用Matlab做的人脸识别源代码
基于matlab的人脸识别源代码
function varargout = FR_Processed_histogram(varargin) %这种算法是基于直方图处理的方法%The histogram of image is calculated and then bin formation is done on the%basis of mean of successive graylevels frequencies. The training is done on odd images of 40 subjects (200 images out of 400 images)%The results of the implemented algorithm is 99.75 (recognition fails on image number 4 of subject 17)gui_Singleton = 1;gui_State = struct('gui_Name', mfilename, ...'gui_Singleton', gui_Singleton, ...'gui_OpeningFcn', @FR_Processed_histogram_OpeningFcn.,..'gui_OutputFcn',@FR_Processed_histogram_OutputFcn.,..'gui_LayoutFcn', [] , ... 'gui_Callback', []);if nargin && ischar(varargin{1}) gui_State.gui_Callback =str2func(varargin{1});endif nargout[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});elsegui_mainfcn(gui_State, varargin{:});end% End initialization code - DO NOT EDIT% -------------------------------------------------------------------------% --- Executes just before FR_Processed_histogram is made visible. function FR_Processed_histogram_OpeningFcn(hObjecte, ventdata, handles, varargin)% This function has no output args, see OutputFcn.% hObject handle to figure% eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % varargin command line arguments to FR_Processed_histogram (see VARARGIN)% Choose default command line output forFR_Processed_histogramhandles.output = hObject;% Update handles structure guidata(hObject, handles);% UIWAIT makes FR_Processed_histogram wait for user response(see UIRESUME)% uiwait(handles.figure1);global total_sub train_img sub_img max_hist_level bin_numform_bin_num;total_sub = 40;train_img = 200;sub_img = 10;max_hist_level = 256;bin_num = 9;form_bin_num = 29;% -------------------------------------------------------------------------% --- Outputs from this function are returned to the command line.function varargout = FR_Processed_histogram_OutputFcn(hObject, eventdata, handles)% varargout cell array for returning output args (see VARARGOUT);% hObject handle to figure% eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA)% Get default command line output from handles structurevarargout{1} = handles.output;% -------------------------------------------------------------------------% --- Executes on button press in train_button.function train_button_Callback(hObject, eventdata, handles)% hObject handle to train_button (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA)global train_processed_bin;global total_sub train_img sub_img max_hist_level bin_numform_bin_num;train_processed_bin(form_bin_num,train_img) = 0;K = 1;train_hist_img = zeros(max_hist_level, train_img);for Z=1:1:total_subfor X=1:2:sub_img %%%train on odd number of images of each subjectI = imread( strcat('ORL\S',int2str(Z), '\',int2str(X), '.bmp') ); [rowscols] = size(I);for i=1:1:rowsfor j=1:1:colsif( I(i,j) == 0 ) train_hist_img(max_hist_level, K)train_hist_img(max_hist_level, K) + 1;else train_hist_img(I(i,j), K) = train_hist_img(I(i,j), K) + 1;endendendK = K + 1;endend[r c] = size(train_hist_img);sum = 0;for i=1:1:cK = 1;for j=1:1:rif( (mod(j,bin_num)) == 0 )sum = sum + train_hist_img(j,i);train_processed_bin(K,i) = sum/bin_num; K = K + 1;sum = 0;elsesum = sum + train_hist_img(j,i);endendtrain_processed_bin(K,i) = sum/bin_num;enddisplay ('Training Done') save'train' train_processed_bin;% --- Executes on button press in Testing_button.function Testing_button_Callback(hObject, eventdata, handles)% hObject handle to Testing_button (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) global train_img max_hist_level bin_num form_bin_num;global train_processed_bin;global filename pathname Iload 'train'test_hist_img(max_hist_level) = 0;test_processed_bin(form_bin_num) = 0;[rows cols] = size(I);for i=1:1:rowsfor j=1:1:colsif( I(i,j) == 0 )test_hist_img(max_hist_level)test_hist_img(max_hist_level) + 1;elsetest_hist_img(I(i,j)) = test_hist_img(I(i,j)) + 1;endendend[r c] = size(test_hist_img); sum = 0;K = 1;for j=1:1:cif( (mod(j,bin_num)) == 0 )sum = sum + test_hist_img(j); test_processed_bin(K) =sum/bin_num;K = K + 1;sum = 0;elsesum = sum + test_hist_img(j);endendtest_processed_bin(K) = sum/bin_num;sum = 0;K = 1;for y=1:1:train_imgfor z=1:1:form_bin_numsum = sum + abs( test_processed_bin(z) - train_processed_bin(z,y) );endimg_bin_hist_sum(K,1) = sum;sum = 0;K = K + 1;end[temp M] = min(img_bin_hist_sum);M = ceil(M/5);getString_start=strfind(pathname',S');getString_start=getString_start(end)+1;getString_end=strfind(pathname',\');getString_end=getString_end(end)-1;subjectindex=str2num(pathname(getString_start:getString_end));if (subjectindex == M)axes (handles.axes3)%image no: 5 is shown for visualization purposeimshow(imread(STRCAT('ORL\S',num2str(M),'\5.bmp')))msgbox ( 'Correctly Recognized');elsedisplay ([ 'Error==> Testing Image of Subject >>'num2str(subjectindex) ' matches with the image of subject >> 'num2str(M)])axes (handles.axes3)%image no: 5 is shown for visualization purposeimshow(imread(STRCAT( 'ORL\S' ,num2str(M),'\5.bmp')))msgbox ( 'Incorrectly Recognized');enddisplay('Testing Done')% -------------------------------------------------------------------------function box_Callback(hObject, eventdata, handles)% hObject handle to box (see GCBO)% eventdata reserved - to be defined in a future version ofMATLAB% handles structure with handles and user data (see GUIDATA)% Hints: get(hObject,'String') returns contents of box as text% str2double(get(hObject,'String')) returns contents of box as a double% -------------------------------------------------------------------------% --- Executes during object creation, after setting all properties.function box_CreateFcn(hObject, eventdata, handles)% hObject handle to box (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB % handles empty - handles not created until after all CreateFcns called% Hint: edit controls usually have a white background on Windows.% See ISPC and COMPUTER.if ispc && isequal(get(hObject,'BackgroundColor'),get(0,'defaultUicontrolBackgroundColor'))set(hObject,'BackgroundColor','white');end% --- Executes on button press in Input_Image_button.function Input_Image_button_Callback(hObject, eventdata, handles) % hObject handle to Input_Image_button (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) global filename pathname I[filename, pathname] = uigetfile('*.bmp', 'Test Image');axes(handles.axes1)imgpath=STRCAT(pathname,filename);I = imread(imgpath);imshow(I)% -------------------------------------------------------------------------% --- Executes during object creation, after setting all properties.function axes3_CreateFcn(hObject, eventdata, handles)% hObject handle to axes3 (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles empty - handles not created until after all CreateFcns called% Hint: place code in OpeningFcn to populate axes3%Programmed by Usman Qayyum。
基于肤色的人脸检测matlab代码
基于肤色的人脸检测matlab代码mainclose allclear allclc% 输入图像名字img_name = input('请输入图像名字(图像必须为RGB图像，输入0结束)：','s'); % 当输入0时结束while ~strcmp(img_name,'0')% 进行人脸识别facedetection(img_name);img_name = input('请输入图像名字(图像必须为RGB图像，输入0结束)：','s'); endfacedetectionfunctionfacedetection(img_name)% 读取RGB图像I = imread(img_name);% 转换为灰度图像gray = rgb2gray(I);% 将图像转化为YCbCr颜色空间YCbCr = rgb2ycbcr(I);% 获得图像宽度和高度heigth = size(gray,1);width = size(gray,2);% 根据肤色模型将图像二值化fori = 1:heigthfor j = 1:widthY = YCbCr(i,j,1);Cb = YCbCr(i,j,2);Cr = YCbCr(i,j,3);if(Y < 80)gray(i,j) = 0;elseif(skin(Y,Cb,Cr) == 1)gray(i,j) = 255;elsegray(i,j) = 0;endendendend% 二值图像形态学处理SE=strel('arbitrary',eye(5));%gray = bwmorph(gray,'erode');% imopen先腐蚀再膨胀gray = imopen(gray,SE);% imclose先膨胀再腐蚀%gray = imclose(gray,SE);imshow(gray);% 取出图片中所有包含白色区域的最小矩形[L,num] = bwlabel(gray,8);STATS = regionprops(L,'BoundingBox'); % 存放经过筛选以后得到的所有矩形块n = 1;result = zeros(n,4);figure,imshow(I);hold on;fori = 1:numbox = STATS(i).BoundingBox;x = box(1); %矩形坐标xy = box(2); %矩形坐标yw = box(3); %矩形宽度wh = box(4); %矩形高度h% 宽度和高度的比例ratio = h/w;ux = uint8(x);uy = uint8(y);ifux> 1ux = ux - 1;endifuy> 1uy = uy - 1;end% 可能是人脸区域的矩形应满足以下条件：% 1、高度和宽度必须都大于20，且矩形面积大于400 % 2、高度和宽度比率应该在范围（0.6,2）内% 3、函数findeye返回值为1if w < 20 || h < 20 || w*h < 400continueelseif ratio < 2 && ratio > 0.6 &&findeye(gray,ux,uy,w,h) == 1 % 记录可能为人脸的矩形区域result(n,:) = [uxuy w h];n = n+1;endend% 对可能是人脸的区域进行标记if size(result,1) == 1 && result(1,1) > 0rectangle('Position',[result(1,1),result(1,2),result(1,3),result(1, 4)],'EdgeColor','r'); else% 如果满足条件的矩形区域大于1则再根据其他信息进行筛选for m = 1:size(result,1)m1 = result(m,1);m2 = result(m,2);m3 = result(m,3);m4 = result(m,4);% 标记最终的人脸区域if m1 + m3 < width && m2 + m4 <heigth< p=""> rectangle('Position',[m1,m2,m3,m4],'EdgeColor','r');endendendfindeye% 判断二值图像中是否含有可能是眼睛的块% bImage----二值图像% x---------矩形左上角顶点X坐标% y---------矩形左上角顶点Y坐标% w---------矩形宽度% h---------矩形长度% 如果有则返回值eye等于1，否则为0function eye = findeye(bImage,x,y,w,h)% 根据矩形相关属性得到二值图像中矩形区域中的数据% 存放矩形区域二值图像信息part = zeros(h,w);% 二值化fori = y:(y+h)for j = x:(x+w)ifbImage(i,j) == 0part(i-y+1,j-x+1) = 255;elsepart(i-y+1,j-x+1) = 0;endendend[L,num] = bwlabel(part,8);% 如果区域中有两个以上的矩形则认为有眼睛ifnum< 2eye = 0;elseeye = 1;endskin% Anil K.Jain提出的基于YCbCr颜色空间的肤色模型% 根据当前点的Cb Cr值判断是否为肤色function result = skin(Y,Cb,Cr)% 参数% a = 25.39;a = 28;% b = 14.03;b=18;ecx = 1.60;ecy = 2.41;sita = 2.53;cx = 109.38;cy = 152.02;xishu = [cos(sita) sin(sita);-sin(sita) cos(sita)];% 如果亮度大于230，则将长短轴同时扩大为原来的1.1倍if(Y > 230)a = 1.1*a;b = 1.1*b;end% 根据公式进行计算Cb = double(Cb);Cr = double(Cr);t = [(Cb-cx);(Cr-cy)];temp = xishu*t;value = (temp(1) - ecx)^2/a^2 + (temp(2) - ecy)^2/b^2; % 大于1则不是肤色，返回0；否则为肤色，返回1if value > 1result = 0;elseresult = 1;end</heigth<>。
基于matlab的人脸识别源代码
function varargout = FR_Processed_histogram(varargin) %这种算法是基于直方图处理的方法%The histogram of image is calculated and then bin formation is done on the%basis of mean of successive graylevels frequencies. The training is done on odd images of 40 subjects (200 images out of 400 images)%The results of the implemented algorithm is 99.75 (recognition fails on image number 4 of subject 17)gui_Singleton = 1;gui_State = struct('gui_Name', mfilename, ...'gui_Singleton', gui_Singleton, ...'gui_OpeningFcn', @FR_Processed_histogram_OpeningFcn.,..'gui_OutputFcn',@FR_Processed_histogram_OutputFcn.,..'gui_LayoutFcn', [] , ... 'gui_Callback', []);if nargin && ischar(varargin{1}) gui_State.gui_Callback =str2func(varargin{1});endif nargout[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});elsegui_mainfcn(gui_State, varargin{:});end% End initialization code - DO NOT EDIT% -------------------------------------------------------------------------% --- Executes just before FR_Processed_histogram is made visible. function FR_Processed_histogram_OpeningFcn(hObjecte, ventdata, handles, varargin)% This function has no output args, see OutputFcn.