Abstract
Currently, iris identification systems are not easy to use since they need a strict cooperation of the user during the snapshot acquisition process. Several acquisitions are generally needed to obtain a workable image of the iris for recognition purpose. To make the system more flexible and open to large public applications, we propose to work on the entire sequence acquired by a camera during the enrolment. Hence the recognition step can be applied on a selected number of the “best workable images” of the iris within the sequence. In this context, the aim of the paper is to present a method for pupil tracking based on a dynamic Gaussian Mixture Model (GMM) together with Kalman prediction of the pupil position along the sequence. The method has been experimented on a real video sequence captured by a near Infra-Red (IR) sensitive camera and has shown its effectiveness in nearly real time computing.
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
Ross, A., Jain, A.K.: Information fusion in biometrics. Pattern Recognition Letters 24, 2115–2125 (2003)
Daugman, J.: High confidence visual recognition of persons by a test of statistical independence. IEEE trans. on PAMI 15, 1148–1161 (1993)
Tisse, C.L., Martin, L., Torres, L., Robert, M.: Person identification technique using human iris recognition. In: 15th Int. Conf. on Vision Interface, Calgary, CA, pp. 294–299 (2002)
Ma, L., Tan, T., Wang, Y., Zhang, D.: Efficient iris recognition by characterizing key local variations. IEEE trans. on Image Processing 13, 739–750 (2004)
Sirohey, S., Rosenfeld, A., Duric, Z.: A method of detecting and traking irises and eyelids in video. Pattern Recognition 35, 389–401 (2002)
Zhu, Z., Ji, Q.: Robust real-time eye detection and tracking under variable lighting conditions and various face orientations. Computer Vision and Image Understanding 38, 124–154 (2005)
Zhu, Y., Fujimura, K.: Driver face tracking using Gaussian mixture model. In: Proc. of the IEEE Intelligent Vehicles Symp., pp. 587–592 (2003)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. of the Royal Statistical Society 39, 1–38 (1977)
Bilmes, J.A.: A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models. report tr-97-021, International Computer Science Institute, Berkeley, CA, USA (1997)
McKenna, S., Gong, S., Raja, Y.: Modelling facial colour and identity with Gaussian mixtures. Pattern Recognition 31, 1883–1892 (1998)
Yang, J., Lu, W., Waibel, A.: Skin color modeling and adaptation. In: Chin, R., Pong, T.-C. (eds.) ACCV 1998. LNCS, vol. 1352, pp. 687–694. Springer, Heidelberg (1997)
Minkler, G., Minkler, J.: Theory and Application of Kalman filtering. Magellan Book Company, Palm Bay (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ketchantang, W., Derrode, S., Bourennane, S., Martin, L. (2005). Video Pupil Tracking for Iris Based Identification. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2005. Lecture Notes in Computer Science, vol 3708. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11558484_1
Download citation
DOI: https://doi.org/10.1007/11558484_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29032-2
Online ISBN: 978-3-540-32046-3
eBook Packages: Computer ScienceComputer Science (R0)