Abstract
During recent years, iris recognition has been accepted by public as a high-discriminative personal identification technology duo to its unique, stable and noninvasiveness properties. However, eyelash occlusion degrades iris equality and leads low recognition rate of nonideal iris. In this paper we propose a novel eyelash detection method based on expectation maximization (EM) algorithm and Gaussian Mixture Model (GMM). This method is robust, and does not rely on the fixed threshold and the detection of eyelid. Experimental results depict this method can detect eyelashes accurately and effectively.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Daugman, J.: How iris recognition works. IEEE Transactions on Circuits and Systems for Video Technology 14(1), 21–30 (2004)
Wildes, R.P.: Iris recognition: an emerging biometric technology. Proceedings of the IEEE 85(9), 1348–1363 (1997)
Kong, W.K., Zhang, D.: Accurate iris segmentation based on novel reflection and eyelash detection model. In: Proceedings of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing (2001)
Huang, J.: A New Iris Segmentation Method for Recognition. In: 17th International Conference on Pattern Recognition (ICPR 2004), vol. 3, pp. 554–557 (2004)
Kang, B.J., Park, K.R.: A robust eyelash detection based on iris focus assessment. Pattern Recognition Letters 28(13), 1630–1639 (2007)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society. Series B (Methodological) 39(1), 1–38 (1977)
Tran, D., Wagner, H.: Fuzzy expectation-maximisation algorithm for speech and speaker recognition. In: 18th International Conference of the North American Fuzzy Information Processing Society, NAFIPS (1999)
Zhu, Y., Fujimura, K.: Driver face tracking using Gaussian mixture model(GMM). In: Proceedings of Intelligent Vehicles Symposium. IEEE (2003)
Bilmes, J.A.: A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models, pp. 2–7 (1998)
Institute of Automation, Chinese Academy of Science, http://www.sinobiometrics.com/english/IrisDatabase.asp.2006
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, T., Han, M., Wan, H., Yin, Y. (2011). A Robust and Fast Eyelash Detection Basted on Expectation Maximization and Gaussian Mixture Model. In: Yang, D. (eds) Informatics in Control, Automation and Robotics. Lecture Notes in Electrical Engineering, vol 133. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25992-0_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-25992-0_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25991-3
Online ISBN: 978-3-642-25992-0
eBook Packages: EngineeringEngineering (R0)