The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
LBP − TOP Based Countermeasure against Face Spoofing Attacks
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
(2008) · doi:10.1007/978-0-387-71041-9 · Cited by 1,406
Cited by
Face Spoof Detection With Image Distortion Analysis
Wen, D., Han, H., & Jain, A. K. (2015) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2015.2400395 · Cited by 905
Biometric Antispoofing Methods: A Survey in Face Recognition
Galbally, J., Marcel, S., & Fierrez, J. (2014) · IEEE Access · doi:10.1109/access.2014.2381273 · Cited by 440
Self-citationFace anti-spoofing using patch and depth-based CNNs
Atoum, Y., Liu, Y., Jourabloo, A., & Liu, X. (2017) · 2017 IEEE International Joint Conference on Biometrics (IJCB) · doi:10.1109/btas.2017.8272713 · Cited by 429
Presentation Attack Detection Methods for Face Recognition Systems: A Comprehensive Survey
Ramachandra, R., & Busch, C. (2018) · ACM Computing Surveys · doi:10.1145/3038924 · Cited by 332
Learning temporal features using LSTM-CNN architecture for face anti-spoofing
Xu, Z., Li, S., & Deng, W. (2015) · 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR) · doi:10.1109/acpr.2015.7486482 · Cited by 293
Metadata
- Authors
- de Freitas Pereira, T., Anjos, A., De Martino, J. M., & Marcel, S.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2013
- Pages
- 121-132
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1770095230
- Cited by
- 319
- References
- 1
- FWCI
- 8.07
- 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: LBP − TOP Based Countermeasure against Face Spoofing Attacks. https://app.cochat.ai/papers/10.1007/978-3-642-37410-4_11
- The paper
- de Freitas Pereira, T., Anjos, A., De Martino, J. M., & Marcel, S. (2013). LBP − TOP Based Countermeasure against Face Spoofing Attacks. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-642-37410-4_11
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.
