Abstract
This work deals with dynamic iris biometry using video, which is increasingly gaining interest for its flexibility in the framework of biometric portals. We propose several improvements for “real-time” dynamic iris biometry in order to build gradually an iris code of high quality by selecting on-the-fly the best iris images as they appear during acquisition. In particular, tracking is performed using an optimally-tuned Kalman’s filter, i.e. a Kalman’s filter with state and observation matrices specifically learned to follow the movement of a pupil. Experiments on four videos acquired with an IR-sensitive low-cost webcam show reduced computation time with a slight but significant gain in accuracy when compared to the classical Kalman tracker.
The second main contribution is to combine iris codes of images within the video stream providing the “best quality” iris texture. The so-obtained fuzzy iris codes clearly exhibit areas with high confidence and areas with low one due to eyelashes and eyelids. Hence, these areas involve an imprecision in detecting iris and pupil. Such uncertainty can be further exploited for identification.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Benletaief, N., Benazza-Benyahia, A., Derrode, S.: Pupil localization and tracking for video-based iris biometrics. In: Proc. of the 10th Int. Conf. on Information Sciences, Signal Processing and their Applications (ISSPA 2010), Kuala Lumpur, Malaysia, pp. 650–653 (2010)
Besag, J.: On the statistical analysis of dirty pictures (with discussion). J. of Royal Statistical Society 48(3), 259–302 (1986)
Daugman, J.: How iris recognition works. IEEE Trans. on Circuits and Systems for Video Technology 14, 21–30 (2004)
Gander, W., Golub, G.H., Strebel, R.: Least-squares fitting of circles and ellipses. BIT Numerical Mathematics 34, 558–578 (1994)
Haddouche, A., Adel, M., Rasigni, M., Conrath, J., Bourennane, S.: Detection of the foveal avascular zone on retinal angiograms using Markov random fields. Digital Signal Processing 20, 149–154 (2010)
Kang, B.J., Park, K.R.: A Study on Iris Image Restoration. In: Kanade, T., Jain, A., Ratha, N.K. (eds.) AVBPA 2005. LNCS, vol. 3546, pp. 31–40. Springer, Heidelberg (2005)
Ketchantang, W., Derrode, S., Bourennane, S., Martin, L.: Video Pupil Tracking for Iris Based Identification. In: Blanc-Talon, J., Philips, W., Popescu, D.C., Scheunders, P. (eds.) ACIVS 2005. LNCS, vol. 3708, pp. 1–8. Springer, Heidelberg (2005)
Kien, N., Fookes, C., Sridharan, S., Denman, S.: Quality-driven super-resolution for less constrained iris recognition at a distance and on the move. IEEE Trans. on Information Forensics and Security 6, 1248–1258 (2011)
Lee, Y., Phillips, P.J., Micheals, R.J.: An automated video-based system for iris recognition. In: Proc. of the 3rd IAPR-IEEE Int. Conf. on Biometrics, Sassari, Italy (2009)
Li, P., Liu, X., Xiao, L., Song, Q.: Robust and accurate iris segmentation in very noisy iris images. Image and Vision Computing 28, 246–253 (2010)
Shumway, R.H., Stoffer, D.S.: An approach to time series smoothing and forecasting using the EM algorithm. J. of Time Series Analysis 3(4), 253–264 (1982)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Némesin, V., Derrode, S., Benazza-Benyahia, A. (2012). Gradual Iris Code Construction from Close-Up Eye Video. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P., Zemčík, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2012. Lecture Notes in Computer Science, vol 7517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33140-4_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-33140-4_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33139-8
Online ISBN: 978-3-642-33140-4
eBook Packages: Computer ScienceComputer Science (R0)