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Probabilistic combination of static and dynamic gait features for verification

Probabilistic combination of static and dynamic gait features for verification
Probabilistic combination of static and dynamic gait features for verification
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.
Probabilistic, Biometrics, Gait, Bayesian, Logistic function, Fusion
23-30
Bazin, Alex I.
feead1f3-0fc6-4a1e-b089-f62361614633
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Jain, Anil K.
fdc5a2d3-c5f5-4124-b695-b718f004332f
Ratha, Nalini K.
ba80d3da-790c-478d-87c8-bb57ddb1489b
Bazin, Alex I.
feead1f3-0fc6-4a1e-b089-f62361614633
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Jain, Anil K.
fdc5a2d3-c5f5-4124-b695-b718f004332f
Ratha, Nalini K.
ba80d3da-790c-478d-87c8-bb57ddb1489b

Bazin, Alex I. and Nixon, Mark S. (2005) Probabilistic combination of static and dynamic gait features for verification. Jain, Anil K. and Ratha, Nalini K. (eds.) Biometric Technology for Human Identification II, SPIE Defense and Security Symposium, United States. pp. 23-30 .

Record type: Conference or Workshop Item (Other)

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.

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More information

Published date: 2005
Additional Information: Event Dates: March 2005
Venue - Dates: Biometric Technology for Human Identification II, SPIE Defense and Security Symposium, United States, 2005-03-01
Keywords: Probabilistic, Biometrics, Gait, Bayesian, Logistic function, Fusion
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 260722
URI: https://eprints.soton.ac.uk/id/eprint/260722
PURE UUID: 95950f5f-51c5-4989-983f-a1f6a43d7aea
ORCID for Mark S. Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 07 Apr 2005
Last modified: 06 Jun 2018 13:17

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