Probabilistic combination of static and dynamic gait features for verification

Bazin, Alex I. and Nixon, Mark S. (2005) Probabilistic combination of static and dynamic gait features for verification. At Biometric Technology for Human Identification II, SPIE Defense and Security Symposium, Orlando (Kissimmee), Florida , USA, SPIE, 23-30.


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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.

Item Type: Conference or Workshop Item (Speech)
Additional Information: Event Dates: March 2005
Keywords: Probabilistic, Biometrics, Gait, Bayesian, Logistic function, Fusion
Divisions : Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Southampton Wireless Group
ePrint ID: 260722
Accepted Date and Publication Date:
Date Deposited: 07 Apr 2005
Last Modified: 31 Mar 2016 14:02
Further Information:Google Scholar

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