The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Secure and Robust Iris Recognition Using Random Projections and Sparse Representations
Abstract
Noncontact biometrics such as face and iris have additional benefits over contact-based biometrics such as fingerprint and hand geometry. However, three important challenges need to be addressed in a noncontact biometrics-based authentication system: ability to handle unconstrained acquisition, robust and accurate matching, and privacy enhancement without compromising security. In this paper, we propose a unified framework based on random projections and sparse representations, that can simultaneously address all three issues mentioned above in relation to iris biometrics. Our proposed quality measure can handle segmentation errors and a wide variety of possible artifacts during iris acquisition. We demonstrate how the proposed approach can be easily extended to handle alignment variations and recognition from iris videos, resulting in a robust and accurate system. The proposed approach includes enhancements to privacy and security by providing ways to create cancelable iris templates. Results on public data sets show significant benefits of the proposed approach.
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Cites
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957
Candes, E. J., Romberg, J., & Tao, T. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2005.862083 · Cited by 15,639
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
Tropp, J. A., & Gilbert, A. C. (2007) · IEEE Transactions on Information Theory · doi:10.1109/tit.2007.909108 · Cited by 9,653
Robust Face Recognition via Sparse Representation
Wright, J., Yang, A. Y., Ganesh, A., Sastry, S. S., & Ma, Y. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.79 · Cited by 9,388
Stable signal recovery from incomplete and inaccurate measurements
Candès, E. J., Romberg, J. K., & Tao, T. (2006) · Communications on Pure and Applied Mathematics · doi:10.1002/cpa.20124 · Cited by 7,036
Atomic Decomposition by Basis Pursuit
Chen, S. S., Donoho, D. L., & Saunders, M. A. (1998) · SIAM Journal on Scientific Computing · doi:10.1137/s1064827596304010 · Cited by 6,845
CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
Needell, D., & Tropp, J. A. (2009) · Applied and Computational Harmonic Analysis · doi:10.1016/j.acha.2008.07.002 · Cited by 4,326
Compressive Sensing [Lecture Notes]
Baraniuk, R. (2007) · IEEE Signal Processing Magazine · doi:10.1109/msp.2007.4286571 · Cited by 4,177
High confidence visual recognition of persons by a test of statistical independence
Daugman, J. G. (1993) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.244676 · Cited by 3,252
Iris recognition: an emerging biometric technology
Wildes, R. P. (1997) · Proceedings of the IEEE · doi:10.1109/5.628669 · Cited by 2,202
Cited by
Cancelable Biometrics: A review
Patel, V. M., Ratha, N. K., & Chellappa, R. (2015) · IEEE Signal Processing Magazine · doi:10.1109/msp.2015.2434151 · Cited by 419
Self-citationIris Recognition With Off-the-Shelf CNN Features: A Deep Learning Perspective
Nguyen, K., Fookes, C., Ross, A., & Sridharan, S. (2018) · IEEE Access · doi:10.1109/access.2017.2784352 · Cited by 355
Zhang, L., Li, L., Yang, A., Shen, Y., & Yang, M. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.04.016 · Cited by 294
Joint Biological ID : A Secure and Efficient Lightweight Biometric Authentication Scheme
Wang, H., Fan, K., Chen, H., Chen, Q., Zhang, K., Li, F., Li, H., & Yang, Y. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3186999 · Cited by 6
Metadata
- Authors
- Pillai, J. K., Patel, V. M., Chellappa, R., & Ratha, N. K.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2011-09
- Volume
- 33
- Issue
- 9
- Pages
- 1877-1893
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2099278510
- Cited by
- 324
- References
- 39
- FWCI
- 39.52
- Type
- journal article
- Language
- en
- Open access
- closed
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- Pillai, J. K., Patel, V. M., Chellappa, R., & Ratha, N. K. (2011). Secure and Robust Iris Recognition Using Random Projections and Sparse Representations. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2011.34
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