Interval type-2 fuzzy linear discriminant analysis for gender recognition

Abstract

In this paper, we propose the interval type-2 fuzzy linear discriminant analysis (IT2FLDA) algorithm for gender recognition. In this algorithm, we first proposed the supervised interval type-2 fuzzy C-Mean (IT2FCM), which introduces the classified information to the IT2FCM, and then the supervised IT2FCM is incorporated into traditional linear discriminant analysis (LDA). By this way, means of each class that are estimated by the supervised IT2FCM can converge to a more desirable location than means of each class obtained by class sample average and the type-1 fuzzy k-nearest neighbor (FKNN) method in the presence of noise. 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 traditional LDA. The experimental results show that the IT2FLDA improved the gender recognition rate when compared to the results from the previous techniques.