Probabilistic combination of static and dynamic gait features for verification

Alex I. Bazin; Mark S. Nixon

doi:10.1117/12.602107

28 March 2005 Probabilistic combination of static and dynamic gait features for verification

Alex I. Bazin, Mark S. Nixon

Proceedings Volume 5779, Biometric Technology for Human Identification II; (2005) https://doi.org/10.1117/12.602107
Event: Defense and Security, 2005, Orlando, Florida, United States

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.

Citation Download Citation

Alex I. Bazin and Mark S. Nixon "Probabilistic combination of static and dynamic gait features for verification", Proc. SPIE 5779, Biometric Technology for Human Identification II, (28 March 2005); https://doi.org/10.1117/12.602107

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