Discriminative common vectors for face recognition

In face recognition tasks, the dimension of the sample space is typically larger than the number of the samples in the training set. As a consequence, the within-class scatter matrix is singular and the linear discriminant analysis (LDA) method cannot be applied directly. This problem is known as th...

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Bibliographic Details
Published in:	IEEE transactions on pattern analysis and machine intelligence Vol. 27; no. 1; pp. 4 - 13
Main Authors:	Cevikalp, H., Neamtu, M., Wilkes, M., Barkana, A.
Format:	Journal Article
Language:	English
Published:	Los Alamitos, CA IEEE 01.01.2005 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Application software Applied sciences Artificial Intelligence Computer science; control theory; systems Discriminant Analysis discriminative common vectors Exact sciences and technology Face - anatomy & histology Face detection Face recognition Fisher's linear discriminant analysis History, Ancient Humans Image Interpretation, Computer-Assisted Image recognition Index Terms- Common vectors Light scattering Linear discriminant analysis Mathematical analysis Mathematical models Pattern Recognition, Automated - methods Pattern recognition. Digital image processing. Computational geometry Photography - methods Principal Component Analysis Sample Size Signal Processing, Computer-Assisted small sample size Studies subspace methods Testing Training Vectors Vectors (mathematics) Face recognition Sample size Subspace method subspace methods Small sample small sample size discriminative common vectors Common vectors Fisher's linear discriminant analysis Principal component analysis
ISSN:	0162-8828, 1939-3539
Online Access:	Get full text
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