Face recognition by fuzzy generalized 2DFLD method and RBF neural network classifier

The goal of this paper is to present a new fuzzy logic-based feature extraction method, called Fuzzy Generalized Two-Dimensional Fisher's Linear Discriminant (FG-2DFLD) method for face recognition. More specifically, the FG-2DFLD method is an extension of the G-2DFLD method. In FG-2DFLD method, we introduced fuzzy membership values to all the training samples and incorporated them into the within-class and between-class scatter matrices along the row and column directions. The class-wise mean images are also generated by considering the membership values. The fuzzy k-nearest neighbour (FKNN) algorithm is applied for generating the corresponding degrees of class membership. Finally, the feature matrix generated by solving the eigenvalue problem of these scatter matrices actually provide better discrimination information as supported by the simulation results on the AT&T (formally known as ORL) and UMIST face databases using a radial basis function (RBF) neural network.