Gait Recognition by Applying Multiple Projections and Kernel PCA
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Improved gait recognition by multiple-projections normalization
Machine Vision and Applications
An efficient gait recognition with backpack removal
EURASIP Journal on Advances in Signal Processing
Automatic gait recognition by multi-projection analysis
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
An automated system for contact lens inspection
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
A new approach for human identification using gait recognition
ICCSA'06 Proceedings of the 2006 international conference on Computational Science and Its Applications - Volume Part III
A new attempt to silhouette-based gait recognition for human identification
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
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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.