Abstract
The performance of an iris recognition algorithm depends greatly on its classification ability as well as speed. In this paper, an iris recognition algorithm using local extreme points is proposed. It first detects the local extreme points along the angular direction as key points. Then, the sample vector along the angular direction is encoded into a binary feature vector according to the surface trend (gradient) characterized by the local extreme points. Finally, the Hamming distance between two iris patterns is calculated to make a decision. Extensive experimental results show the high performance of the proposed method in terms of accuracy and speed.
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
Jain, A.K., Bolle, R.M., Pankanti, S. (eds.): Biometrics: Personal Identification in a Networked Society. Kluwer, Norwell (1999)
Flom, L., Safir, A.: Iris Recognition System., U.S. Patent, No. 4641394 (1987)
Daugman, J.: High Confidence Visual Recognition of Persons by a Test of Statistical Independence. IEEE Trans. on Pattern Analysis and Machine Intelligence 15(11), 1148–1161 (1993)
Wildes, R., Asmuth, J., et al.: A Machine-vision System for Iris Recognition. Machine Vision and Applications 9, 1–8 (1996)
Boles, W.W., Boashah, B.: A Human Identification Technique Using Images of the Iris and Wavelet Transform. IEEE Trans. on Signal Processing 46, 1185–1188 (1998)
Ma, L., Wang, Y., Tan, T.: Iris Recognition Based on Multichannel Gabor Filters. In: Proc. of the Fifth Asian Conference on Computer Vision, vol. I, pp. 279–283 (2002)
Ma, L., Wang, Y., Tan, T.: Iris Recognition Using Circular Symmetric Filters. In: The Sixteenth International Conference on Pattern Recognition, vol. II, pp. 414–417 (2002)
Ma, L., Tan, T., Wang, Y., Zhang, D.: Personal Identification Based on Iris Texture Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 1519–1533 (2003)
Ma, L., Tan, T., Wang, Y., Zhang, D.: Efficient Iris Recognition by Characterizing Key Local Variations. IEEE Trans. on Image Processing (accepted)
Daugman, J.: Neural Image Processing Strategies Applied in Real-Time pattern Recognition. Real-Time Imaging 3, 157–171 (1997)
Daugman, J.: Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns. International Journal of Computer Vision 45(1), 25–38 (2001)
Cui, J., Ma, L., Wang, Y., Tan, T., Sun, Z.: A Fast and Robust Iris Localization Method based on Texture Segmentation. In: SPIE, Defense and Security Symposium (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cui, J., Wang, Y., Tan, T., Ma, L., Sun, Z. (2004). An Iris Recognition Algorithm Using Local Extreme Points. In: Zhang, D., Jain, A.K. (eds) Biometric Authentication. ICBA 2004. Lecture Notes in Computer Science, vol 3072. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25948-0_61
Download citation
DOI: https://doi.org/10.1007/978-3-540-25948-0_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22146-3
Online ISBN: 978-3-540-25948-0
eBook Packages: Springer Book Archive