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01-Applied Mathematics & Information Sciences
An International Journal
               
 
 
 
 
 
 
 
 
 
 
 
 
 

Content
 

Volumes > Volume 06 > No. 6-1S

 
   

Two-Dimensional Supervised Discriminant Projection Method For Feature Extraction

PP: 81S-85S
Author(s)
Jianguo Wang,
Abstract
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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