% hObject handle to figure% eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) % varargin command line arguments to FR_Processed_histogram (see VARARGIN)% Choose default command line output forFR_Processed_histogramhandles.output = hObject;% Update handles structure guidata(hObject, handles);% UIWAIT makes FR_Processed_histogram wait for user response(see UIRESUME)% uiwait(handles.figure1);global total_sub train_img sub_img max_hist_level bin_numform_bin_num;total_sub = 40;train_img = 200;sub_img = 10;max_hist_level = 256;bin_num = 9;form_bin_num = 29;% -------------------------------------------------------------------------% --- Outputs from this function are returned to the command line.function varargout = FR_Processed_histogram_OutputFcn(hObject, eventdata, handles)% varargout cell array for returning output args (see VARARGOUT);% hObject handle to figure% eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA)% Get default command line output from handles structurevarargout{1} = handles.output;% -------------------------------------------------------------------------% --- Executes on button press in train_button.function train_button_Callback(hObject, eventdata, handles)% hObject handle to train_button (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA)global train_processed_bin;global total_sub train_img sub_img max_hist_level bin_numform_bin_num;train_processed_bin(form_bin_num,train_img) = 0;K = 1;train_hist_img = zeros(max_hist_level, train_img);for Z=1:1:total_subfor X=1:2:sub_img %%%train on odd number of images of each subjectI = imread( strcat('ORL\S',int2str(Z), '\',int2str(X), '.bmp') ); [rowscols] = size(I);for i=1:1:rowsfor j=1:1:colsif( I(i,j) == 0 ) train_hist_img(max_hist_level, K)train_hist_img(max_hist_level, K) + 1;else train_hist_img(I(i,j), K) = train_hist_img(I(i,j), K) + 1;endendendK = K + 1;endend[r c] = size(train_hist_img);sum = 0;for i=1:1:cK = 1;for j=1:1:rif( (mod(j,bin_num)) == 0 )sum = sum + train_hist_img(j,i);train_processed_bin(K,i) = sum/bin_num; K = K + 1;sum = 0;elsesum = sum + train_hist_img(j,i);endendtrain_processed_bin(K,i) = sum/bin_num;enddisplay ('Training Done') save'train' train_processed_bin;% --- Executes on button press in Testing_button.function Testing_button_Callback(hObject, eventdata, handles)% hObject handle to Testing_button (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) global train_img max_hist_level bin_num form_bin_num;global train_processed_bin;global filename pathname Iload 'train'test_hist_img(max_hist_level) = 0;test_processed_bin(form_bin_num) = 0;[rows cols] = size(I);for i=1:1:rowsfor j=1:1:colsif( I(i,j) == 0 )test_hist_img(max_hist_level)test_hist_img(max_hist_level) + 1;elsetest_hist_img(I(i,j)) = test_hist_img(I(i,j)) + 1;endendend[r c] = size(test_hist_img); sum = 0;K = 1;for j=1:1:cif( (mod(j,bin_num)) == 0 )sum = sum + test_hist_img(j); test_processed_bin(K) =sum/bin_num;K = K + 1;sum = 0;elsesum = sum + test_hist_img(j);endendtest_processed_bin(K) = sum/bin_num;sum = 0;K = 1;for y=1:1:train_imgfor z=1:1:form_bin_numsum = sum + abs( test_processed_bin(z) - train_processed_bin(z,y) );endimg_bin_hist_sum(K,1) = sum;sum = 0;K = K + 1;end[temp M] = min(img_bin_hist_sum);M = ceil(M/5);getString_start=strfind(pathname',S');getString_start=getString_start(end)+1;getString_end=strfind(pathname',\');getString_end=getString_end(end)-1;subjectindex=str2num(pathname(getString_start:getString_end));if (subjectindex == M)axes (handles.axes3)%image no: 5 is shown for visualization purposeimshow(imread(STRCAT('ORL\S',num2str(M),'\5.bmp')))msgbox ( 'Correctly Recognized');elsedisplay ([ 'Error==> Testing Image of Subject >>'num2str(subjectindex) ' matches with the image of subject >> 'num2str(M)])axes (handles.axes3)%image no: 5 is shown for visualization purposeimshow(imread(STRCAT( 'ORL\S' ,num2str(M),'\5.bmp')))msgbox ( 'Incorrectly Recognized');enddisplay('Testing Done')% -------------------------------------------------------------------------function box_Callback(hObject, eventdata, handles)% hObject handle to box (see GCBO)% eventdata reserved - to be defined in a future version ofMATLAB% handles structure with handles and user data (see GUIDATA)% Hints: get(hObject,'String') returns contents of box as text% str2double(get(hObject,'String')) returns contents of box as a double% -------------------------------------------------------------------------% --- Executes during object creation, after setting all properties.function box_CreateFcn(hObject, eventdata, handles)% hObject handle to box (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB % handles empty - handles not created until after all CreateFcns called% Hint: edit controls usually have a white background on Windows.% See ISPC and COMPUTER.if ispc && isequal(get(hObject,'BackgroundColor'),get(0,'defaultUicontrolBackgroundColor'))set(hObject,'BackgroundColor','white');end% --- Executes on button press in Input_Image_button.function Input_Image_button_Callback(hObject, eventdata, handles) % hObject handle to Input_Image_button (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) global filename pathname I[filename, pathname] = uigetfile('*.bmp', 'Test Image');axes(handles.axes1)imgpath=STRCAT(pathname,filename);I = imread(imgpath);imshow(I)% -------------------------------------------------------------------------% --- Executes during object creation, after setting all properties.function axes3_CreateFcn(hObject, eventdata, handles)% hObject handle to axes3 (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles empty - handles not created until after all CreateFcns called% Hint: place code in OpeningFcn to populate axes3%Programmed by Usman Qayyum。
MATLAB-高级编程与工程应用-人脸识别-实验报告+源代码
MATLAB高级编程与工程应用实验四图像处理第一章基础知识1、MATLAB 提供了图像处理工具箱，在命令窗口输入help images 可查看该工具箱内的所有函数。
请阅读并大致了解这些函数的基本功能。
大致了解。
2、利用MATLAB 提供的Image file I/O 函数分别完成以下处理：(a)以测试图像的中心为圆心，图像的长和宽中较小值的一半为半径画一个红颜色的圆；分析：直接利用半径条件，满足条件的点将红色元素置为255，绿色和蓝色元素置为0，于是得到如下图像：源代码：load('hall_color.mat');%首先获得三原数组R = hall_color(:,:,1);G = hall_color(:,:,2);B = hall_color(:,:,3);%将圆上的点改为红色for i = 1:120for j = 1:168a = abs(i - 60.5);b = abs(j - 84.5);d = sqrt(a ^ 2 + b ^ 2);if(abs(d - 60) < 0.5)R(i,j) = 255;G(i,j) = 0;B(i,j) = 0;endendend%生成新的矩阵hall_color1(:,:,1) = R;hall_color1(:,:,2) = G;hall_color1(:,:,3) = B;imshow(hall_color1);(b)将测试图像涂成国际象棋状的“黑白格”的样子，其中“黑”即黑色，“白”则意味着保留原图。
用一种看图软件浏览上述两个图，看是否达到了目标。
分析：首先设置标记flag在进行循环，对不同方格实行颜色更改就行。
效果：源代码：clear all;load('hall_color.mat');R = hall_color(:,:,1);G = hall_color(:,:,2);B = hall_color(:,:,3);flag = 1;for i = 1:8flag = mod((flag + 1),2);for j = 1:8if(flag == 1)for m = 15*(i - 1) + 1:15*ifor n = 21*(j - 1) + 1:21*jR(m,n) = 0;G(m,n) = 0;B(m,n) = 0;endendendflag = mod((flag + 1),2);endendword格式-可编辑-感谢下载支持hall_color1(:,:,1) = R;hall_color1(:,:,2) = G;hall_color1(:,:,3) = B;imshow(hall_color1);用看图软件打开成功：第二章图像压缩编码1、图像的预处理是将每个像素灰度值减去128 ，这个步骤是否可以在变换域进行？请在测试图像中截取一块验证你的结论。
基于MATLAB的人脸识别源程序
基于MATLA酌人脸识别源程序1•色彩空间转换function [r,g]=rgb_RGB(Ori_Face)R=0ri_Face(:,:,1);G=0ri_Face(:,:,2);B=Ori_Face(:,:,3);R1=im2double(R); % 将uint8 型转换成double型G1=im2double(G);B1=im2double(B);RGB=R1+G1+B1;row=size(Ori_Face, 1); %行像素column=size(Ori_Face,2); %列像素for i=1:rowfor j=1:columnrr(i,j)=R1(i,j)/RGB(i,j);gg(i,j)=G1(i,j)/RGB(i,j);endendrrr=mean(rr);r=mean(rrr);ggg=mean(gg);g=mean(ggg); 2•均值和协方差皮肤库\2・jpg');[r2,g2]=rgb_RGB(t2);皮肤库\3・jpg');[r3,g3]=rgb_RGB(t3);皮肤库\4・jpg');[r4,g4]=rgb_RGB(t4);皮肤库\5・jpg');[r5,g5]=rgb_RGB(t5);皮肤库\6・jpg');[r6,g6]=rgb_RGB(t6);皮肤库\7・jpg');[r7,g7]=rgb_RGB(t7);皮肤库\8・jpg');[r8,g8]=rgb_RGB(t8);皮肤库\9・jpg');[r9,g9]=rgb_RGB(t9);皮肤库\10・jpg');[r10,g10]=rgb_RGB(t10); 皮肤库\11・jpg');[r11,g11]=rgb_RGB(t11); 皮肤库\12・jpg');[r12,g12]=rgb_RGB(t12); 皮肤库\13・jpg');[r13,g13]=rgb_RGB(t13); 皮肤库\14・jpg');[r14,g14]=rgb_RGB(t14); 皮肤库\15・jpg');[r15,g15]=rgb_RGB(t15); 皮肤库\16・jpg');[r16,g16]=rgb_RGB(t16); 皮肤库\17・jpg');[r17,g17]=rgb_RGB(t17); 皮肤库\18・jpg');[r18,g18]=rgb_RGB(t18); 皮肤库\19・jpg');[r19,g19]=rgb_RGB(t19); 皮肤库\20・jpg');[r20,g20]=rgb_RGB(t20); 皮肤库\21・jpg');[r21,g21]=rgb_RGB(t21);皮肤库\24・jpg');[r24,g24]=rgb_RGB(t24);皮肤库\25・jpg');[r25,g25]=rgb_RGB(t25);皮肤库\26・jpg');[r26,g26]=rgb_RGB(t26);皮肤库\27・jpg');[r27,g27]=rgb_RGB(t27); r=cat(1,r1,r2,r3,r4,r5,r6,r7,r8,r9,r10,r11,r12,r13,r14,r15,r16,r17,r18, r19,r20,r21,r22,r23,r24,r25,r26,r27);g=cat(1,g1,g2,g3,g4,g5,g6,g7,g8,g9,g10,g11,g12,g13,g14,g15,g16,g17,g1 8,g19,g20,g21,g22,g23,g24,g25,g26,g27);m=mean([r,g])n=cov([r,g])3•求质心function [xmean, ymean] = center(bw)bw=bwfill(bw,'holes');area = bwarea(bw);[m n] =size(bw);bw=double(bw);xmean =0; ymean = 0;for i=1:m,for j=1:n,xmean = xmean + j*bw(i,j);ymean = ymean + i*bw(i,j);end;end;if(area==0)xmean=0;ymean=0;elsexmean = xmean/area;ymean = ymean/area;xmean = round(xmean);ymean = round(ymean);end4.求偏转角度function [theta] = orient(bw,xmean,ymean) [m n] =size(bw); bw=double(bw);a = 0;b = 0;c = 0;for i=1:m,for j=1:n,a = a + (j - xmean)A2 * bw(i,j);b = b + (j - xmean) * (i - ymean) * bw(i,j);c = c + (i - ymean)A2 * bw(i,j);end;end;b = 2 * b;theta = atan(b/(a-c))/2;theta = theta*(180/pi); % 从幅度转换到角度5.找区域边界function [left, right, up, down] = bianjie(A)[m n] = size(A);left = -1;right = -1;up = -1;down = -1;for j=1:n,for i=1:m,if (A(i,j) ~= 0)left = j;break;end;end;if (left ~= -1) break;end;end;for j=n:-1:1,for i=1:m,if (A(i,j) ~= 0)right = j;break;end;end;if (right ~= -1) break; end;end;for i=1:m,for j=1:n,if (A(i,j) ~= 0)up = i;break;end;end;if (up ~= -1) break;end;end;for i=m:-1:1,for j=1:n,if (A(i,j) ~= 0)down = i;break;end;end;if (down ~= -1)break;end;end;6.求起始坐标function newcoord = checklimit(coord,maxval)newcoord = coord;if (newcoord<1)newcoord=1;end;if (newcoord>maxval)newcoord=maxval;end;7•模板匹配function [ccorr, mfit, RectCoord]mobanpipei(mult. frontalmodel,ly,wx,cx, cy, angle) frontalmodel=rgb2gray(frontalmodel);model_rot = imresize(frontalmodel,[ly wx],'bilinear'); % 调整模板大小model_rot = imrotate(model_rot,angle,'bilinear'); %旋转模板[l,r,u,d] = bianjie(model_rot); % 求边界坐标bwmodel_rot=imcrop(model_rot,[l u (r-l) (d-u)]); % 选择模板人脸区域[modx,mody] =center(bwmodel_rot); % 求质心[morig, norig] = size(bwmodel_rot);%产生一个覆盖了人脸模板的灰度图像mfit = zeros(size(mult));mfitbw = zeros(size(mult));[limy, limx] = size(mfit);%计算原图像中人脸模板的坐标startx = cx-modx;starty = cy-mody;endx = startx + norig-1;endy = starty + morig-1;startx = checklimit(startx,limx);starty = checklimit(starty,limy);endx = checklimit(endx,limx);endy = checklimit(endy,limy);for i=starty:endy,for j=startx:endx,mfit(i,j) = model_rot(i-starty+1,j-startx+1);end;end;ccorr = corr2(mfit,mult) % 计算相关度[l,r,u,d] = bianjie(bwmodel_rot);sx = startx+l;sy = starty+u;RectCoord = [sx sy (r-1) (d-u)]; % 产生矩形坐标8.主程序clear;[fname,pname]=uigetfile({'*・jpg';'* .bmp';'* ・tif';'* ・gif'},'Please choose a color picture・・・')；%返回打开的图片名与图片路径名[u,v]=size(fname);y=fname(v); % 图片格式代表值switch ycase 0errordlg('You Should Load Image File First ・・・','Warning ・・・');case{'g';'G';'p';'P';'f';'F'}; % 图片格式若是JPG/jpg、BMP/bmp、TIF/tif 或者GIF/gif，才打开I=cat(2,pname,fname);Ori_Face=imread(l);subplot(2,3,1),imshow(Ori_Face);otherwiseerrordlg('You Should Load Image File First ・・・','Warning ・・・'); endR=Ori_Face(:,:,1);G=Ori_Face(:,:,2);B=Ori_Face(:,:,3);R1=im2double(R); % 将uint8 型转换成double型处理G1=im2double(G);B1=im2double(B);RGB=R1+G1+B1;m=[ 0・4144,0.3174]; % 均值n=[0・0031,-0・0004;-0・0004,0.0003]; % 方差row=size(Ori_Face,1); % 行像素数column=size(Ori_Face,2); % 列像素数for i=1:rowfor j=1:columnif RGB(i,j)==0rr(i,j)=0;gg(i,j)=0;elserr(i,j)=R1(i,j)/RGB(i,j);% rgb归一化gg(i,j)=G1(i,j)/RGB(i,j);x=[rr(i,j),gg(i,j)];p(i,j)=exp((-0・5)*(x-m)*inv(n)*(x-m)'); % 皮肤概率服从高斯分布endendendsubplot(2,3,2);imshow(p); % 显示皮肤灰度图像low_pass=1/9*ones(3);image_low=filter2(low_pass, p); % 低通滤波去噪声subplot(2,3,3);imshow(image」ow);%自适应阀值程序previousSkin2 = zeros(i,j);changelist =[];for threshold = 0・55:-0.1:0.05two_value = zeros(i,j);two_value(find(image_low>threshold)) = 1;change = sum(sum(two_value - previousSkin2));changelist = [changelist change];previousSkin2 = two_value;end[C, I] = min(changelist);optimalThreshold = (7-1)* 0.1two_value = zeros(i,j);two_value(find(image_low>optimalThreshold))= 1; %二值化subplot(2,3,4);imshow(two_value); % 显示二值图像我的照片人脸模板.jpg'); %读入人脸模板照片FaceCoord=[|;imsourcegray=rgb2gray(Ori_Face); % 将原照片转换为灰度图像[L,N]=bwlabel(two_value,8); % 标注二值图像中连接的部分丄为数据矩阵，N为颗粒的个数for i=1:N,[x,y]=find(bwlabel(two_value)==i); % 寻找矩阵中标号为i 的行和列的下标bwsegment = bwselect(two_value,y,x,8); % 选择出第i 个颗粒numholes = 1-bweuler(bwsegment,4); % 计算此区域的空洞数if (numholes >= 1) % 若此区域至少包含一个洞，则将其选出进行下一步运算RectCoord = -1;[m n] = size(bwsegment);[cx,cy]=center(bwsegment);%求此区域的质心bwnohole=bwfill(bwsegment,'holes'); % 将洞圭寸住(将灰度值赋为1)justface = uint8(double(bwnohole)・* double(imsourcegray));%只在原照片的灰度图像中保留该候选区域angle = orient(bwsegment,cx,cy); %求此区域的偏转角度bw = imrotate(bwsegment, angle, 'bilinear');bw = bwfill(bw,'holes');[l,r,u,d] =bianjie(bw);wx = (r - l +1); % 宽度ly = (d - u + 1); % 高度wratio = ly/wx % 高宽比if ((0 ・8v=wratio)&(wratiov=2))%如果目标区域的高度/宽度比例大于0・8且小于2.0,则将其选出进行下一步运算S=ly*wx; %计算包含此区域矩形的面积A=bwarea(bwsegment); %计算此区域面积if (A/S>0.35)[ccorr,mfit, RectCoord] =mobanpipei(justface,frontalmodel,ly,wx, cx,cy, angle);end if (ccorr>=0.6)mfitbw=(mfit>=1);invbw = xor(mfitbw,ones(size(mfitbw)));source_with_hole = uint8(double(invbw) double(imsourcegray));final_image = uint8(double(source_with_hole) double(mfit));subplot(2,3,5);imshow(final_image); % 显示覆盖了模板脸的灰度图像imsourcegray = final_image;subplot(2,3,6);imshow(Ori_Face); % 显示检测效果图end;if (RectCoord ~= -1)FaceCoord = [FaceCoord; RectCoord];endendendend%在认为是人脸的区域画矩形[numfaces x] = size(FaceCoord);for i=1:numfaces,hd = rectangle('Position',FaceCoord(i,:));set(hd, 'edgecolor', 'y');end人脸检测是人脸识别、人机交互、智能视觉监控等工作的前提。
人脸检测matlab代码
round(linspace(1, size(I, 2), 10)));
mesh(yt, xt, zeros(size(xt)), 'FaceColor', ...
