Improvement of Face Recognition Approach through Fuzzy-Based SVM

In this investigation, automatic face recognition algorithms are discussed. For this purpose, a combination of learning algorithms with supervision are realized; in this way, the classification is first designed 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 proposed here, in order to address the effects of asymmetric classes, the adaptive coefficients are employed. 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.

Keywords:

face recognition , Classification , fuzzy-based support vector machine , Adaboost