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A comparative approach between cepstral features for human authentication using heart sounds

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Abstract

The main objective of this paper is to provide a comparative study between different cepstral features for the application of human recognition using heart sounds. In the past 10 years, heart sound, which is known as phonocardiogram, has been adopted for human biometric authentication tasks. Most of the previously proposed systems have adopted mel-frequency and linear frequency cepstral coefficients as features for heart sounds. In this paper, two more cepstral features are proposed. The first one is based on wavelet packet decomposition where a new filter bank structure is designed to select the appropriate bases for extracting discriminant features from heart sounds. The other is based on nonlinear modification for mel-scaled cepstral features. The four cepstral features are tested and compared on two databases: One consists of 21 subjects, and the other consists of 206 subjects. Based on the achieved results over the two databases, the two proposed cepstral features achieved higher correct recognition rates and lower error rates in identification and verification modes, respectively.

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References

  1. Matsumoto, T., Matsumoto, H., Yamada, K., Hoshino, S.: Impact of artificial gummy fingers on fingerprint systems. In: Electronic Imaging 2002. International Society for Optics and Photonics, pp. 275–289 (2002)

  2. Nixon, K.A., Aimale, V., Rowe, R.K.: Spoof Detection Schemes. In: Jain, A.K., Flynn, P., Ross, A.A. (eds.) Handbook of Biometrics. Springer, pp. 403–423 (2008)

  3. Abbas, A.K., Bassam, R.: Phonocardiography signal processing. Synth. Lect. Biomed. Eng. 4(1), 1–194 (2009)

    Article  Google Scholar 

  4. Abo-Zahhad, M., Ahmed, S.M., Abbas, S.: Biometric authentication based on pcg and ecg signals: present status and future directions. Signal Image Video Process. 8(4), 739–751 (2014)

    Article  Google Scholar 

  5. Beritelli, F., Spadaccini, A.: Human identity verification based on heart sounds: recent advances and future directions. In: Yang, J. (ed.) Biometrics. INTECH Open Access Publisher, pp. 217–234 (2011)

  6. Beritelli, F., Serrano, S.: Biometric identification based on frequency analysis of cardiac sounds. IEEE Trans. Inf. Forensics Secur. 2(3), 596–604 (2007)

    Article  Google Scholar 

  7. Phua, K., Chen, J., Dat, T.H., Shue, L.: Heart sound as a biometric. Pattern Recog. 41(3), 906–919 (2008)

    Article  Google Scholar 

  8. Fatemian, S.Z., Agrafioti, F., Hatzinakos, D.: Heartid: Cardiac biometric recognition. In: 2010 Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS). IEEE, pp. 1–5 (2010)

  9. Fatemian, S.Z.: A wavelet-based approach to electrocardiogram (ecg) and phonocardiogram (pcg) subject recognition. Master’s thesis, University of Toronto (2009)

  10. Zhao, Z., Shen, Q., Ren, F.: Heart sound biometric system based on marginal spectrum analysis. Sensors 13(2), 2530–2551 (2013)

    Article  Google Scholar 

  11. Spadaccini, A., Beritelli, F.: Performance evaluation of heart sounds biometric systems on an open dataset. In: 2013 18th International Conference on Digital Signal Processing (DSP). IEEE, pp. 1–5 (2013)

  12. Delac, K., Grgic, M.: A survey of biometric recognition methods. In: Electronics in Marine 2004, Proceedings Elmar 2004, 46th International Symposium. IEEE, pp. 184–193 (2004)

  13. Biosec. pcg database. http://www.comm.utoronto.ca/~biometrics/databases.html

  14. Hsct-11 open database. http://www.diit.unict.it/hsct11/

  15. Mallat, S.: A Wavelet Tour of Signal Processing. Academic Press, Waltham (1999)

    MATH  Google Scholar 

  16. Messer, S.R., Agzarian, J., Abbott, D.: Optimal wavelet denoising for phonocardiograms. Microelectron. J. 32(12), 931–941 (2001)

    Article  Google Scholar 

  17. Guo, D.-f., Zhu, W.-h., Gao, Z.-m., Zhang, J.-q.: A study of wavelet thresholding denoising. In: 5th International Conference on Signal Processing Proceedings, 2000. WCCC-ICSP 2000, vol. 1. IEEE, pp. 329–332 (2000)