'None', 'LineWidth', 2, ...
subplot(1, 2, 2);mesh(p);title('实际肤色分布');
if ndims(Img) == 3
I=rgb2gray(Img);
else
I = Img;
end
J=imnoise(I,'salt & pepper',0.01);
I1=filter2(fspecial('average',3),J,'full')/255;
G1=im2double(G);
B1=im2double(B);
RGB=R1+G1+B1;
m=[ 0.4144,0.3174]; % 均值
n=[0.0031,-0.0004;-0.0004,0.0003]; % 方差
[x1,y1]=meshgrid(0:0.01:1,0:0.01:1);
'EdgeColor', 'b');
subplot(2, 4, 3); imshow(p);title('基于肤色概率分布的灰度图像');
subplot(2, 4, 4); imshow(I1);title('邻域平均法滤波后图像');
基于MATLAB的人脸识别源程序
1.色彩空间转换function ［r,g］=rgb_RGB（Ori_Face）R=Ori_Face（：,：,1);G=Ori_Face(:,：，2）；B=Ori_Face（：,:，3）;R1=im2double（R); ％将uint8型转换成double型G1=im2double（G）;B1=im2double（B)；RGB=R1+G1+B1;row=size(Ori_Face,1)；％行像素column=size（Ori_Face,2）; ％列像素for i=1:rowfor j=1：columnrr(i，j）=R1（i,j)/RGB(i，j)；gg（i,j)=G1（i，j)/RGB（i，j);endendrrr=mean(rr）；r=mean(rrr)；ggg=mean（gg）；g=mean(ggg);2.均值和协方差t1=imread('D：\matlab\皮肤库\1.jpg'）;[r1，g1]=rgb_RGB（t1）;t2=imread（’D:\matlab\皮肤库\2。
jpg'）；[r2,g2]=rgb_RGB(t2)；t3=imread（’D：\matlab\皮肤库\3。
jpg'）;［r3,g3］=rgb_RGB（t3）; t4=imread(’D:\matlab\皮肤库\4。
jpg'）；［r4，g4］=rgb_RGB(t4）;t5=imread(’D：\matlab\皮肤库\5。
jpg');[r5，g5］=rgb_RGB(t5）；t6=imread('D:\matlab\皮肤库\6.jpg');［r6,g6]=rgb_RGB(t6);t7=imread（'D:\matlab\皮肤库\7.jpg'）；［r7，g7］=rgb_RGB(t7）;t8=imread（'D：\matlab\皮肤库\8.jpg'）;[r8,g8]=rgb_RGB（t8）；t9=imread('D：\matlab\皮肤库\9.jpg'）；［r9,g9］=rgb_RGB（t9)；t10=imread（'D：\matlab\皮肤库\10。
(完整版)人脸识别MATLAB代码
1.色彩空间转换function [r,g]=rgb_RGB(Ori_Face)R=Ori_Face(:,:,1);G=Ori_Face(:,:,2);B=Ori_Face(:,:,3);R1=im2double(R); % 将uint8型转换成double型G1=im2double(G);B1=im2double(B);RGB=R1+G1+B1;row=size(Ori_Face,1); % 行像素column=size(Ori_Face,2); % 列像素for i=1:rowfor j=1:columnrr(i,j)=R1(i,j)/RGB(i,j);gg(i,j)=G1(i,j)/RGB(i,j);endendrrr=mean(rr);r=mean(rrr);ggg=mean(gg);g=mean(ggg);2.均值和协方差t1=imread('D:\matlab\皮肤库\1.jpg');[r1,g1]=rgb_RGB(t1); t2=imread('D:\matlab\皮肤库\2.jpg');[r2,g2]=rgb_RGB(t2); t3=imread('D:\matlab\皮肤库\3.jpg');[r3,g3]=rgb_RGB(t3); t4=imread('D:\matlab\皮肤库\4.jpg');[r4,g4]=rgb_RGB(t4); t5=imread('D:\matlab\皮肤库\5.jpg');[r5,g5]=rgb_RGB(t5); t6=imread('D:\matlab\皮肤库\6.jpg');[r6,g6]=rgb_RGB(t6); t7=imread('D:\matlab\皮肤库\7.jpg');[r7,g7]=rgb_RGB(t7); t8=imread('D:\matlab\皮肤库\8.jpg');[r8,g8]=rgb_RGB(t8);t9=imread('D:\matlab\皮肤库\9.jpg');[r9,g9]=rgb_RGB(t9);t10=imread('D:\matlab\皮肤库\10.jpg');[r10,g10]=rgb_RGB(t10);t11=imread('D:\matlab\皮肤库\11.jpg');[r11,g11]=rgb_RGB(t11);t12=imread('D:\matlab\皮肤库\12.jpg');[r12,g12]=rgb_RGB(t12);t13=imread('D:\matlab\皮肤库\13.jpg');[r13,g13]=rgb_RGB(t13);t14=imread('D:\matlab\皮肤库\14.jpg');[r14,g14]=rgb_RGB(t14);t15=imread('D:\matlab\皮肤库\15.jpg');[r15,g15]=rgb_RGB(t15);t16=imread('D:\matlab\皮肤库\16.jpg');[r16,g16]=rgb_RGB(t16);t17=imread('D:\matlab\皮肤库\17.jpg');[r17,g17]=rgb_RGB(t17);t18=imread('D:\matlab\皮肤库\18.jpg');[r18,g18]=rgb_RGB(t18);t19=imread('D:\matlab\皮肤库\19.jpg');[r19,g19]=rgb_RGB(t19);t20=imread('D:\matlab\皮肤库\20.jpg');[r20,g20]=rgb_RGB(t20);t21=imread('D:\matlab\皮肤库\21.jpg');[r21,g21]=rgb_RGB(t21);t22=imread('D:\matlab\皮肤库\22.jpg');[r22,g22]=rgb_RGB(t22);t23=imread('D:\matlab\皮肤库\23.jpg');[r23,g23]=rgb_RGB(t23);t24=imread('D:\matlab\皮肤库\24.jpg');[r24,g24]=rgb_RGB(t24);t25=imread('D:\matlab\皮肤库\25.jpg');[r25,g25]=rgb_RGB(t25);t26=imread('D:\matlab\皮肤库\26.jpg');[r26,g26]=rgb_RGB(t26);t27=imread('D:\matlab\皮肤库\27.jpg');[r27,g27]=rgb_RGB(t27);r=cat(1,r1,r2,r3,r4,r5,r6,r7,r8,r9,r10,r11,r12,r13,r14,r15,r16,r17,r18,r19,r20,r21,r22, r23,r24,r25,r26,r27);g=cat(1,g1,g2,g3,g4,g5,g6,g7,g8,g9,g10,g11,g12,g13,g14,g15,g16,g17,g18,g19,g20 ,g21,g22,g23,g24,g25,g26,g27);m=mean([r,g])n=cov([r,g])3.求质心function [xmean, ymean] = center(bw)bw=bwfill(bw,'holes');area = bwarea(bw);[m n] =size(bw);bw=double(bw);xmean =0; ymean = 0;for i=1:m,for j=1:n,xmean = xmean + j*bw(i,j);ymean = ymean + i*bw(i,j);end;end;if(area==0)xmean=0;ymean=0;elsexmean = xmean/area;ymean = ymean/area;xmean = round(xmean);ymean = round(ymean);end4. 求偏转角度function [theta] = orient(bw,xmean,ymean) [m n] =size(bw);bw=double(bw);a = 0;b = 0;c = 0;for i=1:m,for j=1:n,a = a + (j - xmean)^2 * bw(i,j);b = b + (j - xmean) * (i - ymean) * bw(i,j);c = c + (i - ymean)^2 * bw(i,j);end;b = 2 * b;theta = atan(b/(a-c))/2;theta = theta*(180/pi); % 从幅度转换到角度5. 找区域边界function [left, right, up, down] = bianjie(A)[m n] = size(A);left = -1;right = -1;up = -1;down = -1;for j=1:n,for i=1:m,if (A(i,j) ~= 0)left = j;break;end;end;if (left ~= -1) break;end;end;for j=n:-1:1,for i=1:m,if (A(i,j) ~= 0)right = j;break;end;end;if (right ~= -1) break;end;for i=1:m,for j=1:n,if (A(i,j) ~= 0)up = i;break;end;end;if (up ~= -1)break;end;end;for i=m:-1:1,for j=1:n,if (A(i,j) ~= 0)down = i;break;end;end;if (down ~= -1)break;end;end;6. 求起始坐标function newcoord = checklimit(coord,maxval) newcoord = coord;if (newcoord<1)newcoord=1;end;if (newcoord>maxval)newcoord=maxval;end;7.模板匹配function [ccorr, mfit, RectCoord] = mobanpipei(mult, frontalmodel,ly,wx,cx, cy, angle)frontalmodel=rgb2gray(frontalmodel);model_rot = imresize(frontalmodel,[ly wx],'bilinear'); % 调整模板大小model_rot = imrotate(model_rot,angle,'bilinear'); % 旋转模板[l,r,u,d] = bianjie(model_rot); % 求边界坐标bwmodel_rot=imcrop(model_rot,[l u (r-l) (d-u)]); % 选择模板人脸区域[modx,mody] =center(bwmodel_rot); % 求质心[morig, norig] = size(bwmodel_rot);% 产生一个覆盖了人脸模板的灰度图像mfit = zeros(size(mult));mfitbw = zeros(size(mult));[limy, limx] = size(mfit);% 计算原图像中人脸模板的坐标startx = cx-modx;starty = cy-mody;endx = startx + norig-1;endy = starty + morig-1;startx = checklimit(startx,limx);starty = checklimit(starty,limy);endx = checklimit(endx,limx);endy = checklimit(endy,limy);for i=starty:endy,for j=startx:endx,mfit(i,j) = model_rot(i-starty+1,j-startx+1);end;end;ccorr = corr2(mfit,mult) % 计算相关度[l,r,u,d] = bianjie(bwmodel_rot);sx = startx+l;sy = starty+u;RectCoord = [sx sy (r-1) (d-u)]; % 产生矩形坐标8.主程序clear;[fname,pname]=uigetfile({'*.jpg';'*.bmp';'*.tif';'*.gif'},'Please choose a color picture...'); % 返回打开的图片名与图片路径名[u,v]=size(fname);y=fname(v); % 图片格式代表值switch ycase 0errordlg('You Should Load Image File First...','Warning...');case{'g';'G';'p';'P';'f';'F'}; % 图片格式若是JPG/jpg、BMP/bmp、TIF/tif 或者GIF/gif，才打开I=cat(2,pname,fname);Ori_Face=imread(I);subplot(2,3,1),imshow(Ori_Face);otherwiseerrordlg('You Should Load Image File First...','Warning...');endR=Ori_Face(:,:,1);G=Ori_Face(:,:,2);B=Ori_Face(:,:,3);R1=im2double(R); % 将uint8型转换成double型处理G1=im2double(G);B1=im2double(B);RGB=R1+G1+B1;m=[ 0.4144,0.3174]; % 均值n=[0.0031,-0.0004;-0.0004,0.0003]; % 方差row=size(Ori_Face,1); % 行像素数column=size(Ori_Face,2); % 列像素数for i=1:rowfor j=1:columnif RGB(i,j)==0rr(i,j)=0;gg(i,j)=0;elserr(i,j)=R1(i,j)/RGB(i,j); % rgb归一化gg(i,j)=G1(i,j)/RGB(i,j);x=[rr(i,j),gg(i,j)];p(i,j)=exp((-0.5)*(x-m)*inv(n)*(x-m)'); % 皮肤概率服从高斯分布endendendsubplot(2,3,2);imshow(p); % 显示皮肤灰度图像low_pass=1/9*ones(3);image_low=filter2(low_pass, p); % 低通滤波去噪声subplot(2,3,3);imshow(image_low);% 自适应阀值程序previousSkin2 = zeros(i,j);changelist = [];for threshold = 0.55:-0.1:0.05two_value = zeros(i,j);two_value(find(image_low>threshold)) = 1;change = sum(sum(two_value - previousSkin2));changelist = [changelist change];previousSkin2 = two_value;end[C, I] = min(changelist);optimalThreshold = (7-I)*0.1two_value = zeros(i,j);two_value(find(image_low>optimalThreshold)) = 1; % 二值化subplot(2,3,4);imshow(two_value); % 显示二值图像frontalmodel=imread('E:\我的照片\人脸模板.jpg'); % 读入人脸模板照片FaceCoord=[];imsourcegray=rgb2gray(Ori_Face); % 将原照片转换为灰度图像[L,N]=bwlabel(two_value,8); % 标注二值图像中连接的部分,L为数据矩阵，N为颗粒的个数for i=1:N,[x,y]=find(bwlabel(two_value)==i); % 寻找矩阵中标号为i的行和列的下标bwsegment = bwselect(two_value,y,x,8); % 选择出第i个颗粒numholes = 1-bweuler(bwsegment,4); % 计算此区域的空洞数if (numholes >= 1) % 若此区域至少包含一个洞，则将其选出进行下一步运算RectCoord = -1;[m n] = size(bwsegment);[cx,cy]=center(bwsegment); % 求此区域的质心bwnohole=bwfill(bwsegment,'holes'); % 将洞封住（将灰度值赋为1）justface = uint8(double(bwnohole) .* double(imsourcegray));% 只在原照片的灰度图像中保留该候选区域angle = orient(bwsegment,cx,cy); % 求此区域的偏转角度bw = imrotate(bwsegment, angle, 'bilinear');bw = bwfill(bw,'holes');[l,r,u,d] =bianjie(bw);wx = (r - l +1); % 宽度ly = (d - u + 1); % 高度wratio = ly/wx % 高宽比if ((0.8<=wratio)&(wratio<=2))% 如果目标区域的高度/宽度比例大于0.8且小于2.0，则将其选出进行下一步运算S=ly*wx; % 计算包含此区域矩形的面积A=bwarea(bwsegment); % 计算此区域面积if (A/S>0.35)[ccorr,mfit, RectCoord] = mobanpipei(justface,frontalmodel,ly,wx, cx,cy, angle);endif (ccorr>=0.6)mfitbw=(mfit>=1);invbw = xor(mfitbw,ones(size(mfitbw)));source_with_hole = uint8(double(invbw) .* double(imsourcegray));final_image = uint8(double(source_with_hole) + double(mfit));subplot(2,3,5);imshow(final_image); % 显示覆盖了模板脸的灰度图像imsourcegray = final_image;subplot(2,3,6);imshow(Ori_Face); % 显示检测效果图end;if (RectCoord ~= -1)FaceCoord = [FaceCoord; RectCoord];endendendend% 在认为是人脸的区域画矩形[numfaces x] = size(FaceCoord);for i=1:numfaces,hd = rectangle('Position',FaceCoord(i,:));set(hd, 'edgecolor', 'y');end人脸检测是人脸识别、人机交互、智能视觉监控等工作的前提。
肤色分割人脸检测matlab代码
