Face Recognition by Using 2D Orthogonal Subspace Projections


TRAITEMENT DU SIGNAL, vol.38, no.1, pp.51-60, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 38 Issue: 1
  • Publication Date: 2021
  • Doi Number: 10.18280/ts.380105
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, PASCAL, Business Source Elite, Business Source Premier, Compendex, zbMATH
  • Page Numbers: pp.51-60
  • Keywords: face recognition, common matrix approach, support vector machine, convolutional neural networks, 2D feature extraction, PRINCIPAL COMPONENT ANALYSIS, COMMON VECTOR APPROACH, 2-DIMENSIONAL PCA, REPRESENTATION, KERNEL, SAMPLE
  • Eskisehir Osmangazi University Affiliated: Yes


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 Common Vector Approach (CVA), finds a common matrix for each face class. From the experimental results, we have observed that the SVM presents a dominant performance in general. When overall results of all datasets are considered, CMA is slightly superior to others in case of 2DPCA- and 2DSVD-based features matrices of the AR dataset. On the other side, CNN is better than other classifiers when it comes to develop a face recognition system based on original face samples and 2DPCA-based feature matrices of the Yale dataset. The experimental results indicate that use of these feature matrices with CMA, SVM, and CNN in classification problems is more advantageous than the use of original pixel matrices in the sense of both processing time and memory requirement.