№1, 2012

AN ALTERED FINGERPRINT DETECTION METHOD BASED ON FRACTAL CHARACTERISTICS

Yadigar N. Imamverdiyev

The possibility of using concepts of fractal theory to describe the properties of fingerprints is studied. A method for determining the fractal dimensions of fingerprints and on this basis an effective approach for detecting altered fingerprints is proposed. Results of experiments show that the method distinguishes well images of altered fingerprints. The proposed method requires no additional hardware and can be easily integrated into existing fingerprint recognition systems. (pp. 17-26)

Keywords: altered fingerprint detection, fractal, fractal dimension, multifractal spectr, support vector machine
References
  • Feng J., Jain A. K., Ross A. Fingerprint alteration // MSU-CSE-09-30. Dec. 2009.
  • Cummins H. Attempts to Alter and Obliterate Fingerprints // Journal of American Institute of Criminal Law and Criminology, 1935, v.25, pp.982–991.
  • Imamverdiyev Y.N., Kerimova L.E., Musayev V.Y. Method of detection of real fingerprints on the basis of the Radon transform // Automatic Control and Computer Sciences, 2009, v.43, №5, pp.270–275.
  • Алгулиев Р.М., Имамвердиев Я.Н., Мусаев В.Я. Методы обнаружения живучести в биометрических системах // Вопросы защиты информации, 2009, №3 (86), c.16–21.
  • Yoon S., Feng J., Jain A.K. Altered fingerprints: analysis and detection // IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011.
  • Feng J., Jain A.K., Ross A. Detecting Altered Fingerprints // Proc. 20th International Conference on Pattern Recognition, August 23-26, 2010, Istanbul, Turkey, pp. 1622–1625.
  • Petrovici A., Lazar C. Identifying Fingerprint Alteration Using the Reliability Map of the Orientation Field // The Annals of the Univeristy of Craiova, Series Automation, Computers, Electronics and Mechatronics, 2010, v.7(34). №1, pp.45–52.
  • Tabassi E., Wilson C., Watson C. Fingerprint Image Quality, NISTIR 7151, August 2004, http://fingerprint.nist.gov/NFIS/ir 7151.pdf.
  • Singh R., Vatsa M., Bhatt H.S., Bharadwaj S., Noore A., Nooreyezdan S.S. Plastic Surgery: A New Dimension to Face Recognition // IEEE Trans. Information Forensics and Security, 2010, v.5, №3, pp.441–448.
  • Roizenblatt R., Schor P., Dante F., Roizenblatt J., Belfort R. Iris Recognition As a Biometric Method after Cataract Surgery // American Journal of Ophthalmology, 2005, v.140, №5, pp.969–979.
  • Maltoni D., Maio D., Jain A. K., Prabhakar S. Handbook of Fingerprint Recognition (Second Edition). Springer-Verlag. 2009.
  • Павлов А.Н., Анищенко В.С. Мультифрактальный анализ сложных сигналов // Успехи физических наук, 2007, т. 177, №8, c.859–876.
  • Bazen A.M., Gerez S.H. Systematic Methods for the Computation of the Directional Fields and Singular Points of Fingerprints // IEEE Trans. Pattern Analysis and Machine Intelligence, 2002, v.24, №7, pp.905–919.
  • Zhou J., Gu J. A Model-Based Method for the Computation of Fingerprints’ Orientation Field // IEEE Trans. ImageProcessing, 2004, v.13, №6, pp.821–835.
  • Wang Y., Hu J. Global Ridge Orientation Modeling for Partial Fingerprint Identification // IEEE Trans. Pattern Analysis and Machine Intelligence, 2010, v.33, №1, pp.72–87.
  • Mandelbrot B.B. The Fractal Geometry of Nature. San-Francisco: W.H. Freeman and Comp. 1982, 459 p.
  • Mandelbrot B.B. A Multifractal Walk down Wall Street // Scientific American, Feb. 1999, pp.70–73.
  • Кроновер Д. М. Фракталы и хаос в динамических системах. Основы теории. М.: Постмаркет, 2000, 352 с.
  • Федер Е. Фракталы. М.: Мир, 1991, 260 с.
  • Петерс Э. Фрактальный анализ финансовых рынков: применение теории хаоса в инвестициях и экономике. М.: Интернет-трейдинг, 2004, 304 с.
  • Pentland A. Fractal-Based Description of Natural Scenes // IEEE Transactions on Pattern Analysis and Machine Recognition, 1984, v.6, №6, pp.661–674.
  • Polikarpova N. On the Fractal Features in Fingerprint Analysis / Proc. of the 13th Int. Conf. on Pattern Recognition, 1996, v.3, pp. 591–
  • Lin C.-H., Chen J.-L., Gaing Z.-L. Combining Biometric Fractal Pattern and Particle Swarm Optimization-Based Classifier for Fingerprint Recognition // Hindawi Publishing Corporation. Mathematical Problems in Engineering, 2010, v.2010, Article ID 328676. 14 p.
  • Lopes R., Betrouni N. Fractal and multifractal analysis: A review // Medical Image Analysis, 2009, v.13, pp.634–649.
  • Hong L., Wan Y., Jain A. Fingerprint image enhancement: algorithm and performance evaluation // IEEE Trans Pattern Anal Mach Intelligence, 1998, v.20, №8, pp.777–89.
  • Katz M.J. Fractals and the analysis of waveforms // Computers in Biology and Medicine, 1988, v.18, №3, pp.145–156.
  • Higuchi T. Approach to an irregular time series on the basis of the fractal theory // Physica D: Nonlinear Phenomena, 1988, v.31, №2, pp.277–283.
  • Sevcik C. On fractal dimension of waveforms // Chaos, Solitons and Fractals, 2006, v.28, №2, pp.579–580.
  • Esteller R., Vachtsevanos G., Echauz J., Litt B. A Comparison of Waveform Fractal Dimension Algorithms // IEEE Trans. Circuits Syst.‒I: Fundam. Theory Appl. 2001, v.48, №2, pp.177–183.
  • Struzik Z.R. Determining local singularity strengths and their spectra with the wavelet transform // Fractals. 2000, v.82, pp.163–179.
  • Дремин И.М., Иванов О.В., Нечитайло В.А. Вейвлеты и их использование // Успехи физических наук, 2001, .т.171, №5, c.465–501.
  • Xu Y., Ji H., Fermuller C. Viewpoint Invariant Texture Description Using Fractal Analysis // Int. Journal of Computer Vision, 2009, v.83, №1, pp.85–100.
  • Wendt H., Abry P., Jaffard S., Ji H., Shen Z. Wavelet Leader Multifractal Analysis for Texture Classification / Proc. of the 16th IEEE Int. Conference on Image Processing (ICIP), 2009, pp.3785–3788.
  • Chen Y.W., Lin C.J. Combining SVMs with various feature selection strategies //Studies in Fuzziness and Soft Computing, 2006, v.207, pp.315–324.
  • FracLab 2.0. A fractal analysis toolbox for signal and image processing. http://fraclab.saclay.inria.fr/
  • Moisy F. Computing a fractal dimension with Matlab: 1D, 2D and 3D Box-counting. http://www.mathworks.com/matlabcentral/fileexchange/13063-boxcount/content/boxcount/html/demo.html
  • Chang C.-C., and Lin C.-J., LIBSVM: a library for support vector machines, 2001. http://www.csie.ntu.edu.tw/_cjlin/libsvm.
  • Fawcett T. An introduction to ROC analysis // Pattern Recognition Letters, 2006, v.27, №8, pp.861–874.