image = imread('im.jpg');figure,imshow(image);red = double(image(:,:,1));green = double(image(:,:,2));blue = double(image(:,:,3));[m n]=size(red);Y = zeros(m,n);Cb = zeros(m,n);Cr = zeros(m,n);I = zeros(m,n);Q = zeros(m,n);red_gama = zeros(m,n);green_gama = zeros(m,n);blue_gama = zeros(m,n);for i=1:m %gamma矫正for j=1:nif red(i,j)>0 && red(i,j)<90fai=pi*red(i,j)/180;gama=1+0.5*cos(fai);red_gama(i,j)=255*(red(i,j)/255)^(1/gama);elseif red(i,j)>=90 && red(i,j)<=170fai=pi/2;gama=1+0.5*cos(fai);red_gama(i,j)=255*(red(i,j)/255)^(1/gama);elseif red(i,j)>170 && red(i,j)<=255fai=pi-pi*(255-red(i,j))/170;gama=1+0.5*cos(fai);red_gama(i,j)=255*(red(i,j)/255)^(1/gama);endif green(i,j)>0 && green(i,j)<90fai=pi*green(i,j)/180;gama=1+0.5*cos(fai);green_gama(i,j)=255*(green(i,j)/255)^(1/gama);elseif green(i,j)>=90 && green(i,j)<=170fai=pi/2;gama=1+0.5*cos(fai);green_gama(i,j)=255*(green(i,j)/255)^(1/gama);elseif green(i,j)>170 && green(i,j)<=255fai=pi-pi*(255-green(i,j))/170;gama=1+0.5*cos(fai);green_gama(i,j)=255*(green(i,j)/255)^(1/gama);endif blue(i,j)>0 && blue(i,j)<90fai=pi*blue(i,j)/180;gama=1+0.5*cos(fai);blue_gama(i,j)=255*(blue(i,j)/255)^(1/gama);elseif blue(i,j)>=90 && blue(i,j)<=170fai=pi/2;gama=1+0.5*cos(fai);blue_gama(i,j)=255*(blue(i,j)/255)^(1/gama);elseif blue(i,j)>170 && blue(i,j)<=255fai=pi-pi*(255-blue(i,j))/170;gama=1+0.5*cos(fai);blue_gama(i,j)=255*(blue(i,j)/255)^(1/gama);endendendfor i=1:mfor j=1:nY(i,j)=0.2989*red_gama(i,j)+0.5866*green_gama(i,j)+0.1145*blue_gama(i,j) ;Cb(i,j)=-0.1688*red_gama(i,j)-0.3312*green_gama(i,j)+0.5000*blue_gama(iCr(i,j)=0.5000*red_gama(i,j)-0.4184*green_gama(i,j)-0.0817*blue_gama(i,j) ;endendemp=zeros(m,n);sita=zeros(m,n);for i=1:mfor j=1:nif Cr(i,j)>0 && Cb(i,j)>0sita(i,j)=atan(abs(Cr(i,j))/abs(Cb(i,j)))*180/pi;elseif Cr(i,j)>0 && Cb(i,j)<0sita(i,j)=180-atan(abs(Cr(i,j))/abs(Cb(i,j)))*180/pi;elseif Cr(i,j)<0 && Cb(i,j)<0sita(i,j)=180 + atan(abs(Cr(i,j))/abs(Cb(i,j)))*180/pi;elsesita(i,j)=0;endendendfor i=1:mfor j=1:nif sita(i,j)>105 && sita(i,j)<150emp(i,j)=sita(i,j);elseemp(i,j)=0;Y(i,j)=0;endendfigure,imshow(emp); figure,imshow(uint8(Y));原图像分割结果分割结果。
基于matlab的人脸识别源代码
function varargout = FR_Processed_histogram(varargin)%这种算法是基于直方图处理的方法%The histogram of image is calculated and then bin formation is done on the%basis of mean of successive graylevels frequencies. The training is done on odd images of 40 subjects (200 images out of 400 images)%The results of the implemented algorithm is 99.75 (recognition fails on image number 4 of subject 17)gui_Singleton = 1;gui_State = struct('gui_Name', mfilename, ...'gui_Singleton', gui_Singleton, ...'gui_OpeningFcn',@FR_Processed_histogram_OpeningFcn, ...'gui_OutputFcn',@FR_Processed_histogram_OutputFcn, ...'gui_LayoutFcn', [] , ...'gui_Callback', []);if nargin && ischar(varargin{1})gui_State.gui_Callback = str2func(varargin{1});endif nargout[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});elsegui_mainfcn(gui_State, varargin{:});end% End initialization code - DO NOT EDIT%--------------------------------------------------------------------------% --- Executes just before FR_Processed_histogram is made visible.function FR_Processed_histogram_OpeningFcn(hObject, eventdata, handles, varargin)% This function has no output args, see OutputFcn.% hObject handle to figure% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA) % varargin command line arguments to FR_Processed_histogram (see VARARGIN)% Choose default command line output for FR_Processed_histogramhandles.output = hObject;% Update handles structureguidata(hObject, handles);% UIWAIT makes FR_Processed_histogram wait for user response (see UIRESUME)% uiwait(handles.figure1);global total_sub train_img sub_img max_hist_level bin_num form_bin_num;total_sub = 40;train_img = 200;sub_img = 10;max_hist_level = 256;bin_num = 9;form_bin_num = 29;%--------------------------------------------------------------------------% --- Outputs from this function are returned to the command line.function varargout = FR_Processed_histogram_OutputFcn(hObject, eventdata, handles)% varargout cell array for returning output args (see VARARGOUT);% hObject handle to figure% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)% Get default command line output from handles structurevarargout{1} = handles.output;%--------------------------------------------------------------------------% --- Executes on button press in train_button.function train_button_Callback(hObject, eventdata, handles)% hObject handle to train_button (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)global train_processed_bin;global total_sub train_img sub_img max_hist_level bin_num form_bin_num;train_processed_bin(form_bin_num,train_img) = 0;K = 1;train_hist_img = zeros(max_hist_level, train_img);for Z=1:1:total_subfor X=1:2:sub_img %%%train on odd number of images of each subjectI = imread( strcat('ORL\S',int2str(Z),'\',int2str(X),'.bmp') );[rows cols] = size(I);for i=1:1:rowsfor j=1:1:colsif( I(i,j) == 0 )train_hist_img(max_hist_level, K) = train_hist_img(max_hist_level, K) + 1;elsetrain_hist_img(I(i,j), K) = train_hist_img(I(i,j), K) + 1;endendendK = K + 1;endend[r c] = size(train_hist_img);sum = 0;for i=1:1:cK = 1;for j=1:1:rif( (mod(j,bin_num)) == 0 )sum = sum + train_hist_img(j,i);train_processed_bin(K,i) = sum/bin_num;K = K + 1;sum = 0;elsesum = sum + train_hist_img(j,i);endendtrain_processed_bin(K,i) = sum/bin_num;enddisplay ('Training Done')save 'train'train_processed_bin;%--------------------------------------------------------------------------% --- Executes on button press in Testing_button.function Testing_button_Callback(hObject, eventdata, handles)% hObject handle to Testing_button (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA) global train_img max_hist_level bin_num form_bin_num;global train_processed_bin;global filename pathname Iload 'train'test_hist_img(max_hist_level) = 0;test_processed_bin(form_bin_num) = 0;[rows cols] = size(I);for i=1:1:rowsfor j=1:1:colsif( I(i,j) == 0 )test_hist_img(max_hist_level) =test_hist_img(max_hist_level) + 1;elsetest_hist_img(I(i,j)) = test_hist_img(I(i,j)) + 1;endendend[r c] = size(test_hist_img);sum = 0;K = 1;for j=1:1:cif( (mod(j,bin_num)) == 0 )sum = sum + test_hist_img(j);test_processed_bin(K) = sum/bin_num;K = K + 1;sum = 0;elsesum = sum + test_hist_img(j);endendtest_processed_bin(K) = sum/bin_num;sum = 0;K = 1;for y=1:1:train_imgfor z=1:1:form_bin_numsum = sum + abs( test_processed_bin(z) - train_processed_bin(z,y) );endimg_bin_hist_sum(K,1) = sum;sum = 0;K = K + 1;end[temp M] = min(img_bin_hist_sum);M = ceil(M/5);getString_start=strfind(pathname,'S');getString_start=getString_start(end)+1;getString_end=strfind(pathname,'\');getString_end=getString_end(end)-1;subjectindex=str2num(pathname(getString_start:getString_end));if (subjectindex == M)axes (handles.axes3)%image no: 5 is shown for visualization purposeimshow(imread(STRCAT('ORL\S',num2str(M),'\5.bmp')))msgbox ( 'Correctly Recognized');elsedisplay ([ 'Error==> Testing Image of Subject >>' num2str(subjectindex) ' matches with the image of subject >> ' num2str(M)])axes (handles.axes3)%image no: 5 is shown for visualization purposeimshow(imread(STRCAT('ORL\S',num2str(M),'\5.bmp')))msgbox ( 'Incorrectly Recognized');enddisplay('Testing Done')%--------------------------------------------------------------------------function box_Callback(hObject, eventdata, handles)% hObject handle to box (see GCBO)% eventdata reserved - to be defined in a future version ofMATLAB% handles structure with handles and user data (see GUIDATA)% Hints: get(hObject,'String') returns contents of box as text% str2double(get(hObject,'String')) returns contents of box as a double%--------------------------------------------------------------------------% --- Executes during object creation, after setting all properties.function box_CreateFcn(hObject, eventdata, handles)% hObject handle to box (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles empty - handles not created until after all CreateFcns called% Hint: edit controls usually have a white background on Windows.% See ISPC and COMPUTER.if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))set(hObject,'BackgroundColor','white');end%--------------------------------------------------------------------------% --- Executes on button press in Input_Image_button.function Input_Image_button_Callback(hObject, eventdata, handles) % hObject handle to Input_Image_button (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA) global filename pathname I[filename, pathname] = uigetfile('*.bmp', 'Test Image');axes(handles.axes1)imgpath=STRCAT(pathname,filename);I = imread(imgpath);imshow(I)%--------------------------------------------------------------------------% --- Executes during object creation, after setting all properties.function axes3_CreateFcn(hObject, eventdata, handles)% hObject handle to axes3 (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles empty - handles not created until after all CreateFcnscalled% Hint: place code in OpeningFcn to populate axes3 %Programmed by Usman Qayyum。
ICA人脸识别算法实例matlab源码
% 将其组成矩阵
S=[I1;I2;I3]; % 图片个数即为变量数，图片的像素数即为采样数
% 因此S_all是一个变量个数＊采样个数的矩阵
b=((1-u)*t'*g*b+u*X*g)/SampleNum-mean(dg)*b;
% 核心公式，参见理论部分公式2.52
b=b-B*B'*b; % 对b正交化
%%%%%%%%%%%%%%%%%%%%%%%%%% 初始化 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clc%%%%%%%%%%% 读入原始图像，混合，并输出混合图像 %%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%% PCA计算并构图 %%%%%%%%%%%%%%%%%%%%%%%%
[V,D]=eig(MixedS_cov);
Vtmp=zeros(size(V,1),1);
for j=1:2
for i=1:2
if D(i,i)
X=MixedS_white; % 以下算法将对X进行操作
[VariableNum,SampleNum]=size(X);
numofIC=VariableNum; % 在此应用中，独立元个数等于变量个数
Sweight=randn(size(S,1)); % 取一随机矩阵，作为信号混合的权矩阵
