Robust and Accurate Iris Segmentation Algorithm for Color and Noisy Eye Images

• Autorzy: Strzelczyk P.
• Języki publikacji: EN

Abstrakty

EN

Efficient and robust segmentation of iris images captured in the uncontrolled environments is one of the challenges of non-cooperative iris recognition systems. We address this problem by proposing a novel iris segmentation algorithm, which is suitable both for monochrome and color eye images. The method presented use modified Hough transform to roughly localize the possible iris and pupil boundaries, approximating them by circles. A voting mechanisms is applied to select a candidate iris regions. The detailed iris boundary is approximated by the spline curve. Its shape is determined by minimizing introduced boundary energy function. The described algorithm was submitted to the NICE.I iris image segmentation contest, when it was ranked 11th and 10th out of total 97.

Słowa kluczowe

EN

biometrics; image segmentation; iris recognition

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2010
• Tom: nr 4
• Strony: 5--9
• Opis fizyczny: Bibliogr. 12 poz., rys.

Twórcy

autor

Strzelczyk P.

• Research and Academic Computer Network (NASK), Wąwozowa st 18, 02-796 Warsaw, Poland,

Bibliografia

• [1] H. Proenca and L. A. Alexandre, “The NICE.I: noisy iris challenge evaluation, part I”, in Proc. Int. Conf. Biometr. Theory, Appl. Sys., Washington, USA, 2007, pp. 1–4.
• [2] H. Proenca and L. A. Alexandre, “UBIRIS: a noisy iris image database”, Lect. Notes Comput. Sci., vol. 3617, pp. 970–977, 2005[Online]. Available: http://iris.di.ubi.pt
• [3] T. Tan, Z. He, and Z. Sun, “Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition”, Image Vision Comput., vol. 28, pp. 223–230, 2010.
• [4] M. A. Luengo-Oroz, E. Faure, and J. Angulo, “Robust iris segmentation on uncalibrated noisy images using mathematical morphology”, Image Vision Comput., vol. 28, pp. 278–284, 2010.
• [5] Y. Chen, M. Adjouadi, C. Han, J. Wang, A. Barreto, N. Rishe, and J. Andrian, “A highly accurate and computationally efficient approach for unconstrained iris segmentation”, Image and Vision Comput., vol. 28, pp. 261–269, 2010.
• [6] W. Sankowski, K. Grabowski, M. Napieralska, M. Zubert, and Andrzej Napieralski, “Reliable algorithm for iris segmentation in eye image”, Image Vision Comput., vol. 28, pp. 231–237, 2010.
• [7] P. de Almeida, “A knowledge-based approach to the iris segmentation problem”, Image Vision Comput., vol. 28, pp. 238–245, 2010.
• [8] P. Li, X. Liu, L. Xiao, and Q. Song, “Robust and accurate iris segmentation in very noisy iris images”, Image Vision Comput., vol. 28, pp. 246–253, 2010.
• [9] D. Sik Jeong, J. Won Hwang, B. Jun Kang, K. Ryoung Park, C. Sun Wonc, D.-K. Park, and J. Kim, “A new iris segmentation method for non-ideal iris images”, Image Vision Comput., vol. 28, pp. 254–260, 2010.
• [10] R. D. Labati and F. Scotti, “Noisy iris segmentation with boundary regularization and reflections removal”, Image Vision Comput., vol. 28, pp. 270–277, 2010.
• [11] J. Daugman, “New methods in iris recognition”, IEEE Trans. Sys. Man Cyb., Part B, Cybernetics, vol. 37, no. 5, pp. 1167–1175, 2007.
• [12] J. Daugman, “How iris recognition works?”, IEEE Trans. Circ. Sys. Video Technol., vol. 14, no. 1, pp. 21–30, 2004.