Abstract
In recent years, due to the crime spreading and the potential of security threats, a lot of efforts have been spent to develop a reliable surveillance system. Biometric systems have grown in popularity as a trustworthy way to authenticate the identity of individuals. There are several biometrics to identify the persons such as Fingerprint, Face, Iris, Ear and Gait. The iris biometric is considered the best biometrics, due to its speed and accuracy in the identification, and distinctive features along the individual’s lifetime. This paper focuses on issues of the iris segmentation research in the existing techniques. The techniques reported here can be categorized based on image acquisition environment into two scenarios: close-up imaging settings and non-cooperative imaging settings. The theorem behind each strategy is presented along with the outlines of implementation. A review of the issues in the iris segmentation methods coupled with comparison to the relevant issues is included. The weakness of each method is also obtained.
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
Daugman, J.G.: High Confidence Visual Recognition of Persons by a Test of Statistical Independence. IEEE Transactions on Pattern Analysis and Machine Intelligence 15, 1148–1161 (1993)
Daugman, J.G.: High Confidence Recognition of Persons by Iris Patterns. In: The 35th International Carnahan Conference on Security Technology, pp. 254–263. IEEE, Los Alamitos (2001)
Daugman, J.: How Iris Recognition Works. IEEE Transactions on Circuits and Systems for Video Technology 14, 21–30 (2004)
Wildes, R.P.: Iris Recognition: an Emerging Biometric Technology. Proceedings of the IEEE 85, 1348–1363 (1997)
Tisse, C.-L.: Lionel Torres, Robert, M.: Person Identification Technique using Human Iris Recognition. In: the 15th International Conference on Vision Interface (2002)
Ferreira, A., Lourenço, A., Pinto, B., Tendeiro, J.: Tuning Iris Recognition for Noisy Images. In: Fred, A., Filipe, J., Gamboa, H. (eds.) BIOSTEC 2009. Communications in Computer and Information Science, vol. 52, pp. 211–224. Springer, Heidelberg (2010)
Lye Wil, L., Chekima, A., Liau Chung, F., Dargham, J.A.: Iris Recognition using Self-organizing Neural Network. In: Student Conference on Research and Developing Systemsm, SCOReD 2002, pp. 169–172. IEEE, Los Alamitos (2002)
Daugman, J.G.: New Methods in Iris Recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 37, 1167–1175 (2007)
Ritter, N., Owens, R., Cooper, J., Van Saarloos, P.P.: Location of the Pupil-Iris Border in Slit-lamp Images of the Cornea. In: International Conference on Image Analysis and Processing, pp. 740–745 (1999)
Arvacheh, E.M.: Study on Segmentation and Normalization for Iris Recognition. Systems Design Engineering, MSc, p. 81. University of Waterloo, Waterloo (2006)
Liu, X.: Optimizations in Iris Recognition. Computer Science, Phd, p. 130. University of Notre Dame, Notre Dame (2006)
Cui, J.L., Wang, Y.H., Tan, T.N., Ma, L., Sun, Z.N.: a Fast and Robust Iris Localization Method Based on Texture Segmentation. In: Biometric Technology for Human Identification, pp. 401–408 (2004)
Proenca, H., Alexandre, L.A.: Iris Segmentation Methodology for Non-Cooperative Recognition. IEE Proceedings - Vision, Image and Signal Processing 153, 199–205 (2006)
Jeong, D.S., Hwang, J.W., Kang, B.J., Park, K.R., Won, C.S., Park, D.K., Kim, J.: a New Iris Segmentation Method for Non-ideal Iris Images. Image and Vision Computing 28, 254–260 (2010)
Labati, R.D., Scotti, F.: Noisy Iris Segmentation with Boundary Regularization and Reflections Removal. Image and Vision Computing 28, 270–277 (2010)
Li, P.H., Liu, X.M., Xiao, L.J., Song, Q.: Robust and Accurate Iris Segmentation in Very Noisy Iris Images. Image and Vision Computing 28, 246–253 (2010)
Tan, T.N., He, Z.F., Sun, Z.Z.: Efficient and Robust Segmentation of Noisy Iris Images for Non-Cooperative Iris Recognition. Image and Vision Computing 28, 223–230 (2010)
Proenca, H.: Iris Recognition: On the Segmentation of Degraded Images Acquired in the Visible Wavelength. IEEE Transactions on Pattern Analysis and Machine Intelligence 32, 1502–1516 (2010)
Proenca, H.: Towards Non-Cooperative Biometric Iris Recognition. Department of Computer Science, Phd, p. 175. University of Beira Interior, Covilhã (2006)
’Institute of Automation’, Chinese Academy of Sciences’ CASIA Iris Image Database (2004), http://www.sinobiometrics.com
’Indian Institute of Technology Delhi’, IIT Delhi Iris Database version 1.0 (2007), http://web.iitd.ac.in/~biometrics/Database_Iris.htm
’National Institute of Standards and Technology’, Iris Challenge Evaluation Database (2006), http://iris.nist.gov/ICE/
’Multimedia University’, MMU Iris Image Database (2004), http://pesona.mmu.edu.my/~ccteo/
Dobes, M., Machala, L.: UPOL Iris Image Database (2004), http://phoenix.inf.upol.cz/iris/
Proença, H., Alexandre, L.: UBIRIS: A Noisy Iris Image Database (2005), http://iris.di.ubi.pt
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
Radman, A., Jumari, K., Zainal, N. (2011). Iris Segmentation: A Review and Research Issues. In: Mohamad Zain, J., Wan Mohd, W.M.b., El-Qawasmeh, E. (eds) Software Engineering and Computer Systems. ICSECS 2011. Communications in Computer and Information Science, vol 179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22170-5_60
Download citation
DOI: https://doi.org/10.1007/978-3-642-22170-5_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22169-9
Online ISBN: 978-3-642-22170-5
eBook Packages: Computer ScienceComputer Science (R0)