1. A computer-implemented method of performing facial recognition using genetic algorithm-modified fuzzy linear discriminant analysis, comprising the steps of a) establishing a set of N test face images, where N is a non-zero, positive integer; b) establishing a set of N training face images; c) calculating a mean vector of all test face images as m ′ = 1 N ⁢ ∑ i = 1 N ⁢ x i ′ , where x′ i represents an i-th test face image vector having a size M′, where i is an integer; d) calculating a mean vector of all training face images as m = 1 N ⁢ ∑ i = 1 N ⁢ x i , where x i represents an i-th training face image vector having a size M; e) calculating a total scatter matrix S T as S T = ∑ k = 1 N ⁢ ( x k - m ) ⁢ ( x k - m ) T , where k is an integer; f) calculating a set of orthonormal image vectors w i , wherein the set of orthonormal image vectors w i are eigenvectors of a covariance matrix C =P T P, where P is a matrix composed of mean centered images m i as column vectors placed side by side; g) forming a projection matrix W of order (M×T) from the set of orthonormal image vectors w i , where T represents the total number of orthonormal image vectors w i ; h) calculating a first projection matrix W PCA as W PCA =arg W max |W T S T W|; i) calculating a membership grade of a j-th image vector in the i-th class μ ij as μ ij = α + β ⁢ ⁢ ( n ij k ) if i is the same as a label of the j-th pattern, and as μ ij = β ⁢ ⁢ ( n ij k ) if i is not the same as the label of the j-th pattern, where n ij represents a number of neighbors of the j-th vector that belong to the i-th class, k is an integer representing a number of nearest neighbors of the j-th image vector, and wherein β=1−α, and α represents an offset in membership grading assigned to a vector in its class; j) optimizing α using a genetic algorithm in order to minimize recognition error; k) calculating a mean of all vectors belonging to the l-th class as m _ l = ∑ j = 1 N ⁢ μ ij p ⁢ x j ∑ j = 1 N ⁢ μ ij p , where l is an integer; l) calculating a between-class scatter matrix S B and a within-class scatter matrix S W as S B = ∑ i = 1 C ⁢ ∑ j = 1 N ⁢ μ ij p ⁡ ( m _ l - m ) · ( m _ l - m ) T and S W = ∑ i = 1 C ⁢ ∑ x k ∈ X i ⁢ μ ij p ⁡ ( x i - m _ l ) · ( x i - m _ l ) T , respectively, where X i , represents a set of samples belonging to the i-th class, x k is the k-th image of the i-th class, and i=1, 2,. .. , c, where p is a fuzzy modifier, which is a constant controlling influence of fuzzy membership degree; m) calculating a second projection matrix W LDA as W LDA = arg ⁢ ⁢ max W ⁢  W T ⁢ S B ⁢ W   W T ⁢ S W ⁢ W  ; n) calculating a total transformation matrix W T as W T =W LDA T ·W PCA T ; o) calculating a set of test feature vectors y′ k as y′ k =W T x′ k ; p) calculating a set of training feature vectors y k as y k =W T x k ; q) calculating a Euclidean distance between each of the test feature vectors y′ k and each of the training feature vectors y k ; and r) calculating a classification based upon the calculated Euclidean distances.