Use of biometric thermal factors for identification in access systems

Authors

  • Олексій Олексійович Фразе-Фразенко Odessa National Economic University 8, Preobrazhenskaya str., Оdessa, 65082 Ukraine, Ukraine

DOI:

https://doi.org/10.15587/2312-8372.2013.12178

Keywords:

identification, authentication, thermogram, access, FAR, FRR

Abstract

The article considers the preconditions for use of the biometric identification and authentication methods based on the features of a human thermogram. The article presents the method of solution of the problem of recognition of a human face contour thermogram, which can improve the quality of functioning of the information security system and provide high factors of FAR and FRR. The method consists in the fact that initially for all image pixels the squared norm of gradient of change of their brightness was calculated. Then on a new black and white monochrome matrix by black colour on a white background all elements are set off, whose values of the norm of the gradient are over a threshold value. As outlines of objects in a monochrome matrix, we take coherent configurations of elements in black. Further, the coefficient is determined and the threshold value of squared norm of gradient is calculated. It is necessary to take into account that the values are higher than the overall average levels of non-zero changes in rows and columns, respectively, and among the connected configurations of elements in black on the monochrome matrix, the configurations, in which the number of input elements is less than a certain value, are rejected. For remaining configurations the average degree of neighborhood is calculated, i.e. the result of division of the sum of all elements of the configuration of neighboring elements into the sum of the elements in the configuration. At the same time, the configurations in which the average degree of neighborhood is less than three are rejected, and the remaining ones are accepted as the desired object boundaries.

Author Biography

Олексій Олексійович Фразе-Фразенко, Odessa National Economic University 8, Preobrazhenskaya str., Оdessa, 65082 Ukraine

Deputy Head of the Center for Information Technology

References

  1. Болл, P. M. Руководство по биометрии [Текст]: монография / Р. М. Болл. – М. : Техносфера, 2007. – 368 с.
  2. Лыcак, A. Б. Идентификация и аутентификация личности [Текст] / A. Б. Лыcак // Математические структуры и моделирование. – Омск : ОмГУ. – 2012. – №26. – С. 124-134.
  3. Скопа, О. О. Аналіз розвитку сучасних напрямів інформаційної безпеки автоматизованих систем [Текст] / О. О. Скопа, Н. Ф. Казакова // Системи обробки інформації. – Харків: Харківський ун т Повітряних Сил ім.І.Кожедуба. – 2009. –№7(79): Безпека та захист інформації в інформаційних системах. – С. 48-54.
  4. Градиентный способ выделения контуров объектов на матрице полутонового растрового изображения [Текст] : пат. 2325044 Росія : МПК H04N1/409 (2006.01), G06K9/46 (2006.01) / Гданський М. І. (RU), Марченко Ю. А. (RU) ; заявник та патентообладач Московський державний університет інженерної екології (RU) ; заявл. 21.02.2007 ; дата опублікування невідома.
  5. Павлидис, Т. Алгоритмы машинной графики и обработка изображений [Текст] : монографія. – М. : Радио и связь, 1986. – 86 с.
  6. Андреев, А. Л. Автоматизированные телевизионные системы наблюдения. Арифметико-логические основы и алгоритмы [Текст] : навч. посібник. – СПб. : СПбГУИТМО, 2005. – 138 с.
  7. Danielyan, E. The Lures of Biometrics [Текст] / Е. Danielyan // The Internet Protocol Journal. – 2004. – Том 7. – №1. – С. 15-35.
  8. Monrose, F. Keystroke dynamics as a biometric for authentication : [Текст] / F. Monrose, А. Rubin // Future Generation Computer Systems. – 2000. – №16. – С. 351-359.
  9. Chellappa, R. Human and machine recognition of human face images : [Текст] / R. Chellappa, C. L. Wilson, S. Sirohey // Proceeding of the IEEE. – 1995. – №83. – С. 705-741.
  10. Borkar, M. User identication systems leverage smarter biometrics technologies : керівний документ / White paper : Texas Instruments, 2012. – 6 с.
  11. Boll, P. M. (2007). Guide to Biometrics. Moscow : Technosphere, 2007, 368.
  12. Lysak, A. B. (2012). Identification and authentication of the person. Mathematical structures and modeling, iss. 26, p.p. 124-134.
  13. Skopa, O. O., Kazakova, N. F. (2009). Analysis of the modern trends of information security of automated systems. Systems for processing information, iss. 7 (79) : Safety and security of information in information systems, p.p. 48-54.
  14. Gdans‘kyy, M.I., Marchenko, Y. A. (2006). Gradient method of objects is selected circuits on matrix polutonovoho raster image. Patent 2325044, Russia.
  15. Pavlydys, T. (1986). Mashynnoy graphics algorithms and Processing depicted, Moscow : Radio and Communications, 86.
  16. Andreev, A. (2005). Automated TV-monitoring system. Arithmetic and logical framework and algorithms. St. Petersburg : SPbGUITMO, 138.
  17. Danielyan, E. (2004). The Lures of Biometrics. The Internet Protocol Journal, Vol. 7, Iss. 1, p.p. 15-35.
  18. Monrose, F., Rubin, A. (2000). Keystroke dynamics as a biometric for authentication. Future Generation Computer Systems, 16, p.p. 351-359.
  19. Chellappa, R., Wilson, C. L., Sirohey, S. (1995). Human and machine recognition of human face images. Proceeding of the IEEE, Vol. 83, p.p. 705-741.
  20. Borkar, M. (2012). User identication systems leverage smarter biometrics technologies. White paper : Texas Instruments, 6.

Published

2013-02-27

How to Cite

Фразе-Фразенко, О. О. (2013). Use of biometric thermal factors for identification in access systems. Technology Audit and Production Reserves, 1(1(9), 33–36. https://doi.org/10.15587/2312-8372.2013.12178