Enhancement of the Eigen-Laplacian Smoothing Transform Modeling Based on the Neuman Sparse Spectral
The facial recognition is one of the main topics in the computer vision research. Many algorithms were developed to resolve the main features extraction of the object. The Laplacian Smoothing Transform is one of the algorithms developed based on basis vector smoothness. Unfortunately, it cannot optimally perform the facial image influenced under variant lighting such as the YALE face image database. The proposed approach improves the weakness of the Laplacian Smoothing Transform by the Newman Sparse Matrix Transform. Principally, the proposed method consists by the following steps: to carry out the selection of the Kronecker calculation results, to compute the Enhancement of the Eigen-Laplacian Smoothing Transform including its Enhancement, to calculate the Eigenvalue and Eigenvector, to select the main features, and to project the new space. The evaluation results on the YALE, the ORL, and the UoB face databases indicated that the proposed approach proves the better recognition results respect to the other appearance methods.
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