MixedS=Sweight*S; % 得到三个混合信号矩阵
% 将混合矩阵重新排列并输出
subplot(4,3,4),plot(MixedS(1,:)),title('混合信号1'),
人脸识别MATLAB代码教学内容
1. 色彩空间转换function [r,g]=rgb_RGB(Ori_Face) R=Ori_Face(:,:,1);G=Ori_Face(:,:,2);B=Ori_Face(:,:,3);R1=im2double(R); % 将uint8 型转换成double 型G1=im2double(G);B1=im2double(B);RGB=R1+G1+B1;row=size(Ori_Face,1); % 行像素column=size(Ori_Face,2); % 列像素for i=1:row for j=1:columnrr(i,j)=R1(i,j)/RGB(i,j); gg(i,j)=G1(i,j)/RGB(i,j);endend rrr=mean(rr); r=mean(rrr); ggg=mean(gg); g=mean(ggg);2. 均值和协方差皮肤库\1.jpg');[r1,g1]=rgb_RGB(t1); 皮肤库\2.jpg');[r2,g2]=rgb_RGB(t2); 皮肤库\3.jpg');[r3,g3]=rgb_RGB(t3); 皮肤库\4.jpg');[r4,g4]=rgb_RGB(t4); 皮肤库\5.jpg');[r5,g5]=rgb_RGB(t5); 皮肤库\6.jpg');[r6,g6]=rgb_RGB(t6); 皮肤库\7.jpg');[r7,g7]=rgb_RGB(t7); 皮肤库\8.jpg');[r8,g8]=rgb_RGB(t8);皮肤库\9.jpg');[r9,g9]=rgb_RGB(t9);皮肤库\10.jpg');[r10,g10]=rgb_RGB(t10);皮肤库\11.jpg');[r11,g11]=rgb_RGB(t11);皮肤库\12.jpg');[r12,g12]=rgb_RGB(t12);皮肤库\13.jpg');[r13,g13]=rgb_RGB(t13);皮肤库\14.jpg');[r14,g14]=rgb_RGB(t14);皮肤库\15.jpg');[r15,g15]=rgb_RGB(t15);皮肤库\16.jpg');[r16,g16]=rgb_RGB(t16);皮肤库\17.jpg');[r17,g17]=rgb_RGB(t17);皮肤库\18.jpg');[r18,g18]=rgb_RGB(t18);皮肤库\19.jpg');[r19,g19]=rgb_RGB(t19);皮肤库\20.jpg');[r20,g20]=rgb_RGB(t20);皮肤库\21.jpg');[r21,g21]=rgb_RGB(t21);皮肤库\22.jpg');[r22,g22]=rgb_RGB(t22);皮肤库\23.jpg');[r23,g23]=rgb_RGB(t23);皮肤库\24.jpg');[r24,g24]=rgb_RGB(t24);皮肤库\25.jpg');[r25,g25]=rgb_RGB(t25);皮肤库\26.jpg');[r26,g26]=rgb_RGB(t26);皮肤库\27.jpg');[r27,g27]=rgb_RGB(t27);r=cat(1,r1,r2,r3,r4,r5,r6,r7,r8,r9,r10,r11,r12,r13,r14,r15,r16,r17,r18,r19,r20,r21,r22, r23,r24,r25,r26,r27);g=cat(1,g1,g2,g3,g4,g5,g6,g7,g8,g9,g10,g11,g12,g13,g14,g15,g16,g17,g18,g19,g20 ,g21,g22,g23,g24,g25,g26,g27);m=mean([r,g])n=cov([r,g])3. 求质心function [xmean, ymean] = center(bw)bw=bwfill(bw,'holes');area = bwarea(bw);[m n] =size(bw); bw=double(bw); xmean =0; ymean = 0;for i=1:m,for j=1:n,xmean = xmean + j*bw(i,j); ymean = ymean + i*bw(i,j); end;end;if(area==0)xmean=0;ymean=0;elsexmean = xmean/area;ymean = ymean/area;xmean = round(xmean); ymean = round(ymean);end4. 求偏转角度function [theta] = orient(bw,xmean,ymean) [m n] =size(bw); bw=double(bw);a = 0;b = 0;c = 0;for i=1:m,for j=1:n,a = a + (j - xmea n)^2 * bw(i,j);b = b + (j - xmean) * (i - ymean) * bw(i,j);c = c + (i - ymea n)^2 * bw(i,j);end;end;b = 2 * b;theta = atan(b/(a-c))/2;theta = theta*(180/pi); % 从幅度转换到角度5. 找区域边界function [left, right, up, down] = bianjie(A)[m n] = size(A);left = -1;right = -1;up = -1;down = -1;for j=1:n,for i=1:m,if (A(i,j) ~= 0)left = j;break;end;end;if (left ~= -1) break;end;end;for j=n:-1:1,for i=1:m,if (A(i,j) ~= 0)right = j;break;end;end;if (right ~= -1) break;end;end;for i=1:m,for j=1:n,if (A(i,j) ~= 0)up = i;break;end;end;if (up ~= -1) break;end;end;for i=m:-1:1,for j=1:n,if (A(i,j) ~= 0) down = i;break;end;end;if (down ~= -1)break;end;end;6. 求起始坐标function newcoord = checklimit(coord,maxval) newcoord = coord; if (newcoord<1)newcoord=1;end;if (newcoord>maxval) newcoord=maxval; end; 7. 模板匹配function [ccorr, mfit, RectCoord] = mobanpipei(mult, frontalmodel,ly,wx,cx, cy, angle) frontalmodel=rgb2gray(frontalmodel);model_rot = imresize(frontalmodel,[ly wx],'bilinear'); % 调整模板大小 model_rot = imrotate(model_rot,angle,'bilinear'); [l,r,u,d] = bianjie(model_rot);bwmodel_rot=imcrop(model_rot,[l u (r-l) (d-u)]); [modx,mody] =center(bwmodel_rot); [morig, norig] = size(bwmodel_rot);% 产生一个覆盖了人脸模板的灰度图像 mfit = zeros(size(mult)); mfitbw = zeros(size(mult)); [limy, limx] = size(mfit); % 计算原图像中人脸模板的坐标 startx = cx-modx; starty = cy-mody;endx = startx + norig-1; endy = starty + morig-1; startx = checklimit(startx,limx); starty = checklimit(starty,limy); endx = checklimit(endx,limx); endy = checklimit(endy,limy); for i=starty:endy, for j=startx:endx, mfit(i,j) = model_rot(i-starty+1,j-startx+1); end; end;ccorr = corr2(mfit,mult) % 计算相关度 [l,r,u,d] = bianjie(bwmodel_rot); sx = startx+l; sy = starty+u;RectCoord = [sx sy (r-1) (d-u)]; % 产生矩形坐标 8. 主程序 clear;[fname,pname]=uigetfile({'*.jpg';'*.bmp';'*.tif';'*.gif'},'Please choose a color picture...');% 返回打开的图片名与图片路径名% 旋转模板 % 求边界坐标 % 选择模板人脸区域 % 求质心[u,v]=size(fname);y=fname(v); % 图片格式代表值switch ycase 0errordlg('You Should Load Image File First...','Warning...');case{'g';'G';'p';'P';'f';'F'}; % 图片格式若是JPG/jpg、BMP/bmp、TIF/tif 或者GIF/gif ，才打开I=cat(2,pname,fname);Ori_Face=imread(I);subplot(2,3,1),imshow(Ori_Face);otherwiseerrordlg('You Should Load Image File First...','Warning...');endR=Ori_Face(:,:,1);G=Ori_Face(:,:,2);B=Ori_Face(:,:,3);R1=im2double(R); % 将uint8 型转换成double 型处理G1=im2double(G);B1=im2double(B);RGB=R1+G1+B1;m=[ 0.4144,0.3174]; % 均值n=[0.0031,-0.0004;-0.0004,0.0003]; % 方差row=size(Ori_Face,1); % 行像素数column=size(Ori_Face,2); % 列像素数for i=1:rowfor j=1:column if RGB(i,j)==0 rr(i,j)=0;gg(i,j)=0;else rr(i,j)=R1(i,j)/RGB(i,j); % rgb 归一化gg(i,j)=G1(i,j)/RGB(i,j); x=[rr(i,j),gg(i,j)];p(i,j)=exp((-0.5)*(x-m)*inv(n)*(x-m)'); % 皮肤概率服从高斯分布end endendsubplot(2,3,2);imshow(p); % 显示皮肤灰度图像low_pass=1/9*ones(3);image_low=filter2(low_pass, p); % 低通滤波去噪声subplot(2,3,3);imshow(image_low);% 自适应阀值程序previousSkin2 = zeros(i,j); changelist = [];for threshold = 0.55:-0.1:0.05 two_value = zeros(i,j);two_value(find(image_low>threshold)) = 1; change = sum(sum(two_value - previousSkin2)); changelist = [changelist change]; previousSkin2 = two_value; end[C, I] = min(changelist);optimalThreshold = (7-I)*0.1 two_value = zeros(i,j);two_value(find(image_low>optimalThreshold)) = 1; % 二值化subplot(2,3,4);imshow(two_value); % 显示二值图像 我的照片 人脸模板 .jpg'); %读入人脸模板照片 FaceCoord=[];imsourcegray=rgb2gray(Ori_Face); % 将原照片转换为灰度 图像[L,N]=bwlabel(two_value,8); % 标注二值图像中连接的部分 ,L 为数据矩阵， N 为颗粒的个数for i=1:N,[x,y]=find(bwlabel(two_value)==i);行和列的下标bwsegment = bwselect(two_value,y,x,8); numholes = 1-bweuler(bwsegment,4); 行下一步运算RectCoord = -1;justface = uint8(double(bwnohole) .* double(imsourcegray));中保留该候选区域angle = orient(bwsegment,cx,cy);bw = imrotate(bwsegment, angle, 'bilinear'); bw = bwfill(bw,'holes'); [l,r,u,d] =bianjie(bw); wx = (r - l +1); ly = (d - u + 1); wratio = ly/wxif ((0.8<=wratio)&(wratio<=2))% 如果目标区域的高度 / 宽度比例大于 0.8 且小于 2.0，则将其选 出进行下一步运算S=ly*wx; % 计算包含此区域矩形 的面积A=bwarea(bwsegment); % 计算此区域面积% 寻找矩阵中标号为 i 的% 选择出第 i 个颗粒 % 计算此区域的空洞数if (numholes >= 1)% 若此区域至少包含一个洞，则将其选出进[m n] = size(bwsegment); [cx,cy]=center(bwsegment); bwnohole=bwfill(bwsegment,'holes');% 求此区域的质心% 将洞封住（将灰度值赋为 1 ）% 只在原照片的灰度图像求此区域的偏转角度% 宽度 % 高度 % 高宽比if (A/S>0.35)[ccorr,mfit, RectCoord] = mobanpipei(justface,frontalmodel,ly,wx, cx,cy, angle);endif (ccorr>=0.6)mfitbw=(mfit>=1);invbw = xor(mfitbw,ones(size(mfitbw)));source_with_hole = uint8(double(invbw) .* double(imsourcegray)); final_image = uint8(double(source_with_hole) + double(mfit));subplot(2,3,5);imshow(final_image); % 显示覆盖了模板脸的灰度图像imsourcegray = final_image; subplot(2,3,6);imshow(Ori_Face); % 显示检测效果图end;if (RectCoord ~= -1)FaceCoord = [FaceCoord; RectCoord];endendendend% 在认为是人脸的区域画矩形[numfaces x] = size(FaceCoord);for i=1:numfaces,hd = rectangle('Position',FaceCoord(i,:));set(hd, 'edgecolor', 'y');end人脸检测是人脸识别、人机交互、智能视觉监控等工作的前提。
(完整版)人脸识别MATLAB代码
1.色彩空间转换function [r,g]=rgb_RGB(Ori_Face)R=Ori_Face(:,:,1);G=Ori_Face(:,:,2);B=Ori_Face(:,:,3);R1=im2double(R); % 将uint8型转换成double型G1=im2double(G);B1=im2double(B);RGB=R1+G1+B1;row=size(Ori_Face,1); % 行像素column=size(Ori_Face,2); % 列像素for i=1:rowfor j=1:columnrr(i,j)=R1(i,j)/RGB(i,j);gg(i,j)=G1(i,j)/RGB(i,j);endendrrr=mean(rr);r=mean(rrr);ggg=mean(gg);g=mean(ggg);2.均值和协方差t1=imread('D:\matlab\皮肤库\1.jpg');[r1,g1]=rgb_RGB(t1); t2=imread('D:\matlab\皮肤库\2.jpg');[r2,g2]=rgb_RGB(t2); t3=imread('D:\matlab\皮肤库\3.jpg');[r3,g3]=rgb_RGB(t3); t4=imread('D:\matlab\皮肤库\4.jpg');[r4,g4]=rgb_RGB(t4); t5=imread('D:\matlab\皮肤库\5.jpg');[r5,g5]=rgb_RGB(t5); t6=imread('D:\matlab\皮肤库\6.jpg');[r6,g6]=rgb_RGB(t6); t7=imread('D:\matlab\皮肤库\7.jpg');[r7,g7]=rgb_RGB(t7); t8=imread('D:\matlab\皮肤库\8.jpg');[r8,g8]=rgb_RGB(t8);t9=imread('D:\matlab\皮肤库\9.jpg');[r9,g9]=rgb_RGB(t9);t10=imread('D:\matlab\皮肤库\10.jpg');[r10,g10]=rgb_RGB(t10);t11=imread('D:\matlab\皮肤库\11.jpg');[r11,g11]=rgb_RGB(t11);t12=imread('D:\matlab\皮肤库\12.jpg');[r12,g12]=rgb_RGB(t12);t13=imread('D:\matlab\皮肤库\13.jpg');[r13,g13]=rgb_RGB(t13);t14=imread('D:\matlab\皮肤库\14.jpg');[r14,g14]=rgb_RGB(t14);t15=imread('D:\matlab\皮肤库\15.jpg');[r15,g15]=rgb_RGB(t15);t16=imread('D:\matlab\皮肤库\16.jpg');[r16,g16]=rgb_RGB(t16);t17=imread('D:\matlab\皮肤库\17.jpg');[r17,g17]=rgb_RGB(t17);t18=imread('D:\matlab\皮肤库\18.jpg');[r18,g18]=rgb_RGB(t18);t19=imread('D:\matlab\皮肤库\19.jpg');[r19,g19]=rgb_RGB(t19);t20=imread('D:\matlab\皮肤库\20.jpg');[r20,g20]=rgb_RGB(t20);t21=imread('D:\matlab\皮肤库\21.jpg');[r21,g21]=rgb_RGB(t21);t22=imread('D:\matlab\皮肤库\22.jpg');[r22,g22]=rgb_RGB(t22);t23=imread('D:\matlab\皮肤库\23.jpg');[r23,g23]=rgb_RGB(t23);t24=imread('D:\matlab\皮肤库\24.jpg');[r24,g24]=rgb_RGB(t24);t25=imread('D:\matlab\皮肤库\25.jpg');[r25,g25]=rgb_RGB(t25);t26=imread('D:\matlab\皮肤库\26.jpg');[r26,g26]=rgb_RGB(t26);t27=imread('D:\matlab\皮肤库\27.jpg');[r27,g27]=rgb_RGB(t27);r=cat(1,r1,r2,r3,r4,r5,r6,r7,r8,r9,r10,r11,r12,r13,r14,r15,r16,r17,r18,r19,r20,r21,r22, r23,r24,r25,r26,r27);g=cat(1,g1,g2,g3,g4,g5,g6,g7,g8,g9,g10,g11,g12,g13,g14,g15,g16,g17,g18,g19,g20 ,g21,g22,g23,g24,g25,g26,g27);m=mean([r,g])n=cov([r,g])3.