The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Independent comparative study of PCA, ICA, and LDA on the FERET data set
Abstract
Face recognition is one of the most successful applications of image analysis and understanding and has gained much attention in recent years. Various algorithms were proposed and research groups across the world reported different and often contradictory results when comparing them. The aim of this paper is to present an independent, comparative study of three most popular appearance-based face recognition projection methods (PCA, ICA, and LDA) in completely equal working conditions regarding preprocessing and algorithm implementation. We are motivated by the lack of direct and detailed independent comparisons of all possible algorithm implementations (e.g., all projection–metric combinations) in available literature. For consistency with other studies, FERET data set is used with its standard tests (gallery and probe sets). Our results show that no particular projection–metric combination is the best across all standard FERET tests and the choice of appropriate projection–metric combination can only be made for a specific task. Our results are compared to other available studies and some discrepancies are pointed out. As an additional contribution, we also introduce our new idea of hypothesis testing across all ranks when comparing performance results. © 2006 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 15, 252–260, 2005
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
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
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
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
Martinez, A. M., & Kak, A. C. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.908974 · Cited by 3,249
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
Discriminant Analysis of Principal Components for Face Recognition
Zhao, W., Krishnaswamy, A., Chellappa, R., Swets, D. L., & Weng, J. (1998) · Face Recognition · doi:10.1007/978-3-642-72201-1_4 · Cited by 696
Recognizing faces with PCA and ICA
Draper, B. A., Baek, K., Bartlett, M. S., & Beveridge, J. R. (2003) · Computer Vision and Image Understanding · doi:10.1016/s1077-3142(03)00077-8 · Cited by 549
Principal manifolds and probabilistic subspaces for visual recognition
Moghaddam, B. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1008384 · Cited by 294
Analyzing PCA-based Face Recognition Algorithms: Eigenvector Selection and Distance Measures
Yambor, W. S., Draper, B. A., & Beveridge, J. R. (2002) · Series in Machine Perception and Artificial Intelligence · doi:10.1142/9789812777423_0003 · Cited by 225
Cited by
Gross, R., Matthews, I., Cohn, J., Kanade, T., & Baker, S. (2010) · Image and Vision Computing · doi:10.1016/j.imavis.2009.08.002 · Cited by 1,621
Wang, S., Liu, Z., Lv, S., Lv, Y., Wu, G., Peng, P., Chen, F., & Wang, X. (2010) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2010.2060716 · Cited by 397
Gross, R., Matthews, I., Cohn, J., Kanade, T., & Baker, S. (2008) · 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition · doi:10.1109/afgr.2008.4813399 · Cited by 373
Metadata
- Authors
- Delac, K., Grgic, M., & Grgic, S.
- Venue
- International Journal of Imaging Systems and Technology
- Publisher
- Wiley
- Published
- 2005
- Volume
- 15
- Issue
- 5
- Pages
- 252-260
- ISSN
- 0899-9457 (ISSN-L)
- 1098-1098
- OpenAlex
- W2109002458
- Cited by
- 287
- References
- 15
- FWCI
- 14.76
- 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: Independent comparative study of PCA, ICA, and LDA on the FERET data set. https://app.cochat.ai/papers/10.1002/ima.20059
- The paper
- Delac, K., Grgic, M., & Grgic, S. (2005). Independent comparative study of PCA, ICA, and LDA on the FERET data set. International Journal of Imaging Systems and Technology. https://doi.org/10.1002/ima.20059
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.
