Improvement of face recognition performance using a new hybrid subspace classifier

Abstract

Multiple classification systems play an important role in increasing recognition performance, especially when using heterogeneous classifiers that effectively improve performance. In this study, a new hybrid classifier was designed using heterogeneous Fisherface and discriminative common vector approach (DCVA) subspace recognition methods, which gave successful results in face recognition. While the classification process of DCVA is based on the common properties of signals belonging to the classes, the classification process of Fisherface is based on the different properties of signals. To create a hybrid classifier, called the Hybrid DCVA-Fisherface, the classifiers' decision rules were combined using the Minimum Proportional Score Algorithm and Recognition Update Algorithm. In addition to the proposed subspace classifiers, convolutional neural networks, Transform learning-Alexnet, Alexnet + SVM, and Alexnet + KNN were used for classification. Studies were conducted using the ORL, YALE, Extended YALE B and Face Research Lab London Set (FRLL). To better examine the efficiency of the algorithms, tests were also carried out by downsampling the images. When the experimental results were analysed, the proposed hybrid classifier gave higher recognition rates than all classifiers for ORL, YALE, and Extended YALE B. However, deep learning methods generally achieved better recognition performance than subspace classifiers for the FRLL database, which has more classes than other databases.