A new approach: the local feature extraction based on the new regulation of the locally preserving projection
Arif Muntasa · Applied Mathematical Sciences · 2015
A novelty of the local feature extraction was proposed for face recognition. To optimize the Eigenvalue and Eigenvector, a new regulation has been embedded to the Locally Preserving Projection. The proposed method has reduced computation time to obtain the new subspace of the original of the Locally Preserving Projection. The proposed method has also produced orthogonal basis function matrix. However, orthogonal basis function matrix can reconstruct easier than non-orthogonal function. The proposed method has been evaluated by using three face image databases, they are the University of Bern, the YALE-A, and the ORL face image databases. The experimental results of the proposed method have produced the recognition rate 96% for the University of Bern, 96.19% for the YALE-A and 98% for the ORL facial image databases. The experimental results of the proposed method have produced higher recognition rate than the Principal Component Analysis, the Linear Discriminant Analysis and the Locally Preserving Projection.