求质心function [xmean, ymean] = center(bw)bw=bwfill(bw,'holes');area = bwarea(bw);[m n] =size(bw);bw=double(bw);xmean =0; ymean = 0;for i=1:m,for j=1:n,xmean = xmean + j*bw(i,j);ymean = ymean + i*bw(i,j);end;end;if(area==0)xmean=0;ymean=0;elsexmean = xmean/area;ymean = ymean/area;xmean = round(xmean);ymean = round(ymean);end4. 求偏转角度function [theta] = orient(bw,xmean,ymean) [m n] =size(bw);bw=double(bw);a = 0;b = 0;c = 0;for i=1:m,for j=1:n,a = a + (j - xmean)^2 * bw(i,j);b = b + (j - xmean) * (i - ymean) * bw(i,j);c = c + (i - ymean)^2 * bw(i,j);end;b = 2 * b;theta = atan(b/(a-c))/2;theta = theta*(180/pi); % 从幅度转换到角度5. 找区域边界function [left, right, up, down] = bianjie(A)[m n] = size(A);left = -1;right = -1;up = -1;down = -1;for j=1:n,for i=1:m,if (A(i,j) ~= 0)left = j;break;end;end;if (left ~= -1) break;end;end;for j=n:-1:1,for i=1:m,if (A(i,j) ~= 0)right = j;break;end;end;if (right ~= -1) break;end;for i=1:m,for j=1:n,if (A(i,j) ~= 0)up = i;break;end;end;if (up ~= -1)break;end;end;for i=m:-1:1,for j=1:n,if (A(i,j) ~= 0)down = i;break;end;end;if (down ~= -1)break;end;end;6. 求起始坐标function newcoord = checklimit(coord,maxval) newcoord = coord;if (newcoord<1)newcoord=1;end;if (newcoord>maxval)newcoord=maxval;end;7.模板匹配function [ccorr, mfit, RectCoord] = mobanpipei(mult, frontalmodel,ly,wx,cx, cy, angle)frontalmodel=rgb2gray(frontalmodel);model_rot = imresize(frontalmodel,[ly wx],'bilinear'); % 调整模板大小model_rot = imrotate(model_rot,angle,'bilinear'); % 旋转模板[l,r,u,d] = bianjie(model_rot); % 求边界坐标bwmodel_rot=imcrop(model_rot,[l u (r-l) (d-u)]); % 选择模板人脸区域[modx,mody] =center(bwmodel_rot); % 求质心[morig, norig] = size(bwmodel_rot);% 产生一个覆盖了人脸模板的灰度图像mfit = zeros(size(mult));mfitbw = zeros(size(mult));[limy, limx] = size(mfit);% 计算原图像中人脸模板的坐标startx = cx-modx;starty = cy-mody;endx = startx + norig-1;endy = starty + morig-1;startx = checklimit(startx,limx);starty = checklimit(starty,limy);endx = checklimit(endx,limx);endy = checklimit(endy,limy);for i=starty:endy,for j=startx:endx,mfit(i,j) = model_rot(i-starty+1,j-startx+1);end;end;ccorr = corr2(mfit,mult) % 计算相关度[l,r,u,d] = bianjie(bwmodel_rot);sx = startx+l;sy = starty+u;RectCoord = [sx sy (r-1) (d-u)]; % 产生矩形坐标8.主程序clear;[fname,pname]=uigetfile({'*.jpg';'*.bmp';'*.tif';'*.gif'},'Please choose a color picture...'); % 返回打开的图片名与图片路径名[u,v]=size(fname);y=fname(v); % 图片格式代表值switch ycase 0errordlg('You Should Load Image File First...','Warning...');case{'g';'G';'p';'P';'f';'F'}; % 图片格式若是JPG/jpg、BMP/bmp、TIF/tif 或者GIF/gif，才打开I=cat(2,pname,fname);Ori_Face=imread(I);subplot(2,3,1),imshow(Ori_Face);otherwiseerrordlg('You Should Load Image File First...','Warning...');endR=Ori_Face(:,:,1);G=Ori_Face(:,:,2);B=Ori_Face(:,:,3);R1=im2double(R); % 将uint8型转换成double型处理G1=im2double(G);B1=im2double(B);RGB=R1+G1+B1;m=[ 0.4144,0.3174]; % 均值n=[0.0031,-0.0004;-0.0004,0.0003]; % 方差row=size(Ori_Face,1); % 行像素数column=size(Ori_Face,2); % 列像素数for i=1:rowfor j=1:columnif RGB(i,j)==0rr(i,j)=0;gg(i,j)=0;elserr(i,j)=R1(i,j)/RGB(i,j); % rgb归一化gg(i,j)=G1(i,j)/RGB(i,j);x=[rr(i,j),gg(i,j)];p(i,j)=exp((-0.5)*(x-m)*inv(n)*(x-m)'); % 皮肤概率服从高斯分布endendendsubplot(2,3,2);imshow(p); % 显示皮肤灰度图像low_pass=1/9*ones(3);image_low=filter2(low_pass, p); % 低通滤波去噪声subplot(2,3,3);imshow(image_low);% 自适应阀值程序previousSkin2 = zeros(i,j);changelist = [];for threshold = 0.55:-0.1:0.05two_value = zeros(i,j);two_value(find(image_low>threshold)) = 1;change = sum(sum(two_value - previousSkin2));changelist = [changelist change];previousSkin2 = two_value;end[C, I] = min(changelist);optimalThreshold = (7-I)*0.1two_value = zeros(i,j);two_value(find(image_low>optimalThreshold)) = 1; % 二值化subplot(2,3,4);imshow(two_value); % 显示二值图像frontalmodel=imread('E:\我的照片\人脸模板.jpg'); % 读入人脸模板照片FaceCoord=[];imsourcegray=rgb2gray(Ori_Face); % 将原照片转换为灰度图像[L,N]=bwlabel(two_value,8); % 标注二值图像中连接的部分,L为数据矩阵，N为颗粒的个数for i=1:N,[x,y]=find(bwlabel(two_value)==i); % 寻找矩阵中标号为i的行和列的下标bwsegment = bwselect(two_value,y,x,8); % 选择出第i个颗粒numholes = 1-bweuler(bwsegment,4); % 计算此区域的空洞数if (numholes >= 1) % 若此区域至少包含一个洞，则将其选出进行下一步运算RectCoord = -1;[m n] = size(bwsegment);[cx,cy]=center(bwsegment); % 求此区域的质心bwnohole=bwfill(bwsegment,'holes'); % 将洞封住（将灰度值赋为1）justface = uint8(double(bwnohole) .* double(imsourcegray));% 只在原照片的灰度图像中保留该候选区域angle = orient(bwsegment,cx,cy); % 求此区域的偏转角度bw = imrotate(bwsegment, angle, 'bilinear');bw = bwfill(bw,'holes');[l,r,u,d] =bianjie(bw);wx = (r - l +1); % 宽度ly = (d - u + 1); % 高度wratio = ly/wx % 高宽比if ((0.8<=wratio)&(wratio<=2))% 如果目标区域的高度/宽度比例大于0.8且小于2.0，则将其选出进行下一步运算S=ly*wx; % 计算包含此区域矩形的面积A=bwarea(bwsegment); % 计算此区域面积if (A/S>0.35)[ccorr,mfit, RectCoord] = mobanpipei(justface,frontalmodel,ly,wx, cx,cy, angle);endif (ccorr>=0.6)mfitbw=(mfit>=1);invbw = xor(mfitbw,ones(size(mfitbw)));source_with_hole = uint8(double(invbw) .* double(imsourcegray));final_image = uint8(double(source_with_hole) + double(mfit));subplot(2,3,5);imshow(final_image); % 显示覆盖了模板脸的灰度图像imsourcegray = final_image;subplot(2,3,6);imshow(Ori_Face); % 显示检测效果图end;if (RectCoord ~= -1)FaceCoord = [FaceCoord; RectCoord];endendendend% 在认为是人脸的区域画矩形[numfaces x] = size(FaceCoord);for i=1:numfaces,hd = rectangle('Position',FaceCoord(i,:));set(hd, 'edgecolor', 'y');end人脸检测是人脸识别、人机交互、智能视觉监控等工作的前提。
人脸识别MATLAB代码
1.色彩空间转换function [r,g]=rgb_RGB(Ori_Face)R=Ori_Face(:,:,1);G=Ori_Face(:,:,2);B=Ori_Face(:,:,3);R1=im2double(R); % 将uint8型转换成double型G1=im2double(G);B1=im2double(B);RGB=R1+G1+B1;row=size(Ori_Face,1); % 行像素column=size(Ori_Face,2); % 列像素for i=1:rowfor j=1:columnrr(i,j)=R1(i,j)/RGB(i,j);gg(i,j)=G1(i,j)/RGB(i,j);endendrrr=mean(rr);r=mean(rrr);ggg=mean(gg);g=mean(ggg);2.均值和协方差t1=imread('D:\matlab\皮肤库\1.jpg');[r1,g1]=rgb_RGB(t1); t2=imread('D:\matlab\皮肤库\2.jpg');[r2,g2]=rgb_RGB(t2); t3=imread('D:\matlab\皮肤库\3.jpg');[r3,g3]=rgb_RGB(t3); t4=imread('D:\matlab\皮肤库\4.jpg');[r4,g4]=rgb_RGB(t4); t5=imread('D:\matlab\皮肤库\5.jpg');[r5,g5]=rgb_RGB(t5); t6=imread('D:\matlab\皮肤库\6.jpg');[r6,g6]=rgb_RGB(t6); t7=imread('D:\matlab\皮肤库\7.jpg');[r7,g7]=rgb_RGB(t7); t8=imread('D:\matlab\皮肤库\8.jpg');[r8,g8]=rgb_RGB(t8);t9=imread('D:\matlab\皮肤库\9.jpg');[r9,g9]=rgb_RGB(t9);t10=imread('D:\matlab\皮肤库\10.jpg');[r10,g10]=rgb_RGB(t10);t11=imread('D:\matlab\皮肤库\11.jpg');[r11,g11]=rgb_RGB(t11);t12=imread('D:\matlab\皮肤库\12.jpg');[r12,g12]=rgb_RGB(t12);t13=imread('D:\matlab\皮肤库\13.jpg');[r13,g13]=rgb_RGB(t13);t14=imread('D:\matlab\皮肤库\14.jpg');[r14,g14]=rgb_RGB(t14);t15=imread('D:\matlab\皮肤库\15.jpg');[r15,g15]=rgb_RGB(t15);t16=imread('D:\matlab\皮肤库\16.jpg');[r16,g16]=rgb_RGB(t16);t17=imread('D:\matlab\皮肤库\17.jpg');[r17,g17]=rgb_RGB(t17);t18=imread('D:\matlab\皮肤库\18.jpg');[r18,g18]=rgb_RGB(t18);t19=imread('D:\matlab\皮肤库\19.jpg');[r19,g19]=rgb_RGB(t19);t20=imread('D:\matlab\皮肤库\20.jpg');[r20,g20]=rgb_RGB(t20);t21=imread('D:\matlab\皮肤库\21.jpg');[r21,g21]=rgb_RGB(t21);t22=imread('D:\matlab\皮肤库\22.jpg');[r22,g22]=rgb_RGB(t22);t23=imread('D:\matlab\皮肤库\23.jpg');[r23,g23]=rgb_RGB(t23);t24=imread('D:\matlab\皮肤库\24.jpg');[r24,g24]=rgb_RGB(t24);t25=imread('D:\matlab\皮肤库\25.jpg');[r25,g25]=rgb_RGB(t25);t26=imread('D:\matlab\皮肤库\26.jpg');[r26,g26]=rgb_RGB(t26);t27=imread('D:\matlab\皮肤库\27.jpg');[r27,g27]=rgb_RGB(t27);r=cat(1,r1,r2,r3,r4,r5,r6,r7,r8,r9,r10,r11,r12,r13,r14,r15,r16,r17,r18,r19,r20,r21,r22, r23,r24,r25,r26,r27);g=cat(1,g1,g2,g3,g4,g5,g6,g7,g8,g9,g10,g11,g12,g13,g14,g15,g16,g17,g18,g19,g20 ,g21,g22,g23,g24,g25,g26,g27);m=mean([r,g])n=cov([r,g])3.求质心function [xmean, ymean] = center(bw)bw=bwfill(bw,'holes');area = bwarea(bw);[m n] =size(bw);bw=double(bw);xmean =0; ymean = 0;for i=1:m,for j=1:n,xmean = xmean + j*bw(i,j);ymean = ymean + i*bw(i,j);end;end;if(area==0)xmean=0;ymean=0;elsexmean = xmean/area;ymean = ymean/area;xmean = round(xmean);ymean = round(ymean);end4. 求偏转角度function [theta] = orient(bw,xmean,ymean) [m n] =size(bw);bw=double(bw);a = 0;b = 0;c = 0;for i=1:m,for j=1:n,a = a + (j - xmean)^2 * bw(i,j);b = b + (j - xmean) * (i - ymean) * bw(i,j);c = c + (i - ymean)^2 * bw(i,j);end;end;b = 2 * b;theta = atan(b/(a-c))/2;theta = theta*(180/pi); % 从幅度转换到角度5. 找区域边界function [left, right, up, down] = bianjie(A)[m n] = size(A);left = -1;right = -1;up = -1;down = -1;for j=1:n,for i=1:m,if (A(i,j) ~= 0)left = j;break;end;end;if (left ~= -1) break;end;end;for j=n:-1:1,for i=1:m,if (A(i,j) ~= 0)right = j;break;end;end;if (right ~= -1) break;end;end;for i=1:m,for j=1:n,if (A(i,j) ~= 0)up = i;break;end;end;if (up ~= -1)break;end;end;for i=m:-1:1,for j=1:n,if (A(i,j) ~= 0)down = i;break;end;end;if (down ~= -1)break;end;end;6. 求起始坐标function newcoord = checklimit(coord,maxval) newcoord = coord;if (newcoord<1)newcoord=1;end;if (newcoord>maxval)newcoord=maxval;end;7.模板匹配function [ccorr, mfit, RectCoord] = mobanpipei(mult, frontalmodel,ly,wx,cx, cy, angle)frontalmodel=rgb2gray(frontalmodel);model_rot = imresize(frontalmodel,[ly wx],'bilinear'); % 调整模板大小model_rot = imrotate(model_rot,angle,'bilinear'); % 旋转模板[l,r,u,d] = bianjie(model_rot); % 求边界坐标bwmodel_rot=imcrop(model_rot,[l u (r-l) (d-u)]); % 选择模板人脸区域[modx,mody] =center(bwmodel_rot); % 求质心[morig, norig] = size(bwmodel_rot);% 产生一个覆盖了人脸模板的灰度图像mfit = zeros(size(mult));mfitbw = zeros(size(mult));[limy, limx] = size(mfit);% 计算原图像中人脸模板的坐标startx = cx-modx;starty = cy-mody;endx = startx + norig-1;endy = starty + morig-1;startx = checklimit(startx,limx);starty = checklimit(starty,limy);endx = checklimit(endx,limx);endy = checklimit(endy,limy);for i=starty:endy,for j=startx:endx,mfit(i,j) = model_rot(i-starty+1,j-startx+1);end;end;ccorr = corr2(mfit,mult) % 计算相关度[l,r,u,d] = bianjie(bwmodel_rot);sx = startx+l;sy = starty+u;RectCoord = [sx sy (r-1) (d-u)]; % 产生矩形坐标8.