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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Uncertainty principles and ideal atomic decomposition
Donoho, D. L., & Huo, X. (2001) · IEEE Transactions on Information Theory · doi:10.1109/18.959265 · Cited by 1,949
Probing the Pareto Frontier for Basis Pursuit Solutions
van den Berg, E., & Friedlander, M. P. (2009) · SIAM Journal on Scientific Computing · doi:10.1137/080714488 · Cited by 1,939
Enhancing security and privacy in biometrics-based authentication systems
Ratha, N. K., Connell, J. H., & Bolle, R. M. (2001) · IBM Systems Journal · doi:10.1147/sj.403.0614 · Cited by 1,682
Self-citationJuels, A., & Wattenberg, M. (1999) · Proceedings of the 6th ACM conference on Computer and communications security · doi:10.1145/319709.319714 · Cited by 1,591
Jain, A. K., Nandakumar, K., & Nagar, A. (2008) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/2008/579416 · Cited by 1,134
Personal identification based on iris texture analysis
Ma, L., Tan, T., Wang, Y., & Zhang, D. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1251145 · Cited by 960
Efficient Iris Recognition by Characterizing Key Local Variations
Ma, L., Tan, T., Wang, Y., & Zhang, D. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2004.827237 · Cited by 931
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
Combining Crypto with Biometrics Effectively
Hao, F., Anderson, R., & Daugman, J. (2006) · IEEE Transactions on Computers · doi:10.1109/tc.2006.138 · Cited by 600
Compressed Sensing and Redundant Dictionaries
Rauhut, H., Schnass, K., & Vandergheynst, P. (2008) · IEEE Transactions on Information Theory · doi:10.1109/tit.2008.920190 · Cited by 541
Probing the Uniqueness and Randomness of IrisCodes: Results From 200 Billion Iris Pair Comparisons
Daugman, J. (2006) · Proceedings of the IEEE · doi:10.1109/jproc.2006.884092 · Cited by 467
Teoh, A. B. J., Goh, A., & Ngo, D. C. L. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.250 · Cited by 467
Fast ℓ1-minimization algorithms and an application in robust face recognition: A review
Yang, A. Y., Sastry, S. S., Ganesh, A., & Ma, Y. (2010) · 2010 IEEE International Conference on Image Processing · doi:10.1109/icip.2010.5651522 · Cited by 414
On enabling secure applications through off-line biometric identification
Davida, G. I., Frankel, Y., & Matt, B. J. (2002) · Proceedings. 1998 IEEE Symposium on Security and Privacy (Cat. No.98CB36186) · doi:10.1109/secpri.1998.674831 · Cited by 406
High-Dimensional Centrally Symmetric Polytopes with Neighborliness Proportional to Dimension
Donoho, D. L. (2006) · Discrete & Computational Geometry · doi:10.1007/s00454-005-1220-0 · Cited by 370
Bolle, R. M., Connell, J. H., & Ratha, N. K. (2002) · Pattern Recognition · doi:10.1016/s0031-3203(01)00247-3 · Cited by 324
Self-citationIris segmentation methodology for non-cooperative recognition
Proença, H., & Alexandre, L. A. (2006) · IEE Proceedings - Vision Image and Signal Processing · doi:10.1049/ip-vis:20050213 · Cited by 231
Zuo, J., Ratha, N. K., & Connell, J. H. (2008) · 2008 19th International Conference on Pattern Recognition · doi:10.1109/icpr.2008.4761886 · Cited by 186
Self-citationCancelable Biometrics Realization With Multispace Random Projections
Teoh, A. B. J., & Yuang, C. T. (2007) · IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) · doi:10.1109/tsmcb.2007.903538 · Cited by 183
Image quality assessment for iris biometric
Kalka, N. D., Zuo, J., Schmid, N. A., & Cukic, B. (2006) · SPIE Proceedings · doi:10.1117/12.666448 · Cited by 160
A Bayesian Approach to Deformed Pattern Matching of Iris Images
Thornton, J. R., Savvides, M., & Kumar, B. V. K. V. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1006 · Cited by 151
Matching pursuit filters applied to face identification
Phillips, P. J. (1998) · IEEE Transactions on Image Processing · doi:10.1109/83.704308 · Cited by 130
The ND-IRIS-0405 Iris Image DatasetPreprint
Bowyer, K. W., & Flynn, P. J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.04853 · Cited by 66
Performance Analysis of Iris-Based Identification System at the Matching Score Level
Schmid, N. A., Ketkar, M. V., Singh, H., & Čukić, B. (2006) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2006.873603 · Cited by 65
Meta-Analysis of Third-Party Evaluations of Iris Recognition
Newton, E. M., & Phillips, P. J. (2008) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/tsmca.2008.2008210 · Cited by 50
Using 2D Log-Gabor spatial filters for iris recognition
Du, E. Y. (2006) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.663834 · Cited by 46
Sparsity inspired selection and recognition of iris images
Pillai, J. K., Patel, V. M., & Chellappa, R. (2009) · IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS) · doi:10.1109/btas.2009.5339067 · Cited by 39
Self-citationImage Averaging for Improved Iris Recognition
Hollingsworth, K. P., Bowyer, K. W., & Flynn, P. J. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-01793-3_112 · Cited by 25
Specific Texture Analysis for Iris Recognition
Krichen, E., Allano, L., Garcia-Salicetti, S., & Dorizzi, B. (2005) · Lecture notes in computer science · doi:10.1007/11527923_3 · Cited by 22
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
