The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Principal manifolds and probabilistic subspaces for visual recognition
Abstract
Investigates the use of linear and nonlinear principal manifolds for learning low-dimensional representations for visual recognition. Several leading techniques - principal component analysis (PCA), independent component analysis (ICA) and nonlinear kernel PCA (KPCA) - are examined and tested in a visual recognition experiment using 1,800+ facial images from the "FERET" (FacE REcognition Technology) database. We compare the recognition performance of nearest-neighbor matching with each principal manifold representation to that of a maximum a-posteriori (MAP) matching rule using a Bayesian similarity measure derived from dual probabilistic subspaces. The experimental results demonstrate the simplicity, computational economy and performance superiority of the Bayesian subspace method over principal manifold techniques for visual matching.
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
Turk, M., & Pentland, A. (1991) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1991.3.1.71 · Cited by 13,733
Cited by
Visual tracking and recognition using appearance-adaptive models in particle filters
Zhou, S. K., Chellappa, R., & Moghaddam, B. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2004.836152 · Cited by 668
Self-citationHallucinating Face by Eigentransformation
Wang, X., & Tang, X. (2005) · IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2005.848171 · Cited by 482
A unified framework for subspace face recognition
Wang, X., & Tang, X. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.57 · Cited by 359
Biometric score calibration for forensic face recognition
Ali, T. (2014) · doi:10.3990/1.9789036536899 · Cited by 1
Metadata
- Authors
- Moghaddam, B.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2002-06
- Volume
- 24
- Issue
- 6
- Pages
- 780-788
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2163647352
- Cited by
- 294
- References
- 1
- FWCI
- 23.21
- 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: Principal manifolds and probabilistic subspaces for visual recognition. https://app.cochat.ai/papers/10.1109/tpami.2002.1008384
- The paper
- Moghaddam, B. (2002). Principal manifolds and probabilistic subspaces for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2002.1008384
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.
