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| A New Method Based on MDA to Enhance the Face Recognition Performance
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Full
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 PDF(251.3KB) |
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Source |
International Journal of Image Processing (IJIP) |
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Table of Contents |
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Download
Complete Issue PDF(4.73MB) |
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Volume: 5 Issue: 1 |
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Pages: 1-108 |
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Publication
Date: March / April 2011 |
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ISSN
(Online): 1985-2304 |
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Pages |
69 - 77 |
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Author(s) |
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Published
Date |
04-04-2011 |
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Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
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ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
Colleague |
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KEYWORDS: Dimensionality Reduction, HOSVD, Subspace Learning, Multilinear Principal Component Analysis, Multilinear Discriminant Analysis |
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| This Manuscript is indexed in the following databases/websites:- |
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| 1. Directory of Open Access Journals (DOAJ) |
| 2. Scribd |
| 3. refSeek |
| 4. Socol@r |
| 5. Docstoc |
| 6. Google Scholar |
| 7. iSEEK |
| 8. WorldCat |
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| A novel tensor based method is prepared to solve the supervised dimensionality reduction problem. In this paper a multilinear principal component analysis(MPCA) is utilized to reduce the tensor object dimension then a multilinear discriminant analysis(MDA), is applied to find the best subspaces. Because the number of possible subspace dimensions for any kind of tensor objects is extremely high, so testing all of them for finding the best one is not feasible. So this paper also presented a method to solve that problem, The main criterion of algorithm is not similar to Sequential mode truncation(SMT) and full projection is used to initialize the iterative solution and find the best dimension for MDA. This paper is saving the extra times that we should spend to find the best dimension. So the execution time will be decreasing so much. It should be noted that both of the algorithms work with tensor objects with the same order so the structure of the objects has been never broken. Therefore the performance of this method is getting better. The advantage of these algorithms is avoiding the curse of dimensionality and having a better performance in the cases with small sample sizes. Finally, some experiments on ORL and CMPU-PIE databases is provided. |
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| Aref Shams Baboli : Colleagues
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| Seyyedeh Maryam Hosseyni Nia : Colleagues
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| Ali Akbar Shams Baboli : Colleagues
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| Gholamali Rezai Rad : Colleagues
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