Item
||Event Dates: 08/01/2005
|Venue - Dates:
||7th IEEE Workshop on Applications of Computer Vision, 2005-01-08
||Gait, Logistic Function, Bayesian
||Southampton Wireless Group
||14 Jan 2005
||17 Apr 2017 22:16
|Further Information:||Google Scholar|
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