主程序clear;[fname,pname]=uigetfile({'*.jpg';'*.bmp';'*.tif';'*.gif'},'Please choose a color picture...'); % 返回打开的图片名与图片路径名[u,v]=size(fname);y=fname(v); % 图片格式代表值switch ycase 0errordlg('You Should Load Image File First...','Warning...');case{'g';'G';'p';'P';'f';'F'}; % 图片格式若是JPG/jpg、BMP/bmp、TIF/tif 或者GIF/gif，才打开I=cat(2,pname,fname);Ori_Face=imread(I);subplot(2,3,1),imshow(Ori_Face);otherwiseerrordlg('You Should Load Image File First...','Warning...');endR=Ori_Face(:,:,1);G=Ori_Face(:,:,2);B=Ori_Face(:,:,3);R1=im2double(R); % 将uint8型转换成double型处理G1=im2double(G);B1=im2double(B);RGB=R1+G1+B1;m=[ 0.4144,0.3174]; % 均值n=[0.0031,-0.0004;-0.0004,0.0003]; % 方差row=size(Ori_Face,1); % 行像素数column=size(Ori_Face,2); % 列像素数for i=1:rowfor j=1:columnif RGB(i,j)==0rr(i,j)=0;gg(i,j)=0;elserr(i,j)=R1(i,j)/RGB(i,j); % rgb归一化gg(i,j)=G1(i,j)/RGB(i,j);x=[rr(i,j),gg(i,j)];p(i,j)=exp((-0.5)*(x-m)*inv(n)*(x-m)'); % 皮肤概率服从高斯分布endendendsubplot(2,3,2);imshow(p); % 显示皮肤灰度图像low_pass=1/9*ones(3);image_low=filter2(low_pass, p); % 低通滤波去噪声subplot(2,3,3);imshow(image_low);% 自适应阀值程序previousSkin2 = zeros(i,j);changelist = [];for threshold = 0.55:-0.1:0.05two_value = zeros(i,j);two_value(find(image_low>threshold)) = 1;change = sum(sum(two_value - previousSkin2));changelist = [changelist change];previousSkin2 = two_value;end[C, I] = min(changelist);optimalThreshold = (7-I)*0.1two_value = zeros(i,j);two_value(find(image_low>optimalThreshold)) = 1; % 二值化subplot(2,3,4);imshow(two_value); % 显示二值图像frontalmodel=imread('E:\我的照片\人脸模板.jpg'); % 读入人脸模板照片FaceCoord=[];imsourcegray=rgb2gray(Ori_Face); % 将原照片转换为灰度图像[L,N]=bwlabel(two_value,8); % 标注二值图像中连接的部分,L为数据矩阵，N为颗粒的个数for i=1:N,[x,y]=find(bwlabel(two_value)==i); % 寻找矩阵中标号为i的行和列的下标bwsegment = bwselect(two_value,y,x,8); % 选择出第i个颗粒numholes = 1-bweuler(bwsegment,4); % 计算此区域的空洞数if (numholes >= 1) % 若此区域至少包含一个洞，则将其选出进行下一步运算RectCoord = -1;[m n] = size(bwsegment);[cx,cy]=center(bwsegment); % 求此区域的质心bwnohole=bwfill(bwsegment,'holes'); % 将洞封住（将灰度值赋为1）justface = uint8(double(bwnohole) .* double(imsourcegray));% 只在原照片的灰度图像中保留该候选区域angle = orient(bwsegment,cx,cy); % 求此区域的偏转角度bw = imrotate(bwsegment, angle, 'bilinear');bw = bwfill(bw,'holes');[l,r,u,d] =bianjie(bw);wx = (r - l +1); % 宽度ly = (d - u + 1); % 高度wratio = ly/wx % 高宽比if ((0.8<=wratio)&(wratio<=2))% 如果目标区域的高度/宽度比例大于0.8且小于2.0，则将其选出进行下一步运算S=ly*wx; % 计算包含此区域矩形的面积A=bwarea(bwsegment); % 计算此区域面积if (A/S>0.35)[ccorr,mfit, RectCoord] = mobanpipei(justface,frontalmodel,ly,wx, cx,cy, angle);endif (ccorr>=0.6)mfitbw=(mfit>=1);invbw = xor(mfitbw,ones(size(mfitbw)));source_with_hole = uint8(double(invbw) .* double(imsourcegray));final_image = uint8(double(source_with_hole) + double(mfit));subplot(2,3,5);imshow(final_image); % 显示覆盖了模板脸的灰度图像imsourcegray = final_image;subplot(2,3,6);imshow(Ori_Face); % 显示检测效果图end;if (RectCoord ~= -1)FaceCoord = [FaceCoord; RectCoord];endendendend% 在认为是人脸的区域画矩形[numfaces x] = size(FaceCoord);for i=1:numfaces,hd = rectangle('Position',FaceCoord(i,:));set(hd, 'edgecolor', 'y');end人脸检测是人脸识别、人机交互、智能视觉监控等工作的前提。
MATLAB人脸识别源代码
MATLAB人脸识别源代码% FaceRec.m %CQUPT% PCA 识别率88%% calc xmean,sigma and its eigen decompositionallsamples=[];%所有训练图片for i=1:40for j=1:5a=imread(strcat('e:\ORL\s',num2str(i),'\',num2str(j),'.pgm'));b=a(1:112*92); %b是行矢量1*N，N=10304，提取顺序是先列后行，%即从上到下，从左到右b=double(b);allsamples=[allsamples;b]; %allsamples是一个M*N矩阵，allsamples中每一行数据代%表一张图片，其中M=200endendsamplemean=mean(allsamples); %平均图片，1*N for i=1:200xmean(i,:)=allsamples(i,:)-samplemean; %allsamples是一个M*N矩阵，allsamples中每一行保存的数据是“每个图片数据—平均图片”end;%获取特征植及特征向量sigma=xmean*xmean'; % M* M矩阵[v d]=eig(sigma);d1=diag(d);%按特征值大小以降序排列dsort=flipud(d1);vsort=fliplr(v);%以下选择90%的能量dsum=sum(dsort);dsum_extract=0;p=0;while(dsum_extract/dsum<0.9) p=p+1;dsum_extract=sum(dsort(1:p)); endi=1;% (训练阶段)计算特征脸形成的坐标系base = xmean' * vsort(:,1:p) * diag(dsort(1:p).^(-1/2));%base是N*p阶矩阵，除以dsort(i) ^(-1/2))是对人脸图象的标准化(是其方差为1)% xmean' * vsort(:,1:p)是小矩阵的特征向量向大矩阵特征向量转换的过程%以下两行将训练样本对坐标系上进行投影，得到一个M*p子空间中的一个点，%即在子空间中的组合系数allcoor=allsamples*base;accu = 0; %下面的人脸识别过程中就是利用这些组合系数来进行识别%测试过程for i=1:40for j=6:10 %读入40 x 5 副测试图像a=imread(strcat('e:\ORL\s',num2str(i),'\',num2str(j),'.pgm'));b=a(1:10304);b=double(b);tcoor=b*base; %计算坐标，是1*p阶矩阵for k=1:200mdist(k)=norm(tcoor-allcoor(k,:)); end;%三阶近邻[dist,index2]=sort(mdist); class1=floor( (index2(1)-1)/5 )+1;class2=floor((index2(2)-1)/5)+1;class3=floor((index2(3)-1)/5)+1; if class1~=class2 && class2~=class3 class=class1;elseif class1==class2class=class1;elseif class2==class3class=class2;end;if class==iaccu=accu+1;end;end;end;accuracy=accu/200 % 输出识别率% FaceRec.m %CQUPT% PCA 识别率88%% calc xmean,sigma and its eigen decompositionallsamples=[]; %所有训练图片for i=1:40for j=1:5a=imread(strcat('e:\ORL\s',num2str(i),'\',num2str(j),'.pgm'));b=a(1:112*92); %b是行矢量1*N，N=10304，提取顺序是先列后行，%即从上到下，从左到右b=double(b);allsamples=[allsamples;b]; %allsamples是一个M*N矩阵，allsamples中每一行数据代%表一张图片，其中M=200endendsamplemean=mean(allsamples); %平均图片，1*Nfor i=1:200 xmean(i,:)=allsamples(i,:)-samplemean; %allsamples是一个M*N矩阵，allsamples中每一行保存 %的数据是“每个图片数据—平均图片” end;%获取特征植及特征向量sigma=xmean*xmean'; % M* M矩阵 [v d]=eig(sigma); d1=diag(d);%按特征值大小以降序排列dsort=flipud(d1); vsort=fliplr(v); %以下选择90%的能量dsum=sum(dsort);dsum_extract=0;p=0;while(dsum_extract/dsum<0.9) p=p+1;dsum_extract=sum(dsort(1:p)); endp=199;% (训练阶段)计算特征脸形成的坐标系base = xmean' * vsort(:,1:p) * diag(dsort(1:p).^(-1/2));%生成特征脸for(k=1:p)temp=reshape(base(:,k),112,92); newpath=[…e:\test\? int2str(k)….jpg?];imwrite(mat2gray(temp), newpath); end%将模型保存Save(…e:\ORL\model.mat? ,?base?, …samplemean?);%Reconstruct.m % CQUPTFunction[]=reconstruct() Load e:\ORL\model.mat;%计算新图片在特征子空间中的系数Img=?D:\test2\10.jpg?A=imread(img);b=a(1:112*92); % b是行矢量 1*N，其中N =10304 b=double(b);b=b-samplemean;c = b * base; % c 是图片 a在子空间中的系数, 是 1*p 行矢量 % 根据特征系数及特征脸重建图% 前15 个t = 15;temp = base(:,1:t) * c(1:t)'; temp = temp + samplemean';imwrite(mat2gray(reshape(temp, 112,92)),'d:\test2\t1.jpg');% 前50个t = 50;temp = base(:,1:t) * c(1:t)'; temp = temp + samplemean';imwrite(mat2gray(reshape(temp, 112,92)),'d:\test2\t2.jpg');% 前 100个t = 100;temp = base(:,1:t) * c(1:t)'; temp = temp + samplemean';imwrite(mat2gray(reshape(temp, 112,92)),'d:\test2\t3.jpg');% 前150个t = 150;temp = base(:,1:t) * c(1:t)'; temp = temp + samplemean';imwrite(mat2gray(reshape(temp, 112,92)),'d:\test2\t4.jpg');% 前199 个t = 199;temp = base(:,1:t) * c(1:t)'; temp = temp + samplemean';imwrite(mat2gray(reshape(temp, 112,92)),'d:\test2\t5.jpg');图片标准化通常是一个整体概念，要求把图片归一到均值为0，方差为1的情况下。
根据matlab的人脸识别源代码
function varargout = FR_Processed_histogram(varargin)%这种算法是基于直方图处理的方法%The histogram of image is calculated and then bin formation is done on the%basis of mean of successive graylevels frequencies. The training is done on odd images of 40 subjects (200 images out of 400 images)%The results of the implemented algorithm is 99.75 (recognition fails on image number 4 of subject 17)gui_Singleton = 1;gui_State = struct('gui_Name', mfilename, ...'gui_Singleton', gui_Singleton, ...'gui_OpeningFcn',@FR_Processed_histogram_OpeningFcn, ...'gui_OutputFcn',@FR_Processed_histogram_OutputFcn, ...'gui_LayoutFcn', [] , ...'gui_Callback', []);if nargin && ischar(varargin{1})gui_State.gui_Callback = str2func(varargin{1});endif nargout[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});elsegui_mainfcn(gui_State, varargin{:});end% End initialization code - DO NOT EDIT%--------------------------------------------------------------------------% --- Executes just before FR_Processed_histogram is made visible.function FR_Processed_histogram_OpeningFcn(hObject, eventdata, handles, varargin)% This function has no output args, see OutputFcn.% hObject handle to figure% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA) % varargin command line arguments to FR_Processed_histogram (see VARARGIN)% Choose default command line output for FR_Processed_histogramhandles.output = hObject;% Update handles structureguidata(hObject, handles);% UIWAIT makes FR_Processed_histogram wait for user response (see UIRESUME)% uiwait(handles.figure1);global total_sub train_img sub_img max_hist_level bin_num form_bin_num;total_sub = 40;train_img = 200;sub_img = 10;max_hist_level = 256;bin_num = 9;form_bin_num = 29;%--------------------------------------------------------------------------% --- Outputs from this function are returned to the command line.function varargout = FR_Processed_histogram_OutputFcn(hObject, eventdata, handles)% varargout cell array for returning output args (see VARARGOUT);% hObject handle to figure% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)% Get default command line output from handles structurevarargout{1} = handles.output;%--------------------------------------------------------------------------% --- Executes on button press in train_button.function train_button_Callback(hObject, eventdata, handles)% hObject handle to train_button (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA)global train_processed_bin;global total_sub train_img sub_img max_hist_level bin_num form_bin_num;train_processed_bin(form_bin_num,train_img) = 