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Personal identification based on the internal structure of the ear

The widespread transition to the information society is accompanied by an exacerbation of information security problems. The most important problem is the protection of personal and biometric data from compromise.

THERE IS A STRUGGLE FOR THE RELIABILITY OF BIOMETRIC SYSTEMS

The most important indicator for a biometric system is resistance to counterfeiting (digital or physical “dummies” of biometric images). Many standards have been introduced around the world to protect biometric templates from compromise when storing and transmitting their digital copies over communication channels - GOST R 52633 series, ISO/IEC 19792:2009, ISO/IEC 24761:2009, ISO/IEC 24745:2011 standards), as well as protecting biometric scanners from presentation attacks (spoofing) aimed at “deceiving” sensors (ISO/IEC 30107 series).

Open biometric images (fingerprint, iris, face, voice, and autograph) are “in plain sight” and therefore are compromised in the natural environment, even if all requirements for their protection are met. An attacker can remove biometric characteristics without contact or hidden from the owner (for example, from a door handle or photograph).

The project is devoted to the development of a method and technology for biometric identification and authentication using data on the internal structure of the outer ear obtained using echography. The individual characteristics of the subjects' ear canals are hidden from direct observation and cannot be copied by photographing. A “flat” image of the ear is not informative enough to make a “dummy”.

the essence of the project
The auricle and auditory canal are resonant systems. To obtain information about the internal structure of the outer ear, you can influence the ear canal with acoustic waves, which will be distorted by reflecting from the walls of the canal. The reflected signal will have differences from the original one due to the individual characteristics of the auricle and canal of a person. The parameters of the echo signal or its transfer function contain information about the geometry of the auditory canal and the auricle, so they can be perceived as a vector of biometric parameters (features) characterizing the structural features of the individual’s outer ear. A device has been developed for recording the biometric characteristics of the ear, which are based on the principle of echography, as well as software modules for recognizing subjects using an echogram of the ear.
The average spectra of the echo signal are very informative. Their further cepstral and correlation analysis allows us to identify key features by which a person’s personality can be identified with high accuracy.
Key publications
on the topic "Identification and verification of person based on structural features of the ear"
Personal Identification Based on Acoustic Characteristics of the Outer Ear Using Cepstral Analysis, Bayesian Classifier and Artificial Neural Networks
cepstrograms, windowed Fourier transform, Bayes formula, multilayer neural networks, acoustic signal, pattern identification, machine learning, echograms, biometrics, information security, ear structure
https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/bme2.12037
Sulavko, A. E., Samotuga A.E., Kuprik I.A. Personal Identification Based on Acoustic Characteristics of the Outer Ear Using Cepstral Analysis, Bayesian Classifier and Artificial Neural Networks // IET Biometrics. - 2021. - p. 1-14 (early view)

A hypothesis is discussed concerning the use of echograms of the external auditory canal for personal identification. The authors have developed a device for measuring the acoustic parameters of the external auditory canal. Obtained echograms can be used as biometric patterns for identification and authentication of subjects. Two types of biometric parameters are considered based on spectral and cepstral analyses of echograms. The authors used two approaches for recognizing ear patterns: the first was based on Bayes' formula and the second on artificial neural networks (convolutional and fully connected). The Bayesian classifier has been found to show a lower percentage of identification errors with an equal error rate (EER) = 0.0053. The best result for neural networks was EER = 0.0266. An experiment the authors repeated with the same subjects six months after the initial data collection showed insignificant deviation in the number of wrong decisions (EER = 0.008).
Personal Identification Based on the Individual Sonographic Properties of the Auricle Using Cepstral Analysis and Bayes Formula
cepstrograms, windowed Fourier transform, Bayes' theorem, acoustic signal, pattern recognition, machine learning
https://link.springer.com/article/10.1007/s10559-021-00370-w#citeas
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
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.
Methods of personality recognition based on analysis of the characteristics of the outer ear (Review)
authentication, biometrics, personal identification, pinna detection in an image, feature extraction, pattern recognition
view the work on researchgate.net

Garipov I.M., Sulavko A.E., Kuprik I.A. Methods of personality recognition based on analysis of the characteristics of the outer ear (Review) // Issues of information protection. – 2020. - №. 1. – pp. 33-41

The article describes approaches to extracting biometric parameters of the ear in two-dimensional and three-dimensional images, and the basis of measurements of the transfer functions of the ear canal. The methods used for pattern recognition for the construction of means of biometric identification and authentication according to the parameters of the auricle are considered. The main research results in this area are presented.
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