The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Analyzing PCA-based Face Recognition Algorithms: Eigenvector Selection and Distance Measures
Abstract
Abstract. This study examines the role of Eigenvector selection and Eigenspace distance measures on PCA-based face recognition systems. In particular, it builds on earlier results from the FERET face recognition evaluation studies, which created a large face database (1,196 subjects) and a baseline face recognition system for comparative evaluations. This study looks at using a combinations of traditional distance measures (City-block, Euclidean, Angle, Mahalanobis) in Eigenspace to improve performance in the matching stage of face recognition. A statistically signi cant improvement is observed for the Mahalanobis distance alone when compared to the other three alone. However, no combinations of these measures appear to perform better than Mahalanobis alone. This study also examines questions of how many EIgenvectors to select and according to what ordering criterion. It compares variations in perfor-mance due to dierent distance measures and numbers of Eigenvectors. Ordering Eigenvectors according to a like-image dierence value rather than their Eigenvalues is also considered. 1
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
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
Cited by
Independent comparative study of PCA, ICA, and LDA on the FERET data set
Delac, K., Grgic, M., & Grgic, S. (2005) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.20059 · Cited by 287
Employing fusion of learned and handcrafted features for unconstrained ear recognition
Hansley, E. E., Segundo, M. P., & Sarkar, S. (2018) · IET Biometrics · doi:10.1049/iet-bmt.2017.0210 · Cited by 90
Metadata
- Authors
- Yambor, W. S., Draper, B. A., & Beveridge, J. R.
- Venue
- Series in Machine Perception and Artificial Intelligence
- Publisher
- WORLD SCIENTIFIC
- Published
- 2002-05
- Pages
- 39-60
- ISSN
- 1793-0839 (ISSN-L)
- OpenAlex
- W2483194642
- Cited by
- 225
- References
- 1
- FWCI
- 24.27
- Type
- book chapter
- 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: Analyzing PCA-based Face Recognition Algorithms: Eigenvector Selection and Distance Measures. https://app.cochat.ai/papers/10.1142/9789812777423_0003
- The paper
- Yambor, W. S., Draper, B. A., & Beveridge, J. R. (2002). Analyzing PCA-based Face Recognition Algorithms: Eigenvector Selection and Distance Measures. Series in Machine Perception and Artificial Intelligence. https://doi.org/10.1142/9789812777423_0003
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.
