A novel approach to fuzzy-based facial feature extraction and face recognition

Abstract

Generalized two-dimensional Fisher's linear discriminant (G-2DFLD) is an effective feature extraction technique that maximizes class separability along row and column directions simultaneously. In this paper, we have presented a fuzzy-based feature extraction technique, named fuzzy generalized two-dimensional Fisher's linear discriminant analysis (FG-2DLDA) method. The FG-2DLDA is extended version of the G-2DFLD method. In this study, we also have demonstrated the face recognition using the presented method with radial basis function (RBF) as a classifier. In this context, it is to be noted that the fuzzy membership matrix for the training samples is computed by means of fuzzy k-nearest neighbour (Fk-NN) algorithm. The global mean and class-wise mean training images are generated by combining the fuzzy membership values with the training samples. These mean images are used to compute the fuzzy intra- and inter-class scatter matrices along x- and y-directions. Finally, by solving the Eigen value problems of these scatter matrices, we find the optimal fuzzy projection vectors, which actually used to generate more discriminant features. The presented method has been validated over three public face databases using RBF neural network and establish that the proposed FG-2DLDA method provides favourable recognition rates than some contemporary face recognition methods.