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Computer Research and Modeling, 2020, Volume 12, Issue 3, Pages 629–639
DOI: https://doi.org/10.20537/2076-7633-2020-12-3-629-639
(Mi crm806)
 

MODELS IN PHYSICS AND TECHNOLOGY

Mathematical model of the biometric iris recognition system

N. V. Suvorov, M. P. Shleymovich

Kazan National Research Technical University named after A. N. Tupolev — KAI, 10 K. Marksa st., Kazan, 420111, Russia
References:
Abstract: Automatic recognition of personal identity by biometric features is based on unique peculiarities or characteristics of people. Biometric identification process consist in making of reference templates and comparison with new input data. Iris pattern recognition algorithms presents high accuracy and low identification errors percent on practice. Iris pattern advantages over other biometric features are determined by its high degree of freedom (nearly 249), excessive density of unique features and constancy. High recognition reliability level is very important because it provides search in big databases. Unlike one-to-one check mode that is applicable only to small calculation count it allows to work in one-to-many identification mode. Every biometric identification system appears to be probabilistic and qualitative characteristics description utilizes such parameters as: recognition accuracy, false acceptance rate and false rejection rate. These characteristics allows to compare identity recognition methods and asses the system performance under any circumstances. This article explains the mathematical model of iris pattern biometric identification and its characteristics. Besides, there are analyzed results of comparison of model and real recognition process. To make such analysis there was carried out the review of existing iris pattern recognition methods based on different unique features vector. The Python-based software package is described below. It builds-up probabilistic distributions and generates large test data sets. Such data sets can be also used to educate the identification decision making neural network. Furthermore, synergy algorithm of several iris pattern identification methods was suggested to increase qualitative characteristics of system in comparison with the use of each method separately.
Keywords: biometric system, iris recognition, mathematical model, false acceptance rate (FAR), false rejection rate (FRR).
Funding agency Grant number
Ministry of Science and Higher Education of the Russian Federation 2.1724.2017/4.6
This work was supported by the state task No. 2.1724.2017/4.6.
Received: 18.11.2019
Revised: 22.01.2020
Accepted: 12.03.2020
Document Type: Article
UDC: 004.942
Language: English
Citation: N. V. Suvorov, M. P. Shleymovich, “Mathematical model of the biometric iris recognition system”, Computer Research and Modeling, 12:3 (2020), 629–639
Citation in format AMSBIB
\Bibitem{SuvShl20}
\by N.~V.~Suvorov, M.~P.~Shleymovich
\paper Mathematical model of the biometric iris recognition system
\jour Computer Research and Modeling
\yr 2020
\vol 12
\issue 3
\pages 629--639
\mathnet{http://mi.mathnet.ru/crm806}
\crossref{https://doi.org/10.20537/2076-7633-2020-12-3-629-639}
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  • https://www.mathnet.ru/eng/crm/v12/i3/p629
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