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

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.

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Correspondence to A. E. Sulavko.

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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.

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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

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

Keywords

  • cepstrograms
  • windowed Fourier transform
  • Bayes theorem
  • acoustic signal
  • pattern recognition
  • machine learning