Block-Wise Two-Dimensional Maximum Margin Criterion for Face Recognition
Maximum margin criterion (MMC) is a well-known method for feature extraction and dimensionality reduction. However, MMC is based on vector data and fails to exploit local characteristics of image data. In this paper, we propose a two-dimensional generalized framework based on a block-wise approach f...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/875090 |
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| Summary: | Maximum margin criterion (MMC) is a well-known method for feature
extraction and dimensionality reduction. However, MMC is based on
vector data and fails to exploit local characteristics of image
data. In this paper, we propose a two-dimensional generalized
framework based on a block-wise approach for MMC, to deal with
matrix representation data, that is, images. The proposed method,
namely, block-wise two-dimensional maximum margin criterion
(B2D-MMC), aims to find local subspace projections using
unilateral matrix multiplication in each block set, such that in
the subspace a block is close to those belonging to the same class
but far from those belonging to different classes. B2D-MMC avoids
iterations and alternations as in current bilateral projection
based two-dimensional feature extraction techniques by seeking a
closed form solution of one-side projection matrix for each block
set. Theoretical analysis and experiments on benchmark face
databases illustrate that the proposed method is effective and
efficient. |
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| ISSN: | 2356-6140 1537-744X |