A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf
.
Face Recognition by Using 2D Orthogonal Subspace Projections
2021
Traitement du signal
In this paper, the implementations and comparison of some classifiers along with 2D subspace projection approaches have been carried out for the face recognition problem. For this purpose, the well-known classifiers such as K-Nearest Neighbor (K-NN), Common Matrix Approach (CMA), Support Vector Machine (SVM) and Convolutional Neural Network (CNN) are conducted on low dimensional face representations that are determined from 2DPCA-, 2DSVD- and 2DFDA approaches. CMA, which is a 2D version of the
doi:10.18280/ts.380105
fatcat:irnnpaxzjfgzbppks4qk2huyoq