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A New Method Based on MDA to Enhance the Face Recognition Performance
Aref Shams Baboli, Seyyedeh Maryam Hosseyni Nia, Ali Akbar Shams Baboli, Gholamali Rezai Rad
Pages - 69 - 77     |    Revised - 31-03-2011     |    Published - 04-04-2011
Volume - 5   Issue - 1    |    Publication Date - March / April 2011  Table of Contents
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KEYWORDS
Dimensionality Reduction, HOSVD, Subspace Learning, Multilinear Principal Component Analysis, Multilinear Discriminant Analysis
ABSTRACT
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.
CITED BY (2)  
1 Zhang, F., Wang, X., & Sun, K. (2016). A Report on Multilinear PCA Plus GTDA to Deal With Face Image. Cybernetics and Information Technologies, 16(1), 146-157.
2 Zhang, F., Qi, L., & Chen, E. A Survey on Multilinear PCA Plus GTDA to Deal With Biometric Signal.
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Mr. Aref Shams Baboli
- Iran
Dr. Seyyedeh Maryam Hosseyni Nia
- Iran
Mr. Ali Akbar Shams Baboli
- Iran
ali.shams2222@gmail.com
Associate Professor Gholamali Rezai Rad
- Iran