  18. Donoho, D.L., Johnstone, J.M.: Ideal spatial adaptation by wavelet shrinkage. Biometrika 81(3), 425–455 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  19. Opthof, T.: The normal range and determinants of the intrinsic heart rate in man. Cardiovasc. Res. 45(1), 177–184 (2000)

    Article  Google Scholar 

  20. Murty, K., Yegnanarayana, B.: Combining evidence from residual phase and mfcc features for speaker recognition. IEEE Signal Process. Lett. 13(1), 52–55 (2006)

    Article  Google Scholar 

  21. Vergin, R., O’shaughnessy, D., Farhat, A.: Generalized mel frequency cepstral coefficients for large-vocabulary speaker-independent continuous-speech recognition. IEEE Trans. Speech Audio Process. 7(5), 525–532 (1999)

    Article  Google Scholar 

  22. On, C.K., Pandiyan, P.M., Yaacob, S., Saudi, A.: Mel-frequency cepstral coefficient analysis in speech recognition. In: International Conference on Computing & Informatics, 2006. ICOCI’06. IEEE, pp. 1–5 (2006)

  23. Beritelli F., Spadaccini, A.: Human identity verification based on mel frequency analysis of digital heart sounds. In: 2009 16th International Conference on Digital Signal Processing. IEEE, pp. 1–5 (2009)

  24. Erzin, E., Yardimci, Y., et al.: Subband analysis for robust speech recognition in the presence of car noise. In: 1995 International Conference on Acoustics, Speech, and Signal Processing, 1995. ICASSP-95, vol. 1. IEEE, pp. 417–420 (1995)

  25. Pavez, E., Silva, J.F.: Analysis and design of wavelet-packet cepstral coefficients for automatic speech recognition. Speech Commun. 54(6), 814–835 (2012)

    Article  Google Scholar 

  26. Farooq, O., Datta, S.: Mel filter-like admissible wavelet packet structure for speech recognition. IEEE Signal Process. Lett. 8(7), 196–198 (2001)

    Article  Google Scholar 

  27. Misiti, M., Misiti, Y., Oppenheim, G., Poggi, J.-M.: Matlab wavelet toolbox user’s guide. Version 3 (2004)

  28. Quatieri, T.F.: Discrete-Time Speech Signal Processing: Principles and Practice. Pearson Education India, Gurgaon (2002)

  29. Rabiner, L.R., Juang, B.-H.: Fundamentals of Speech Recognition, vol. 14. PTR Prentice Hall, Englewood Cliffs (1993)

    MATH  Google Scholar 

  30. Jabloun, F., Erzin, E., et al.: Teager energy based feature parameters for speech recognition in car noise. IEEE Signal Process. Lett. 6(10), 259–261 (1999)

    Article  Google Scholar 

  31. Jabloun, F., et al.: The teager energy based feature parameters for robust speech recognition in car noise. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, 1999, vol. 1. IEEE, pp. 273–276 (1999)

  32. Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 4th edn. Academic Press, Waltham (2008)

    MATH  Google Scholar 

  33. Abo-Zahhad, M., Ahmed, S.M., Abbas, S.N.: Pcg biometric identification system based on feature level fusion using canonical correlation analysis. In: 2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE, pp. 1–6 (2014)

  34. Sun, Q.-S., Jin, Z., Heng, P.-A., Xia, D.-S.: A novel feature fusion method based on partial least squares regression. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds.) Pattern Recognition and Data Mining. Springer, pp. 268–277 (2005)

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Acknowledgments

The authors would like to thank Dimitrios Hatzinakos, Professor in the Electrical and Computer Engineering Department, University of Toronto, for his help in providing the BioSec. PCG database that made this work possible.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Faculty of Engineering, Assiut University, Assiut, Egypt

    M. Abo-Zahhad, Mohammed Farrag, Sherif N. Abbas & Sabah M. Ahmed

Authors
  1. M. Abo-Zahhad
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  2. Mohammed Farrag
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  3. Sherif N. Abbas
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  4. Sabah M. Ahmed
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Correspondence to Sherif N. Abbas.

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Abo-Zahhad, M., Farrag, M., Abbas, S.N. et al. A comparative approach between cepstral features for human authentication using heart sounds. SIViP 10, 843–851 (2016). https://doi.org/10.1007/s11760-015-0826-9

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  • DOI: https://doi.org/10.1007/s11760-015-0826-9

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