A novel fuzzy linear discriminant analysis for face recognition

Abstract

In practical application, the performances of face recognition are always affected by variations of expression, illumination and so on. To address this problem, an interval type-2 fuzzy linear discriminant analysis (IT2FLDA) method is proposed. In this paper, we first propose the supervised interva l type-2 fuzzy C-Means (IT2FCM) algorithm. Moreover, the supervised IT2FCM is incorporated into linear discriminant analysis (LDA). In this method, the membership degree matrix of training samples belonging to each class and means of each class are firstly calculated by the supervised IT2FCM algorithm. They are then applied to the definition of fuzzy within-class scatter matrix and fuzzy between-class scatter matrix, respectively. In doing so, means of each class that are estimated by the supervised IT2FCM can converge to a more desirable location than ones obtained by class sample average and fuzzy k-nearest neighbor (FKNN) method. Furthermore, the IT2FLDA is able to minimize the effects of uncertainties, find the optimal projective directions and make the feature subspace discriminating and robust, which inherits the benefits of the supervised IT2FCM and LDA. The experiment results show that the IT2FLDA improves the recognition rate and reduces sensitivity to variations when compared to results from the previous techniques.