Article or book title, author's surname, DOI

Research of the algorithms preprocessing of biometric images for the purposes of verification

D. V. Dmitriev1, S. N. Kapranov2, E. V. Markov3

research article Pages: 52-55

  1. 1 Ph.D. (Eng.), Associate Professor, Nizhny Novgorod State Technical University n.a. R.E. Alekseev
  2. 2 Ph.D. (Eng.), Associate Professor, Nizhny Novgorod State Technical University n.a. R.E. Alekseev.@gmail.ru
  3. 3 Magistrant, Nizhny Novgorod State Technical University n.a. R.E. Alekseev

Abstract

Biometric identification and verification is more often used in problems of access control. In this paper for the purpose of verification as a biometric feature selected geometry of the human face.

For verification system developed module extraction biometric features, which selects the most significant elements of the biometric image and the comparison module biometric samples. As a measure of similarity used similarity coefficient based on a normalized Euclidean distance. To reduce errors comparing the first and second kind (FAR, FRR) investigated the following pre-processing algorithms: rotation, scaling, principal component analysis (PCA).

The simulation showed that the using of different pre-processing combinations allows to reduce the error ratio. The best pre-processing combination: scaling and principal component analysis. Scaling pre-processing is present in all the experiments, the performances of which were better than others. The sequence performed pre-processing virtually no effect on the results.

How to cite

Дмитриев Д.В., Капранов С.Н., Марков Е.В. Исследование алгоритмов предварительной обработки биометрических образов для целей верификации // Нейрокомпьютеры: разработка, применение. 2014. № 3. С. 52–55.

References

  1. http://www.biometrics.ru
  2. https://ibgweb.com/
  3. Gaj V.E. Metod obrabotki biometricheskoj informaczii v sistemax kontrolya i upravleniya dostupom // Trudy' NGTU im. R.E. Alekseeva. 2013. № 1. S. 65-73.
  4. Cootes T., Edwards G., Taylor C. Active appearance models. In Proceedings of the European Conference on Computer Vision. 1998. V. 2. P. 484-498.
  5. Baranov V.G., Milov V.R., Zaripova Ju.X., E'pshtejn A.Ju. Intellektualizacziya sistemy' raspoznavaniya obrazov na osnove sravneniya e'ffektivnosti metodov klassifikaczii // Informaczionno-izmeritel'ny'e i upravlyayushhie sistemy'. 2010. T. 8. № 2. S. 35-39.
  6. Kondrat'ev V.V., Milov V.R. Strukturno-parametricheskij sintez nejrosetevogo klassifikatora // Naukoemkie texnologii. 2003. T. 4. № 2. S. 101-106.
  7. Parondzhanov V.D. Kak uluchshit' rabotu uma: Algoritmy' bez programmistov − e'to ochen' prosto! M.: Delo. 2001.
  8. http://www.milbo.users.sonic.net/stasm
  9. Baranov V.G., Kondrat'ev V.V., Milov V.R., Zaripova Ju.X. Nejrosetevy'e algoritmy' raspoznavaniya obrazov // Nejrokomp'yutery': razrabotka, primenenie. 2007. № 11. S. 20-27.
  10. Turk M., Pentland A. Eigenfaces for Recognition // Journal of Cognitive Neuroscience. 1991. V. 3. № 1. P. 71-86.