%% LVQ神经网络的预测——人脸识别
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% <table border="0" width="600px" id="table1"> <tr> <td><b><font size="2">该案例作者申明：</font></b></td> </tr> <tr><td><span class="comment"><font size="2">1：本人长期驻扎在此<a target="_blank" href="http://www.ilovematlab.cn/forum-158-1.html"><font color="#0000FF">板块</font></a>里，对该案例提问，做到有问必答。本套书籍官方网站为：<a href="http://video.ourmatlab.com">video.ourmatlab.com</a></font></span></td></tr><tr> <td><font size="2">2：点此<a href="http://union.dangdang.com/transfer/transfer.aspx?from=P-284318&backurl=http://www.dangdang.com/">从当当预定本书</a>：<a href="http://union.dangdang.com/transfer/transfer.aspx?from=P-284318&backurl=http://www.dangdang.com/">《Matlab神经网络30个案例分析》</a>。</td></tr><tr> <td><p class="comment"></font><font size="2">3</font><font size="2">：此案例有配套的教学视频，视频下载方式<a href="http://video.ourmatlab.com/vbuy.html">video.ourmatlab.com/vbuy.html</a></font><font size="2">。 </font></p></td> </tr> <tr> <td><span class="comment"><font size="2"> 4：此案例为原创案例，转载请注明出处（《Matlab神经网络30个案例分析》）。</font></span></td> </tr> <tr> <td><span class="comment"><font size="2"> 5：若此案例碰巧与您的研究有关联，我们欢迎您提意见，要求等，我们考虑后可以加在案例里。</font></span></td> </tr> </table>
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%% 清除环境变量
clear all
clc
%% 人脸特征向量提取
% 人数
M = 20;
% 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(1:80);
P_train = pixel_value(train_label,:)';
Tc_train = direction_label(train_label);
T_train = ind2vec(Tc_train);
% 测试集
test_label = rand_label(81: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 = 10:100
for j = 1:5
rate{j} = 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 error < error_min
error_min = error;
best_number = number;
best_input = train_set_input;
best_output = train_set_output;
best_train_set_index = train_set_index;
best_validation_set_index = validation_set_index;
end
end
end
disp(['经过交叉验证，得到的最佳神经元个数为：' num2str(best_number)]);
close(h);
%% 创建LVQ网络
for i = 1:5
rate{i} = length(find(Tc_train(:,best_train_set_index) == i))/length(find(best_train_set_index == 1));
end
net = newlvq(minmax(best_input),best_number,cell2mat(rate),0.01);
% 设置训练参数
net.trainParam.epochs = 100;
net.trainParam.goal = 0.001;
net.trainParam.lr = 0.1;
%% 训练网络
net = train(net,best_input,best_output);
%% 人脸识别测试
T_sim = sim(net,P_test);
Tc_sim = vec2ind(T_sim);
result = [Tc_test;Tc_sim]
%% 结果显示
% 训练集人脸标号
strain_label = sort(train_label(best_train_set_index));
htrain_label = ceil(strain_label/N);
% 训练集人脸朝向标号
dtrain_label = strain_label - floor(strain_label/N)*N;
dtrain_label(dtrain_label == 0) = N;
% 显示训练集图像序号
disp('训练集图像为：' );
for i = 1:length(find(best_train_set_index == 1))
str_train = [num2str(htrain_label(i)) '_'...
num2str(dtrain_label(i)) ' '];
fprintf('%s',str_train)
if mod(i,5) == 0
fprintf('\n');
end
end
% 验证集人脸标号
svalidation_label = sort(train_label(best_validation_set_index));
hvalidation_label = ceil(svalidation_label/N);
% 验证集人脸朝向标号
dvalidation_label = svalidation_label - floor(svalidation_label/N)*N;
dvalidation_label(dvalidation_label == 0) = N;
% 显示验证集图像序号
fprintf('\n');
disp('验证集图像为：' );
for i = 1:length(find(best_validation_set_index == 1))
str_validation = [num2str(hvalidation_label(i)) '_'...
num2str(dvalidation_label(i)) ' '];
fprintf('%s',str_validation)
if mod(i,5) == 0
fprintf('\n');
end
end
% 测试集人脸标号
stest_label = sort(test_label);
htest_label = ceil(stest_label/N);
% 测试集人脸朝向标号
dtest_label = stest_label - floor(stest_label/N)*N;
dtest_label(dtest_label == 0) = N;
% 显示测试集图像序号
fprintf('\n');
disp('测试集图像为：');
for i = 1:20
str_test = [num2str(htest_label(i)) '_'...
num2str(dtest_label(i)) ' '];
fprintf('%s',str_test)
if mod(i,5) == 0
fprintf('\n');
end
end
% 显示识别出错图像
error = Tc_sim - Tc_test;
location = {'左方' '左前方' '前方' '右前方' '右方'};
for i = 1:length(error)
if error(i) ~= 0
% 识别出错图像人脸标号
herror_label = ceil(test_label(i)/N);
% 识别出错图像人脸朝向标号
derror_label = test_label(i) - floor(test_label(i)/N)*N;
derror_label(derror_label == 0) = N;
% 图像原始朝向
standard = location{Tc_test(i)};
% 图像识别结果朝向
identify = location{Tc_sim(i)};
str_err = strcat(['图像' num2str(herror_label) '_'...
num2str(derror_label) '识别出错.']);
disp([str_err '(正确结果：朝向' standard...
'；识别结果：朝向' identify ')']);
end
end
% 显示识别率
disp(['识别率为：' num2str(length(find(error == 0))/20*100) '%']);
%web browser http://www.matlabsky.com/thread-11193-1-1.html
%%
% <html>
% <table width="656" align="left" > <tr><td align="center"><p><font size="2"><a href="http://video.ourmatlab.com/">Matlab神经网络30个案例分析</a></font></p><p align="left"><font size="2">相关论坛：</font></p><p align="left"><font size="2">《Matlab神经网络30个案例分析》官方网站：<a href="http://video.ourmatlab.com">video.ourmatlab.com</a></font></p><p align="left"><font size="2">Matlab技术论坛：<a href="http://www.matlabsky.com">www.matlabsky.com</a></font></p><p align="left"><font size="2">M</font><font size="2">atlab函数百科：<a href="http://www.mfun.la">www.mfun.la</a></font></p><p align="left"><font size="2">Matlab中文论坛：<a href="http://www.ilovematlab.com">www.ilovematlab.com</a></font></p></td> </tr></table>
% </html>
LVQ神经网络之人脸朝向识别(源程序+图片数据集) （105个子文件） 
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资源评论
黄建hj2019-05-31值得学习学习
qq_380886552019-05-21能运行，完美
