Skip to main content
SpringerLink
Log in

Personal Identification Based on the Individual Sonographic Properties of the Auricle Using Cepstral Analysis and Bayes Formula

  • Published:
Cybernetics and Systems Analysis Aims and scope

Abstract

A method of personality recognition by echographic parameters of the human ear is developed based on the naive Bayes classifier in the two following modes: the biometric identification (EER = 0.0053) and the biometric authentication (FRR = 0.0002 at FAR ≤ 0.0001), respectively. A device is developed for recording the biometric characteristics of the external ear, and a set of echographic data is collected from the external ears of 75 subjects. The spectral and cepstral characteristics of the signals reflected from the ear canal are used as biometric parameters. Several window functions for constructing spectra and cepstrograms are considered. It is established that more than 90% of “cepstral” features have a weak correlation, which allows us to use the naive Bayesian classifier and to obtain highly accurate results of user recognition at the same time. An advantage of the Bayesian classification is the possibility of the robust fast learning of the identification system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. T. H. Akkermans, T. A. Kevenaar, and D. W. Schobben, “Acoustic ear recognition,” in: Advances in Biometrics: Proc. of Intern. Conf., ICB 2006 (Hong Kong, China, Jan 5–7, 2006); LNCS, Springer, Berlin–Heidelberg, Vol. 3832, 697–705 (2006).

  2. B. N. Epifantsev, P. S. Lozhnikov, and A. E. Sulavko, “Algorithm for the identification of hypotheses in the space of uninformative features based on the sequential application of the Bayes formula,” Mezhotraslevaya Informatsionnaya Sluzhba, No. 2, 57–62 (2013).

  3. P. S. Lozhnikov, Hybrid Workflow Biometric Protection [in Russian], Izd-vo SO RAN, Novosibirsk (2017).

    Google Scholar 

  4. V. I. Vasilyev, A. E. Sulavko, R. V. Borisov, and S. S. Zhumazhanova, “Identification of the psychophysiological state of the user based on hidden monitoring in computer systems,” Sci. Tech. Inf. Proc., Vol. 45, Iss. 6, 398–410 (2018). https://doi.org/10.3103/S0147688218060096.

    Article  Google Scholar 

  5. P. T. Tuyls, E. Verbitskiy, T. Ignatenko, D. Schobben, and T. H. Akkermans, “Privacy-protected biometric templates: Acoustic ear identification,” in: Proc. SPIE, Biometric Technology for Human Identification, Vol. 5404, 176–182 (2004). https://doi.org/10.1117/12.541882.

  6. Y. Gao, W. Wang, V. V. Phoha, W. Sun, and Z. Jin, “EarEcho: Using ear canal echo for wearable authentication,” in: Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., Vol. 3, No. 3, Article 81 (2019). https://doi.org/10.1145/3351239.

Download references

Author information

Authors and Affiliations

  1. Omsk State Technical University, Omsk, Russia

    A. E. Sulavko, P. S. Lozhnikov, I. A. Kuprik & A. E. Samotuga

Authors
  1. A. E. Sulavko
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. S. Lozhnikov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. I. A. Kuprik
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. A. E. Samotuga
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. E. Sulavko.

Additional information

This study was performed with the financial support from the Ministry of Science and Higher Education of the Russian Federation (IT Security Grant), Project No. 6.

Translated from Kibernetyka ta Systemnyi Analiz, No. 3, May–June, 2021, pp. 135–143.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sulavko, A.E., Lozhnikov, P.S., Kuprik, I.A. et al. Personal Identification Based on the Individual Sonographic Properties of the Auricle Using Cepstral Analysis and Bayes Formula. Cybern Syst Anal 57, 455–462 (2021). https://doi.org/10.1007/s10559-021-00370-w

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10559-021-00370-w

Keywords

Search

Navigation