Improvement of Face Recognition Approach through Fuzzy-Based SVM

In this investigation, automatic face recognition algorithms are discussed. For this purpose, a combi-nation of learning algorithms with supervision are realized; in this way, the classification is first de-signed by the fuzzy-based support vector machine and then the AdaBoost meta-algorithm is applied to the designed classification to reach more accuracy and overfitting control. In the research pro-posed here, in order to address the effects of asymmetric classes, the adaptive coefficients are em-ployed. In addition, to reduce the data size, the principal components analysis is also applied to the raw data. It is to note that the proposed approach is carried out in a set of images extracted from Yale University data set and its accuracy of the proposed one is verified.