The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions
Open accessOverview
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
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398
A comparative study of texture measures with classification based on featured distributions
Ojala, T., Pietikäinen, M., & Harwood, D. (1996) · Pattern Recognition · doi:10.1016/0031-3203(95)00067-4 · Cited by 6,882
Face Recognition with Local Binary Patterns
Ahonen, T., Hadid, A., & Pietikäinen, M. (2004) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-24670-1_36 · Cited by 2,462
Cited by
A Completed Modeling of Local Binary Pattern Operator for Texture Classification
Guo, Z., Zhang, L., & Zhang, D. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2044957 · Cited by 2,115
Vehicle detection in aerial imagery : A small target detection benchmark
Razakarivony, S., & Jurie, F. (2016) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2015.11.002 · Cited by 916
Handcrafted vs. non-handcrafted features for computer vision classification
Nanni, L., Ghidoni, S., & Brahnam, S. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.05.025 · Cited by 542
Fusing local patterns of Gabor magnitude and phase for face recognition
Xie, S., Shan, S., Chen, X., & Chen, J. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2010.2041397 · Cited by 395
Local Trinary Patterns for human action recognition
Yeffet, L., & Wolf, L. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459201 · Cited by 339
Metadata
- Authors
- Tan, X., & Triggs, B.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2007-11-03
- Pages
- 168-182
- OpenAlex
- W2980468885
- Cited by
- 604
- References
- 3
- FWCI
- 26.00
- Type
- conference paper
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions. https://app.cochat.ai/papers/10.1007/978-3-540-75690-3_13
- The paper
- Tan, X., & Triggs, B. (2007). Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-540-75690-3_13
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.
