The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Face recognition by support vector machines
Abstract
Support vector machines (SVM) have been recently proposed as a new technique for pattern recognition. SVM with a binary tree recognition strategy are used to tackle the face recognition problem. We illustrate the potential of SVM on the Cambridge ORL face database, which consists of 400 images of 40 individuals, containing quite a high degree of variability in expression, pose, and facial details. We also present the recognition experiment on a larger face database of 1079 images of 137 individuals. We compare the SVM-based recognition with the standard eigenface approach using the nearest center classification (NCC) criterion.
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
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
Cited by
A Survey of Face Recognition Techniques
Jafri, R., & Arabnia, H. R. (2009) · Journal of Information Processing Systems · doi:10.3745/jips.2009.5.2.041 · Cited by 677
Recent advances in visual and infrared face recognition—a review
Kong, S. G., Heo, J., Abidi, B. R., Paik, J., & Abidi, M. A. (2005) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2004.04.001 · Cited by 586
A survey on deep learning based face recognition
Guo, G., & Zhang, N. (2019) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2019.102805 · Cited by 485
Self-citationTotal variation models for variable lighting face recognition
Chen, T., Yin, W., Zhou, X. S., Comaniciu, D., & Huang, T. S. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.195 · Cited by 437
Face recognition with support vector machines: global versus component-based approach
Heisele, B., Ho, P., & Poggio, T. (2002) · Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 · doi:10.1109/iccv.2001.937693 · Cited by 419
Metadata
- Authors
- Guo, G., Li, S. Z., & Chan, K.
- Venue
- Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580)
- Publisher
- IEEE Comput. Soc
- Published
- 2002-11-07
- Pages
- 196-201
- ISSN
- 1541-5058 (ISSN-L)
- OpenAlex
- W1881452810
- Cited by
- 520
- References
- 2
- FWCI
- 15.48
- 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.
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: Face recognition by support vector machines. https://app.cochat.ai/papers/10.1109/afgr.2000.840634
- The paper
- Guo, G., Li, S. Z., & Chan, K. (2002). Face recognition by support vector machines. Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580). https://doi.org/10.1109/afgr.2000.840634
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.
