Personal identification based on acoustic characteristics of outer ear using cepstral analysis, Bayesian classifier, and artificial neural networks

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).