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 propose a fuzzy logic-based feature extraction technique, called fuzzy generalized two-dimensional Fisher’s linear discriminant analysis (FG-2DLDA) method which is extended version of the G-2DFLD method. This paper also explores the use of the proposed method for face recognition using radial basis function (RBF) neural network as a classifier. Fuzzy membership matrix values are calculated by fuzzy k -nearest neighbour (F k- NN) algorithm for the training samples. These fuzzy membership values are combined with the training samples to generate global mean and class-wise mean training images. Thereafter, the global and class-wise mean images are used to generate fuzzy within- and between-class scatter matrices along the both 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. Our proposed method has been evaluated on the four 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.