Feature integration for heart sound biometrics

This paper proposes a feature integration framework for heart sound biometric applications. The method selects the best features of different sound classification systems into a unique heart sound biometric system. The framework is developed and tested for both user identification and verification t...

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Bibliographic Details
Main Authors: Dat Huy Tran, Yi Ren Leng, Haizhou Li
Format: Conference Proceeding
Language:English
Subjects:
Biometric
Biometrics
Cepstral analysis
Feature extraction
Feature Integration
Feature Selection
Frequency
Heart
Heart Sound
Music information retrieval
Recursive Feature Elimination
Speech
Support Vector Machine
Support vector machine classification
Support vector machines
Testing
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Description
Summary:This paper proposes a feature integration framework for heart sound biometric applications. The method selects the best features of different sound classification systems into a unique heart sound biometric system. The framework is developed and tested for both user identification and verification tasks. The experimental results show significant improvements in performance of the proposed system over methods adopting single feature extraction. Among the investigated feature extraction methods, the linear frequency band cepstral coefficients (LFCC) and the GMM super vector are shown to be the best complementary methods.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2010.5495476