The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Efficient Iris Recognition by Characterizing Key Local Variations
Abstract
Unlike other biometrics such as fingerprints and face, the distinct aspect of iris comes from randomly distributed features. This leads to its high reliability for personal identification, and at the same time, the difficulty in effectively representing such details in an image. This paper describes an efficient algorithm for iris recognition by characterizing key local variations. The basic idea is that local sharp variation points, denoting the appearing or vanishing of an important image structure, are utilized to represent the characteristics of the iris. The whole procedure of feature extraction includes two steps: 1) a set of one-dimensional intensity signals is constructed to effectively characterize the most important information of the original two-dimensional image; 2) using a particular class of wavelets, a position sequence of local sharp variation points in such signals is recorded as features. We also present a fast matching scheme based on exclusive OR operation to compute the similarity between a pair of position sequences. Experimental results on 2255 iris images show that the performance of the proposed method is encouraging and comparable to the best iris recognition algorithm found in the current literature.
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Cites
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Cited by
Image understanding for iris biometrics: A survey
Bowyer, K. W., Hollingsworth, K., & Flynn, P. J. (2008) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2007.08.005 · Cited by 907
A review of biometric technology along with trends and prospects
Unar, J. A., Seng, W. C., & Abbasi, A. (2014) · Pattern Recognition · doi:10.1016/j.patcog.2014.01.016 · Cited by 404
Secure and Robust Iris Recognition Using Random Projections and Sparse Representations
Pillai, J. K., Patel, V. M., Chellappa, R., & Ratha, N. K. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.34 · Cited by 324
Combining Face and Iris Biometrics for Identity Verification
Wang, Y., Tan, T., & Jain, A. K. (2003) · Lecture Notes in Computer Science · doi:10.1007/3-540-44887-x_93 · Cited by 313
Self-citationIris recognition: analysis of the error rates regarding the accuracy of the segmentation stage
Proença, H., & Alexandre, L. A. (2010) · Image and Vision Computing · doi:10.1016/j.imavis.2009.03.003 · Cited by 90
Metadata
- Authors
- Ma, L., Tan, T., Wang, Y., & Zhang, D.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2004-06
- Volume
- 13
- Issue
- 6
- Pages
- 739-750
- ISSN
- 1057-7149 (ISSN-L)
- OpenAlex
- W2160242612
- Cited by
- 931
- References
- 21
- FWCI
- 66.78
- Type
- journal article
- Language
- en
- Open access
- closed
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