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Proceedings Paper

Probabilistic combination of static and dynamic gait features for verification
Author(s): Alex I. Bazin; Mark S. Nixon
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Paper Abstract

This paper describes a novel probabilistic framework for biometric identification and data fusion. Based on intra and inter-class variation extracted from training data, posterior probabilities describing the similarity between two feature vectors may be directly calculated from the data using the logistic function and Bayes rule. Using a large publicly available database we show the two imbalanced gait modalities may be fused using this framework. All fusion methods tested provide an improvement over the best modality, with the weighted sum rule giving the best performance, hence showing that highly imbalanced classifiers may be fused in a probabilistic setting; improving not only the performance, but also generalized application capability.

Paper Details

Date Published: 28 March 2005
PDF: 8 pages
Proc. SPIE 5779, Biometric Technology for Human Identification II, (28 March 2005); doi: 10.1117/12.602107
Show Author Affiliations
Alex I. Bazin, Univ. of Southampton (United Kingdom)
Mark S. Nixon, Univ. of Southampton (United Kingdom)


Published in SPIE Proceedings Vol. 5779:
Biometric Technology for Human Identification II
Anil K. Jain; Nalini K. Ratha, Editor(s)

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