基于MATLAB的人脸朝向识别.doc

clear allclc;% 人脸特征向量提取 % 人数M=10;% 人脸朝向类别数N=5; % 特征向量提取pixel_value=feature_extraction(M,N);% 训练集/测试集产生% 产生图像序号的随机序列rand_label=randperm(M*N); % 人脸朝向标号direction_label=repmat(1:N,1,M);% 训练集train_label=rand_label(1:30);P_train=pixel_value(train_label,:);Tc_train=direction_label(train_label);T_train=ind2vec(Tc_train);% 测试集test_label=rand_label(31:end);P_test=pixel_value(test_label,:);Tc_test=direction_label(test_label);% K-fold交叉验证确定最佳神经元个数k_fold=10;Indices=crossvalind(Kfold,size(P_train,2),k_fold);error_min=10e10;best_number=1;best_input=;best_output=;best_train_set_index=;best_validation_set_index=;h=waitbar(0,正在寻找最佳神经元个数.);for i=1:k_fold % 验证集标号 validation_set_index=(Indices=i); % 训练集标号 train_set_index=validation_set_index; % 验证集 validation_set_input=P_train(:,validation_set_index); validation_set_output=T_train(:,validation_set_index); % 训练集 train_set_input=P_train(:,train_set_index); train_set_output=T_train(:,train_set_index); for number=10:30 for j=1:5 ratej=length(find(Tc_train(:,train_set_index)=j)/length(find(train_set_index=1); end net=newlvq(minmax(train_set_input),number,cell2mat(rate); % 设置网络参数 net.trainParam.epochs=100; net.trainParam.show=10; net.trainParam.lr=0.1; net.trainParam.goal=0.001; % 训练网络 net=train(net,train_set_input,train_set_output); waitbar(i-1)*21+number)/219,h); % 仿真测试 T_sim=sim(net,validation_set_input); Tc_sim=vec2ind(T_sim); error=length(find(Tc_sim=Tc_train(:,validation_set_index); if error0.6; if flag1 & flag2 if isequal(Tc(i),a2_1) w1(index1,:)=w1(index1,:)+lr*(P(:,i)-w1(index1,:); w1(index2,:)=w1(index2,:)-lr*(P(:,i)-w1(index2,:); else w1(index1,:)=w1(index1,:)-lr*(P(:,i)-w1(index1,:); w1(index2,:)=w1(index2,:)+lr*(P(:,i)-w1(index2,:); end else w1(index1,:)=w1(index1,:)+lr*(P(:,i)-w1(index1,:); end endendfunction result=lvq_predict(P,Tc,Num_Compet,w1,w2)n=size(P,2);result=zeros(2,n);result(1,:)=Tc;for i=1:n d=zeros(Num_Compet,1); for j=1:Num_Compet d(j)=sqrt(sse(w1(j,:)-P(:,i); end n1=compet(-1*d); n2=purelin(w2*n1); result(2,i)=vec2ind(n2);endNum_Correct=length(find(result(2,:)=Tc);accuracy=Num_Correct/n;disp(accuracy= num2str(accuracy*100) %( num2str(Num_Correct) / num2str(n) );% 清除环境变量clear allclc;% 人脸特征向量提取 % 人数M=10;% 人脸朝向类别数N=5; % 特征向量提取pixel_value=feature_extraction(M,N);% 训练集/测试集产生% 产生图像序号的随机序列rand_label=randperm(M*N); % 人脸朝向标号direction_label=repmat(1:N,1,M);% 训练集train_label=rand_label(1:30);P_train=pixel_value(train_label,:);Tc_train=direction_label(train_label);% 测试集test_label=rand_label(31:end);P_test=pixel_value(test_label,:);Tc_test=direction_label(test_label);% 计算PCfor i=1:5 ratei=length(find(Tc_train=i)/30;end% LVQ1算法w1,w2=lvq1_train(P_train,Tc_train,20,cell2mat(rate),0.01,5);result_1=lvq_predict(P_test,Tc_test,20,w1,w2);% LVQ2算法w1,w2=lvq2_train(P_train,Tc_train,20,0.01,5,w1,w2);result_2=lvq_predict(P_test,Tc_test,20,w1,w2);% 清除环境变量clear allclc;% 人脸特征向量提取 % 人数M=10;% 人脸朝向类别数N=5; % 特征向量提取pixel_value=feature_extraction(M,N);% 训练集/测试集产生% 产生图像序号的随机序列rand_label=randperm(M*N); % 人脸朝向标号direction_label=1 0 0;1 1 0;0 1 0;0 1 1;0 0 1;% 训练集train_label=rand_label(1:30);P_train=pixel_value(train_label,:);dtrain_label=train_label-floor(train_label/N)*N;dtrain_label(dtrain_label=0)=N;T_train=direction_label(dtrain_label,:);% 测试集test_label=rand_label(31:end);P_test=pixel_value(test_label,:);dtest_label=test_label-floor(test_label/N)*N;dtest_label(dtest_label=0)=N;T_test=direction_label(dtest_label,:)% 创建BP网络net=newff(minmax(P_train),10,3,tansig,purelin,trainlm);% 设置训练参数net.trainParam.epochs=1000;net.trainParam.show=10;net.trainParam.goal