The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
A unified framework for subspace face recognition
Abstract
PCA, LDA, and Bayesian analysis are the three most representative subspace face recognition approaches. In this paper, we show that they can be unified under the same framework. We first model face difference with three components: intrinsic difference, transformation difference, and noise. A unified framework is then constructed by using this face difference model and a detailed subspace analysis on the three components. We explain the inherent relationship among different subspace methods and their unique contributions to the extraction of discriminating information from the face difference. Based on the framework, a unified subspace analysis method is developed using PCA, Bayes, and LDA as three steps. A 3D parameter space is constructed using the three subspace dimensions as axes. Searching through this parameter space, we achieve better recognition performance than standard subspace methods.
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The FERET evaluation methodology for face-recognition algorithms
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Cited by
CosFace: Large Margin Cosine Loss for Deep Face Recognition
Wang, H., Wang, Y., Zhou, Z., Ji, X., Gong, D., Zhou, J., Li, Z., & Liu, W. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00552 · Cited by 3,034
Deep Learning Face Representation by Joint Identification-Verification
Sun, Y., Wang, X., & Tang, X. (2014) · arXiv · doi:10.48550/arxiv.1406.4773 · Cited by 1,906
Self-citationDeeply learned face representations are sparse, selective, and robust
Sun, Y., Wang, X., & Tang, X. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298907 · Cited by 939
Self-citationThe CAS-PEAL large-scale chinese face database and baseline evaluations
Gao, W., Cao, B., Shan, S., Chen, X., Zhou, D., Zhang, X., & Zhao, D. (2008) · IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans · doi:10.1109/tsmca.2007.909557 · Cited by 773
Face Recognition Performance: Role of Demographic Information
Klare, B. F., Burge, M. J., Klontz, J. C., Vorder Bruegge, R. W., & Jain, A. K. (2012) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2012.2214212 · Cited by 516
“While 4SF is intended to be representative of learning-based methods in face recognition, it could be the case that other learning-based algorithms (such as [20]–[23]) exhibit different amounts of sensitivity to the demographic distribution of the training data.”
Metadata
- Authors
- Wang, X., & Tang, X.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2004-09
- Volume
- 26
- Issue
- 9
- Pages
- 1222-1228
- ISSN
- 0162-8828 (ISSN-L)
- OpenAlex
- W2167144347
- Cited by
- 359
- References
- 18
- FWCI
- 28.53
- Type
- journal article
- Language
- en
- Open access
- closed
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