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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Vydáno v:	IEEE transactions on pattern analysis and machine intelligence Ročník 27; číslo 1; s. 4 - 13
Hlavní autoři:	Cevikalp, H., Neamtu, M., Wilkes, M., Barkana, A.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Los Alamitos, CA IEEE 01.01.2005 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
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Popis
Shrnutí:	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 the "small sample size" problem. In this paper, we propose a new face recognition method called the discriminative common vector method based on a variation of Fisher's linear discriminant analysis for the small sample size case. Two different algorithms are given to extract the discriminative common vectors representing each person in the training set of the face database. One algorithm uses the within-class scatter matrix of the samples in the training set while the other uses the subspace methods and the Gram-Schmidt orthogonalization procedure to obtain the discriminative common vectors. Then, the discriminative common vectors are used for classification of new faces. The proposed method yields an optimal solution for maximizing the modified Fisher's linear discriminant criterion given in the paper. Our test results show that the discriminative common vector method is superior to other methods in terms of recognition accuracy, efficiency, and numerical stability.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23 ObjectType-Undefined-1 ObjectType-Feature-3
ISSN:	0162-8828 1939-3539
DOI:	10.1109/TPAMI.2005.9