The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition
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Citations
Cites
Daugman, J. (2004) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2003.818350 · Cited by 2,927
Cited by
Multi-Factor Authentication: A Survey
Ometov, A., Bezzateev, S., Mäkitalo, N., Andreev, S., Mikkonen, T., & Koucheryavy, Y. (2018) · Cryptography · doi:10.3390/cryptography2010001 · Cited by 434
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
Metadata
- Authors
- Tan, T., He, Z., & Sun, Z.
- Venue
- Image and Vision Computing
- Publisher
- Elsevier BV
- Published
- 2010-02
- Volume
- 28
- Issue
- 2
- Pages
- 223-230
- ISSN
- 0262-8856 (ISSN-L)
- OpenAlex
- W2168567026
- Cited by
- 271
- References
- 1
- FWCI
- 28.87
- 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.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
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- CoChat. (2026). Citation check: Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition. https://app.cochat.ai/papers/10.1016/j.imavis.2009.05.008
- The paper
- Tan, T., He, Z., & Sun, Z. (2010). Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition. Image and Vision Computing. https://doi.org/10.1016/j.imavis.2009.05.008
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.
