【图像识别】基于LVQ神经网络实现人脸朝向识别附matlab代码 上传版本.zip

%% LVQ神经网络的预测——人脸识别 %% 清除环境变量 clear all clc %% 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 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) '%']);