Heart sound as a biometric

In this paper, we propose a novel biometric method based on heart sound signals. The biometric system comprises an electronic stethoscope, a computer equipped with a sound card and the software application. Our approach consists of a robust feature extraction scheme which is based on cepstral analysis with a specified configuration, combined with Gaussian mixture modeling. Experiments have been conducted to determine the relationship between various parameters in our proposed scheme. It has been demonstrated that heart sounds should be processed within segments of 0.5s and using the full resolution in frequency domain. Also, higher order cepstral coefficients that carry information on the excitation proved to be useful. A preliminary test of 128 heart sounds from 128 participants was collected to evaluate the uniqueness of the heart sounds. The HTK toolkit produces a 99% recognition rate with only one mismatch. Next, a more comprehensive test consisting almost 1000 heart sounds collected from 10 individuals over a period of 2 months yields a promising matching accuracy of 96% using the proposed feature and classification algorithm. A real-time heart sound authentication system is then built and can be used in two modes: to identify a particular individual or to verify an individual's claimed identity.

[1]  D. Reynolds,et al.  Authentication gets personal with biometrics , 2004, IEEE Signal Processing Magazine.

[2]  Robert B. Northrop.  Noninvasive Instrumentation and Measurement in Medical Diagnosis , 2001 .

[3]  Arun Ross,et al.  An introduction to biometric recognition , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[4]  Dario Forte.  Biometrics: Future Abuses , 2003 .

[5]  Robert M. Gray,et al.  An Algorithm for Vector Quantizer Design , 1980, IEEE Trans. Commun..

[6]  Ramaswamy Palaniappan,et al.  Individual identification technique using visual evoked potential signals , 2002 .

[7]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[8]  Brenda K. Wiederhold,et al.  ECG to identify individuals , 2005, Pattern Recognit..

[9]  A. Malliani,et al.  Heart rate variability. Standards of measurement, physiological interpretation, and clinical use , 1996 .

[10]  Douglas A. Reynolds,et al.  Robust text-independent speaker identification using Gaussian mixture speaker models , 1995, IEEE Trans. Speech Audio Process..

[11]  Ola Pettersson,et al.  ECG analysis: a new approach in human identification , 2001, IEEE Trans. Instrum. Meas..

[12]  Satoshi Hoshino,et al.  Impact of artificial "gummy" fingers on fingerprint systems , 2002, IS&T/SPIE Electronic Imaging.

[13]  William Francis Ganong,et al.  Review of Medical Physiology , 1969 .

[14]  L. O'Gorman,et al.  Comparing passwords, tokens, and biometrics for user authentication , 2003, Proceedings of the IEEE.

[15]  Anil K. Jain,et al.  Large-scale evaluation of multimodal biometric authentication using state-of-the-art systems , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Astrid Albrecht.  Understanding the issues behind user acceptance , 2001 .

[17]  Jr. J.P. Campbell,et al.  Speaker recognition: a tutorial , 1997, Proc. IEEE.

[18]  M. Faundez-Zanuy,et al.  On the vulnerability of biometric security systems , 2004, IEEE Aerospace and Electronic Systems Magazine.