The Dimensionality Reduction Based on the Average of the Left and the Right Structure Preserving Projection
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The large dimensionalities are the crucial problem in computer vision, especially in biometrics field. In this research, a new method to reduce the dimensionality based on the left and right preserving projection is proposed. The proposed approach transforms the image input to integrate the row and column information. The image data transformation is employed through the shifting to the left and the right. The shifting results, for both the left and right are computed showing the average values needed to realize the dominant features. In this research, the proposed approach uses the Eigenfaces process calculation to avoid the singularity problem. The projection results of the Eigenfaces to the average value of the calculation results are employed to further process on the covariance of the left and the right Structure Preserving Projection. The Eigenvalue problem solution is applied to determine the Eigenvectors associated to the largest Eigenvalues. The results of the experiments demonstrated that the maximum acceptance rates are 100% for the ORL, 98.78% for the YALE, and 96.52% for the UoB face database. The best average acceptance rates achieved are 95.3%, 97.94%, and 95.21% for the ORL, YALE, and UoB face database respectively. The results have also exhibited that the proposal has outperformed the other appearance approaches.
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