matlab-(含教程)基于Fisher线性判别FLD算法的人脸识别matlab仿真,可以检测出不同表情的同一个人

function [meanDatabase, eigenFaces, V_Fisher, projectedImagesFisher] = fisherfaceCore(dataBase) classPopulation = 9; classCount = (size(dataBase,2))/classPopulation; P = size(dataBase,2); meanDatabase = mean(dataBase,2); A = dataBase - repmat(meanDatabase, 1, P); [eigenVectors, eigenValues] = eig(A'*A); eigenVectors = eigenVectors(:, 1:P-classCount); eigenFaces = A * eigenVectors; projectedImagesPCA = []; for i = 1 : P temp = eigenFaces'*A(:,i); projectedImagesPCA = [projectedImagesPCA temp]; end meanPCA = mean(projectedImagesPCA,2); m = zeros(P-classCount, classCount); Sw = zeros(P-classCount, P-classCount); Sb = zeros(P-classCount, P-classCount); for i = 1 : classCount m(:,i) = mean(( projectedImagesPCA(:, ((i-1) * classPopulation+1) : i*classPopulation)), 2)'; S = zeros(P-classCount, P-classCount); for j = ((i-1)*classPopulation+1) : (i*classPopulation) S = S + (projectedImagesPCA(:,j) - m(:,i)) * (projectedImagesPCA(:,j) - m(:,i))'; end Sw = Sw + S; Sb = Sb + (m(:,i)-meanPCA) * (m(:,i)-meanPCA)'; end [J_eigenVector, J_eigenValue] = eig(Sb,Sw); % Cost function J = inv(Sw) * Sb J_eigenVector = fliplr(J_eigenVector); V_Fisher = J_eigenVector(:, 1:classCount-1); projectedImagesFisher = zeros(classCount-1, P); for i = 1 : classCount*classPopulation projectedImagesFisher(:,i) = V_Fisher' * projectedImagesPCA(:,i); end end