Improvement of face recognition performance using a new hybrid subspace classifier

Multiple Classification Systems (MCS) play an important role in increasing recognition performance, especially when using heterogeneous classifiers effectively improves 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. The main feature of the hybrid classifier is that it can assign the misclassified parts to the correct class. While DCVA is based on the common properties of the signals belonging to the classes for classification, Fisherface is based on the different properties of the signals. In order to create a hybrid classifier, called the Hybrid DCVA-Fisherface (HDF), the classifiers' decision rules were combined using the Minimum Proportional Score Algorithm (MPSA) and Recognition Update Algorithm (RUA). To better examine the efficiency of the algorithms, tests were also carried out by downsampling the images. When the experimental results were analyzed, the proposed hybrid classifier gave higher recognition rates than other classifiers.