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 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.