SciELO - Scientific Electronic Library Online

vol.18 issue1Effects of Interpolation on Segmentation in Cell ImagingSpeech Enhancement with Local Adaptive Rank-Order Filtering author indexsubject indexsearch form
Home Pagealphabetic serial listing  

Services on Demand




Related links

  • Have no similar articlesSimilars in SciELO


Computación y Sistemas

Print version ISSN 1405-5546

Comp. y Sist. vol.18 n.1 México Jan./Mar. 2014 



Functional Data Analysis as an Alternative for the Automatic Biometric Image Recognition: Iris Application


El análisis de datos funcionales como alternativa para el reconocimiento automático de imágenes biométricas: aplicación en el iris


Dania P.-Muñoz, Francisco José Silva Mata, Noslen Hernández, and Isneri Talavera Bustamante


Centro de Aplicaciones de Tecnologías de Avanzada, CENATAV, Cuba.,,,



Functional data analysis has been a novel option for representing images, since the continuous nature of images is preserved. Image representation using functional data provides significant advantages, being the appreciable reduction of the dimensionality one of the most significant. This paper gives a detailed description of the entire imaging process using the proposed approach. As an example, the representation of iris images through functional data for recognition tasks was used. The paper presents experiments and results of applying this approach to the recognition of iris images, demonstrating its effectiveness.

Keywords: Functional data analysis, Zernike bases, biometric image recognition.



El análisis de datos funcionales ha sido una opción novedosa para la representación de imágenes, ya que su naturaleza continua se conserva. La representación de imágenes usando datos funcionales proporciona muchas ventajas, donde una de las más significativas es la reducción apreciable de la dimensión de los datos. En este trabajo se propone una descripción detallada de todo el proceso de representación de imágenes mediante el enfoque propuesto. Además, la representación de las imágenes del iris por medio de los datos funcionales para tareas de reconocimiento, se utilizó como un ejemplo. El artículo presenta algunos experimentos y resultados de la aplicación de este enfoque para el reconocimiento de imágenes del iris, lo que demuestra la eficacia de la misma.

Palabras clave: Análisis de datos funcionales, bases de Zernike, reconocimiento de imágenes biométricas.





1. Wasserman, L. (2004). All of Statistics . A concise course in statistical inference. New York: Springer.         [ Links ]

2. Ramsay, J.O. & Silverman, B.W. (2005). Functional Data Analysis (2nd ed.). New York: Springer.         [ Links ]

3. Boomgaard, R.V.D. & Dorst, L. (2005). Machine vision. An introduction for computer scientists. University of Amsterdam, Computer Science Department.         [ Links ]

4. Ramsay, J.O. & Dalzell, C.J. (1991). Some tools for functional data analysis. Journal of the Royal Statistical Society, 53(3), 539-572.         [ Links ]

5. Locantore, N., Marron, J.S., Simpson, D.G., Tripolo, N., Zhang, J.T., & Cohen, K.L. (1999). Robust principal component analysis for functional data. Sociedad de Estadística e investigación Operativa, 8(1), 1-73.         [ Links ]

6. Ross, A. (2010). Iris Recognition: The Path Forward. Computer, 43(2), 30-35.         [ Links ]

7. Daugman, J. & Downing, C. (2001). Epigenetic Randomness, Complexity,and Singularity of Human Iris Patterns. Proceedings of The Royal Society B: Biological Sciences, 268(1477), 1737-1740.         [ Links ]

8. Jan, F., Usman, I., & Agha, S. (2012). Iris localization in frontal eye images for less constrained iris recognition systems. Digital Signal Processing, 22(6), 971-986.         [ Links ]

9. Tuama, A.S. (2012). Iris image segmentation and recognition. International Journal of Computer Science & Emerging Technologies, 3(2), 60-65.         [ Links ]

10. Jain, A.K., Flynn, P., & Ross, A.A. (2008). Handbook of Biometrics. New York: Springer.         [ Links ]

11. Daugman, J. (2004). How iris recognition works. IEEE Transactions on Circuits and Systems for Video Technology, 14(1), 21-30.         [ Links ]

12. Silva, F., Garea, E., Álvarez, E.M., & Gil, J.L. (2006). A fast adaboosting based method for iris and pupil contour detection. 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications (CIARP'06), Cancun, Mexico, 127-136.         [ Links ]

13. Ferraty, F. & Vieu, P. (2006). Nonparametric Functional Data Analysis: Theory and Practice. New York: Springer.         [ Links ]

14. Ramsay, J.O. & Silverman, B.W. (2002). Applied Functional Data Analysis. New York: Springer.         [ Links ]

15. Ramsay, J.O. & Silverman, B.W. (1997). Functional Data Analysis. New York: Springer.         [ Links ]

16. Berggren, L. (1985). Iridology: A critical review. Acta Ophthalmologica, 63(1), 1-8.         [ Links ]

17. Yang, J.W., Woo, D.H., Kim, D.H., & Yi, J. (2008). The Improvement of the Data Overlapping Phenomenon with Memory Accessing Mode. Journal of information Display, 9(1), 6-13.         [ Links ]

18. Wang, Q., Ronneberger, O., & Burkhardt, H. (2008). Fourier Analysis in Polar and Spherical Coordinates (Internal Repor 1/08), Austria: Albert Ludwigs University Freiburg.         [ Links ]

19. Boyd, J.P. (2001). Chebyshev and Fourier Spectral Methods (2nd ed., rev.). Mineola, N.Y.: Dover Publications.         [ Links ]

20. Boyd, J.P. & Yu, F. (2011). Comparing seven spectral methods for interpolation and for solving the poisson equation in a disk: Zernike polynomials, logan-shepp ridge polynomials, chebyshev-fourier series, cylindrical robert functions, bessel-fourier expansions, square-to-disk conformal mapping and radial basis functions. Journal of Computational Physics, 230(4), 1408-1438.         [ Links ]

21. McAlinden, C., McCartney, M., & Moore, J. (2011) . Mathematics of zernike polynomials: a review. Clinical & Experimental Ophthalmology, 39(8), 820-827.         [ Links ]

22. Goodwin, E.P. & Wyant, J.C. (2006). Field Guide to Interferometric Optical Testing. Bellingham, Wash: SPIE Press.         [ Links ]

23. Iskander, D.R., Collins, M.J., & Davis, B. (2001). Optimal Modeling of Corneal Surfaces with Zernike Polynomials. IEEE Transactions on biomedical engineering, 48(1), 87-95.         [ Links ]

24. Benko, M. (2004). Functional Principal Components Analysis, Implementation and Applications. M.Sc. Thesis, Humboldt University, Berlin, Germany.         [ Links ]

25. Masek, L. & Kovesi, P. (2003). MATLAB Source Code for a Biometric Identification System Based on Iris Patterns. The School of Computer Science and Software Engineering, The University of Western Australia.         [ Links ]

26. Martínez, A.M. & Kak, A.C. (2001). PCA versus LDA. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(2), 228-233.         [ Links ]

27. Birgale, L. & Kokare, M. (2012). Iris Recognition Using Ridgelets. Journal of Information Processing Systems, 8(3), 445-458.         [ Links ]

28. García-Vázquez, M.S. & Ramírez-Acosta, A.A. (2012) . Avances en el Reconocimiento del Iris: Perspectivas y Oportunidades en la Investigación de Algoritmos Biométricos. Computación y sistemas, 16(3), 267-276.         [ Links ]

Creative Commons License All the contents of this journal, except where otherwise noted, is licensed under a Creative Commons Attribution License