The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification
Abstract
In this paper, two supervised methods for enhancing the classification accuracy of the Nonnegative Matrix Factorization (NMF) algorithm are presented. The idea is to extend the NMF algorithm in order to extract features that enforce not only the spatial locality, but also the separability between classes in a discriminant manner. The first method employs discriminant analysis in the features derived from NMF. In this way, a two-phase discriminant feature extraction procedure is implemented, namely NMF plus Linear Discriminant Analysis (LDA). The second method incorporates the discriminant constraints inside the NMF decomposition. Thus, a decomposition of a face to its discriminant parts is obtained and new update rules for both the weights and the basis images are derived. The introduced methods have been applied to the problem of frontal face verification using the well-known XM2VTS database. Both methods greatly enhance the performance of NMF for frontal face verification.
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
Learning the parts of objects by non-negative matrix factorization
Lee, D. D., & Seung, H. S. (1999) · Nature · doi:10.1038/44565 · Cited by 13,868
Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
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
ALGORITHMS FOR NON-NEGATIVE MATRIX FACTORIZATION
Lee, D. D., & Seung, H. S. (2000) · Cited by 7,952
Face recognition: A literature survey
Zhao, W., Chellappa, R., Phillips, P. J., & Rosenfeld, A. (2003) · ACM Computing Surveys · doi:10.1145/954339.954342 · Cited by 6,163
Golub, G. H., & Loan, C. F. V. (1996) · Johns Hopkins University Press eBooks · Cited by 6,027
Bregman, L. M. (1967) · USSR Computational Mathematics and Mathematical Physics · doi:10.1016/0041-5553(67)90040-7 · Cited by 2,618
Human and machine recognition of faces: a survey
Chellappa, R., Wilson, C. L., & Sirohey, S. (1995) · Proceedings of the IEEE · doi:10.1109/5.381842 · Cited by 2,494
Application of the Karhunen-Loeve procedure for the characterization of human faces
Kirby, M., & Sirovich, L. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.41390 · Cited by 2,448
Face recognition by independent component analysis
Bartlett, M. S., Movellan, J. R., & Sejnowski, T. J. (2002) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2002.804287 · Cited by 1,930
Cited by
Online Nonnegative Matrix Factorization With Robust Stochastic Approximation
Guan, N., Tao, D., Luo, Z., & Yuan, B. (2012) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2012.2197827 · Cited by 287
Zhi, R., Flierl, M., Ruan, Q., & Kleijn, W. B. (2011) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2010.2044788 · Cited by 260
Robust Structured Nonnegative Matrix Factorization for Image Representation
Li, Z., Tang, J., & He, X. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2017.2691725 · Cited by 257
Metadata
- Authors
- Zafeiriou, S., Tefas, A., Buciu, I., & Pitas, I.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 2006-05
- Volume
- 17
- Issue
- 3
- Pages
- 683-695
- ISSN
- 1045-9227 (ISSN-L)
- 1941-0093
- OpenAlex
- W2142621404
- Cited by
- 329
- References
- 38
- FWCI
- 20.39
- 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.
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: Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification. https://app.cochat.ai/papers/10.1109/tnn.2006.873291
- The paper
- Zafeiriou, S., Tefas, A., Buciu, I., & Pitas, I. (2006). Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification. IEEE Transactions on Neural Networks. https://doi.org/10.1109/tnn.2006.873291
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.
