The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
FaceTracer: A Search Engine for Large Collections of Images with Faces
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Citations
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
Lecture Notes in Computer Science 1205
(1999) · Industrial Robot: An International Journal · doi:10.1108/ir.1999.04926fae.001 · Cited by 38,662
Rapid object detection using a boosted cascade of simple features
Viola, P., & Jones, M. (2005) · Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 · doi:10.1109/cvpr.2001.990517 · Cited by 18,300
Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
Belhumeur, P. N., Hespanha, J. P., & Kriegman, D. J. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.598228 · Cited by 11,707
Self-citationFrom few to many: illumination cone models for face recognition under variable lighting and pose
Georghiades, A. S., Belhumeur, P. N., & Kriegman, D. J. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.927464 · Cited by 4,912
Self-citationLabeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments
Huang, G. B., Mattar, M., Berg, T. L., & Miller, E. G. (2008) · HAL · Cited by 4,445
The FERET evaluation methodology for face-recognition algorithms
Phillips, P. J., Moon, H., Rizvi, S. A., & Rauss, P. J. (2000) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.879790 · Cited by 3,692
Training support vector machines: an application to face detection
Osuna, E., Freund, R., & Girosit, F. (2002) · Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.1997.609310 · Cited by 2,486
Overview of the Face Recognition Grand Challenge
Phillips, P. J., Flynn, P. J., Scruggs, T., Bowyer, K. W., Chang, J., Hoffman, K., Marques, J., Min, J., & Worek, W. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.268 · Cited by 2,438
Cited by
Deep Learning Face Attributes in the Wild
Liu, Z., Luo, P., Wang, X., & Tang, X. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.425 · Cited by 7,747
The Chicago face database: a free stimulus set of faces and norming data
Ma, D. S., Correll, J., & Wittenbrink, B. (2015) · Behavior Research Methods · doi:10.3758/s13428-014-0532-5 · Cited by 1,788
Attribute-Based Classification for Zero-Shot Visual Object Categorization
Lampert, C. H., Nickisch, H., & Harmeling, S. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.140 · Cited by 1,627
Attribute and simile classifiers for face verification
Kumar, N., Berg, A. C., Belhumeur, P. N., & Nayar, S. K. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459250 · Cited by 1,361
Self-citationRanjan, R., Patel, V. M., & Chellappa, R. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2781233 · Cited by 1,333
Metadata
- Authors
- Kumar, N., Belhumeur, P., & Nayar, S.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2008
- Pages
- 340-353
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1599238028
- Cited by
- 368
- References
- 26
- FWCI
- 12.55
- Type
- conference paper
- 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.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: FaceTracer: A Search Engine for Large Collections of Images with Faces. https://app.cochat.ai/papers/10.1007/978-3-540-88693-8_25
- The paper
- Kumar, N., Belhumeur, P., & Nayar, S. (2008). FaceTracer: A Search Engine for Large Collections of Images with Faces. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-540-88693-8_25
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.
