Abstract
In this paper, a novel and robust verification approach using iris features is presented. Contrasting with conventional approaches, only two iris sub-regions instead of entire iris, where are nearly not occluded by useless parts such as eyelash and eyelid, are segmented for verification. Gabor filtering and wavelet moments methods are used to extract the iris texture features. In the verification stage, the principal component analysis (PCA) technique and one-class-one-network (Back-Propagation Neural Network (BPNN)) classification structure are employed for dimensionality reduction and classification, respectively. The experimental results show that the correct verification rate can reach 98.65% using our proposed approach.
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
Daugman, J.: High Confidence Visual Recognition of Persons by a Test of Statistical Independence. IEEE Transactions on Pattern Analysis and Machine Intelligence 15, 1148–1161 (1993)
Boles, W., Boashansh, B.: A human Identification Technique Using Images of the Iris and Wavelet Transform. IEEE Trans. Signal Processing 46(4), 1185–1188 (1998)
Ma, L., Tan, T., Wang, Y., Zhang, D.: Local intensity variation analysis for iris recognition. Pattern Recognition 37, 1287–1298 (2004)
Ganeshan, B., Theckedath, D., Young, R., Chatwin, C.: Boimetric iris recognition system using a fast and robust iris location and alignment procedure. Optics and Lasers in Engineering 44, 1–24 (2006)
Son, B., Won, H., Kee, G., Lee, Y.: DISCTIMINANT IRIS FEATURE AND SUPPORT VECTOR MACHINES FOR IRIS RECOGNITION. ICIP, 865–868 (2004)
Kong, W.K., Zhang, D., Li, W.: Palmprint feature extraction using 2-D Gabor filters. Pattern Recognition 36, 2339–2347 (2003)
Sanchez-Avila, C., Sanchez-Reillo, R.: Two different approaches for iris recognition using Gabor filters and multiscale zero-crossing representation. Pattern Recognition 38, 231–240 (2005)
Zhang, J., Tan, T.: LiMa: Invariant Texture Segmentation Via Circular Gabor Filters
Pan, H., Xia, L.Z.: Exact and fast algorithm for two-dimensional image wavelet moments via the projection transform. Pattern Recognition 38, 395–402 (2005)
Kumar, A., Zhang, D.: Personal authentication using multiple palmprint representation. Pattern Recognition 38, 1695–1704 (2005)
Daugman, J.G.: Biometric Personal Identification System Based on Iris Analysis, U.S. Patent No. 5, 291, 560 (1994)
Ma, L., Tan, T., Wang, Y., Zhang, D.: Personal identification Based on Iris Texture Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 1519–1533 (2003)
Ma, L., Wang, Y., Tan, T.: Iris recognition using circular symmetric filters. In: Proceedings of the 16th International Conference on Pattern Recognition, pp. 414–417 (2002)
Dobes, M., Machala, L., Tichavsky, P., Pospisil, J.: Human eye iris recognition using the mutual information. International Journal for Light and Electron Optics 9, 399–404 (2004)
Liam, L.W., Chekima, A., Fan, L.Ch., Dargham, J.A.: Iris recognition suing self-organizing neural network. In: Proc. Student Conference on Research and Development, Shah Alam, Malaysia, pp.169–172 (2002)
Sanchez-Avila, C., Sanchez-Reillo, R., Martin-Roche, D.: Iris-based biometric recognition using dyadic wavelet transform. IEEE Aerosp. and Electron. Syst. Mag. 17(10), 3–6 (2002)
Ma, L., Wang, Y., Tan, T.: Iris recognition based on multichannel gabor filtering. In: Proceedings of the Fifth Asian Conference on Computer Vision, pp. 279–283 (2002)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ma, Z., Qi, M., Kang, H., Wang, S., Kong, J. (2007). Iris Verification Using Wavelet Moments and Neural Network. In: Li, K., Li, X., Irwin, G.W., He, G. (eds) Life System Modeling and Simulation. LSMS 2007. Lecture Notes in Computer Science(), vol 4689. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74771-0_25
Download citation
DOI: https://doi.org/10.1007/978-3-540-74771-0_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74770-3
Online ISBN: 978-3-540-74771-0
eBook Packages: Computer ScienceComputer Science (R0)