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Two-Dimensional Supervised Discriminant Projection Method For Feature Extraction |
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PP: 81S-85S |
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Author(s) |
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Jianguo Wang,
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Abstract |
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| For supervised discriminant projection (SDP)method, the image matrix data are vectorized
to find the intrinsic manifold structure, and the dimension of matrix data is usually very high, so SDP
cannot be performed because of the singularity of scatter matrix. In addition, the matrix-to-vector
transform procedure may cause the loss of some useful structural information embedding in the
original images. Thus, in this paper, a novel method, called 2D supervised discriminant projection
(2DSDP), for face recognition is proposed. The proposed method not only takes into account both the
local information of the data and the class information of the data to model the manifold structure,
but also preserves the useful information of the image data. To evaluate the performance of the
proposed method, several experiments are conducted on the Yale face database, and the FERET face
database. The high recognition rates demonstrate the effectiveness of the proposed method. |
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