0;K = 1;train_hist_img = zeros(max_hist_level, train_img);for Z=1:1:total_subfor X=1:2:sub_img %%%train on odd number of images of each subjectI = imread( strcat('ORL\S',int2str(Z),'\',int2str(X),'.bmp') );[rows cols] = size(I);for i=1:1:rowsfor j=1:1:colsif( I(i,j) == 0 )train_hist_img(max_hist_level, K) = train_hist_img(max_hist_level, K) + 1;elsetrain_hist_img(I(i,j), K) = train_hist_img(I(i,j), K) + 1;endendendK = K + 1;endend[r c] = size(train_hist_img);sum = 0;for i=1:1:cK = 1;for j=1:1:rif( (mod(j,bin_num)) == 0 )sum = sum + train_hist_img(j,i);train_processed_bin(K,i) = sum/bin_num;K = K + 1;sum = 0;elsesum = sum + train_hist_img(j,i);endendtrain_processed_bin(K,i) = sum/bin_num;enddisplay ('Training Done')save 'train'train_processed_bin;%--------------------------------------------------------------------------% --- Executes on button press in Testing_button.function Testing_button_Callback(hObject, eventdata, handles)% hObject handle to Testing_button (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA) global train_img max_hist_level bin_num form_bin_num;global train_processed_bin;global filename pathname Iload 'train'test_hist_img(max_hist_level) = 0;test_processed_bin(form_bin_num) = 0;[rows cols] = size(I);for i=1:1:rowsfor j=1:1:colsif( I(i,j) == 0 )test_hist_img(max_hist_level) =test_hist_img(max_hist_level) + 1;elsetest_hist_img(I(i,j)) = test_hist_img(I(i,j)) + 1;endendend[r c] = size(test_hist_img);sum = 0;K = 1;for j=1:1:cif( (mod(j,bin_num)) == 0 )sum = sum + test_hist_img(j);test_processed_bin(K) = sum/bin_num;K = K + 1;sum = 0;elsesum = sum + test_hist_img(j);endendtest_processed_bin(K) = sum/bin_num;sum = 0;K = 1;for y=1:1:train_imgfor z=1:1:form_bin_numsum = sum + abs( test_processed_bin(z) - train_processed_bin(z,y) );endimg_bin_hist_sum(K,1) = sum;sum = 0;K = K + 1;end[temp M] = min(img_bin_hist_sum);M = ceil(M/5);getString_start=strfind(pathname,'S');getString_start=getString_start(end)+1;getString_end=strfind(pathname,'\');getString_end=getString_end(end)-1;subjectindex=str2num(pathname(getString_start:getString_end));if (subjectindex == M)axes (handles.axes3)%image no: 5 is shown for visualization purposeimshow(imread(STRCAT('ORL\S',num2str(M),'\5.bmp')))msgbox ( 'Correctly Recognized');elsedisplay ([ 'Error==> Testing Image of Subject >>' num2str(subjectindex) ' matches with the image of subject >> ' num2str(M)])axes (handles.axes3)%image no: 5 is shown for visualization purposeimshow(imread(STRCAT('ORL\S',num2str(M),'\5.bmp')))msgbox ( 'Incorrectly Recognized');enddisplay('Testing Done')%--------------------------------------------------------------------------function box_Callback(hObject, eventdata, handles)% hObject handle to box (see GCBO)% eventdata reserved - to be defined in a future version ofMATLAB% handles structure with handles and user data (see GUIDATA)% Hints: get(hObject,'String') returns contents of box as text% str2double(get(hObject,'String')) returns contents of box as a double%--------------------------------------------------------------------------% --- Executes during object creation, after setting all properties.function box_CreateFcn(hObject, eventdata, handles)% hObject handle to box (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles empty - handles not created until after all CreateFcns called% Hint: edit controls usually have a white background on Windows.% See ISPC and COMPUTER.if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))set(hObject,'BackgroundColor','white');end%--------------------------------------------------------------------------% --- Executes on button press in Input_Image_button.function Input_Image_button_Callback(hObject, eventdata, handles) % hObject handle to Input_Image_button (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles structure with handles and user data (see GUIDATA) global filename pathname I[filename, pathname] = uigetfile('*.bmp', 'Test Image');axes(handles.axes1)imgpath=STRCAT(pathname,filename);I = imread(imgpath);imshow(I)%--------------------------------------------------------------------------% --- Executes during object creation, after setting all properties.function axes3_CreateFcn(hObject, eventdata, handles)% hObject handle to axes3 (see GCBO)% eventdata reserved - to be defined in a future version of MATLAB% handles empty - handles not created until after all CreateFcnscalled% Hint: place code in OpeningFcn to populate axes3 %Programmed by Usman Qayyum。
基于MATLAB的人脸识别源程序文件
1.色彩空间转换function [r,g]=rgb_RGB(Ori_Face)R=Ori_Face(:,:,1);G=Ori_Face(:,:,2);B=Ori_Face(:,:,3);R1=im2double(R); % 将uint8型转换成double型G1=im2double(G);B1=im2double(B);RGB=R1+G1+B1;row=size(Ori_Face,1); % 行像素column=size(Ori_Face,2); % 列像素for i=1:rowfor j=1:columnrr(i,j)=R1(i,j)/RGB(i,j);gg(i,j)=G1(i,j)/RGB(i,j);endendrrr=mean(rr);r=mean(rrr);ggg=mean(gg);g=mean(ggg);2.均值和协方差t1=imread('D:\matlab\皮肤库\1.jpg');[r1,g1]=rgb_RGB(t1);t2=imread('D:\matlab\皮肤库\2.jpg');[r2,g2]=rgb_RGB(t2);t3=imread('D:\matlab\皮肤库\3.jpg');[r3,g3]=rgb_RGB(t3);t4=imread('D:\matlab\皮肤库\4.jpg');[r4,g4]=rgb_RGB(t4);t5=imread('D:\matlab\皮肤库\5.jpg');[r5,g5]=rgb_RGB(t5);t6=imread('D:\matlab\皮肤库\6.jpg');[r6,g6]=rgb_RGB(t6);t7=imread('D:\matlab\皮肤库\7.jpg');[r7,g7]=rgb_RGB(t7);t8=imread('D:\matlab\皮肤库\8.jpg');[r8,g8]=rgb_RGB(t8);t9=imread('D:\matlab\皮肤库\9.jpg');[r9,g9]=rgb_RGB(t9);t10=imread('D:\matlab\皮肤库\10.jpg');[r10,g10]=rgb_RGB(t10);t11=imread('D:\matlab\皮肤库\11.jpg');[r11,g11]=rgb_RGB(t11);t12=imread('D:\matlab\皮肤库\12.jpg');[r12,g12]=rgb_RGB(t12);t13=imread('D:\matlab\皮肤库\13.jpg');[r13,g13]=rgb_RGB(t13);t14=imread('D:\matlab\皮肤库\14.jpg');[r14,g14]=rgb_RGB(t14);t15=imread('D:\matlab\皮肤库\15.jpg');[r15,g15]=rgb_RGB(t15);t16=imread('D:\matlab\皮肤库t17=imread('D:\matlab\皮肤库\17.jpg');[r17,g17]=rgb_RGB(t17);t18=imread('D:\matlab\皮肤库\18.jpg');[r18,g18]=rgb_RGB(t18);t19=imread('D:\matlab\皮肤库\19.jpg');[r19,g19]=rgb_RGB(t19);t20=imread('D:\matlab\皮肤库\20.jpg');[r20,g20]=rgb_RGB(t20);t21=imread('D:\matlab\皮肤库\21.jpg');[r21,g21]=rgb_RGB(t21);t22=imread('D:\matlab\皮肤库\22.jpg');[r22,g22]=rgb_RGB(t22);t23=imread('D:\matlab\皮肤库\23.jpg');[r23,g23]=rgb_RGB(t23);t24=imread('D:\matlab\皮肤库\24.jpg');[r24,g24]=rgb_RGB(t24);t25=imread('D:\matlab\皮肤库\25.jpg');[r25,g25]=rgb_RGB(t25);t26=imread('D:\matlab\皮肤库\26.jpg');[r26,g26]=rgb_RGB(t26);t27=imread('D:\matlab\皮肤库r=cat(1,r1,r2,r3,r4,r5,r6,r7,r8,r9,r10,r11,r12,r13,r14,r15, r16,r17,r18,r19,r20,r21,r22,r23,r24,r25,r26,r27);g=cat(1,g1,g2,g3,g4,g5,g6,g7,g8,g9,g10,g11,g12,g13,g14,g15, g16,g17,g18,g19,g20,g21,g22,g23,g24,g25,g26,g27);m=mean([r,g])n=cov([r,g])3.求质心function [xmean, ymean] = center(bw)bw=bwfill(bw,'holes');area = bwarea(bw);[m n] =size(bw);bw=double(bw);xmean =0; ymean = 0;for i=1:m,for j=1:n,xmean = xmean + j*bw(i,j);ymean = ymean + i*bw(i,j);end;end;if(area==0)xmean=0;ymean=0;elsexmean = xmean/area;ymean = ymean/area;xmean = round(xmean);ymean = round(ymean);end4. 求偏转角度function [theta] = orient(bw,xmean,ymean) [m n] =size(bw);bw=double(bw);a = 0;b = 0;c = 0;for i=1:m,for j=1:n,a = a + (j - xmean)^2 * bw(i,j);b = b + (j - xmean) * (i - ymean) * bw(i,j);c = c + (i - ymean)^2 * bw(i,j);end;end;b = 2 * b;theta = atan(b/(a-c))/2;theta = theta*(180/pi); % 从幅度转换到角度5. 找区域边界function [left, right, up, down] = bianjie(A) [m n] = size(A);left = -1;right = -1;up = -1;down = -1;for j=1:n,for i=1:m,if (A(i,j) ~= 0)left = j;break;end;end;if (left ~= -1) break;end;end;for j=n:-1:1,for i=1:m,if (A(i,j) ~= 0)right = j;break;end;end;if (right ~= -1) break; end;end;for i=1:m,for j=1:n,if (A(i,j) ~= 0)up = i;break;end;end;if (up ~= -1)break;end;end;for i=m:-1:1,for j=1:n,if (A(i,j) ~= 0) down = i;break;end;end;if (down ~= -1)break;end;end;6. 求起始坐标function newcoord = checklimit(coord,maxval)newcoord = coord;if (newcoord<1)newcoord=1;end;if (newcoord>maxval)newcoord=maxval;end;7.模板匹配function [ccorr, mfit, RectCoord] = mobanpipei(mult, frontalmodel,ly,wx,cx, cy, angle)frontalmodel=rgb2gray(frontalmodel);model_rot = imresize(frontalmodel,[ly wx],'bilinear'); % 调整模板大小model_rot = imrotate(model_rot,angle,'bilinear'); % 旋转模板[l,r,u,d] = bianjie(model_rot); % 求边界坐标bwmodel_rot=imcrop(model_rot,[l u (r-l) (d-u)]); % 选择模板人脸区域[modx,mody] =center(bwmodel_rot); % 求质心[morig, norig] = size(bwmodel_rot);% 产生一个覆盖了人脸模板的灰度图像mfit = zeros(size(mult));mfitbw = zeros(size(mult));[limy, limx] = size(mfit);% 计算原图像中人脸模板的坐标startx = cx-modx;starty = cy-mody;endx = startx + norig-1;endy = starty + morig-1;startx = checklimit(startx,limx);starty = checklimit(starty,limy);endx = checklimit(endx,limx);endy = checklimit(endy,limy);for i=starty:endy,for j=startx:endx,mfit(i,j) = model_rot(i-starty+1,j-startx+1);end;end;ccorr = corr2(mfit,mult) % 计算相关度[l,r,u,d] = bianjie(bwmodel_rot);sx = startx+l;sy = starty+u;RectCoord = [sx sy (r-1) (d-u)]; % 产生矩形坐标8.主程序clear;[fname,pname]=uigetfile({'*.jpg';'*.bmp';'*.tif';'*.gif'},' Please choose a color picture...'); % 返回打开的图片名与图片路径名[u,v]=size(fname);y=fname(v); % 图片格式代表值switch ycase 0errordlg('You Should Load Image File First...','Warning...');case{'g';'G';'p';'P';'f';'F'}; % 图片格式若是JPG/jpg、BMP/bmp、TIF/tif或者GIF/gif，才打开I=cat(2,pname,fname);Ori_Face=imread(I);subplot(2,3,1),imshow(Ori_Face);otherwiseerrordlg('You Should Load Image File First...','Warning...');endR=Ori_Face(:,:,1);G=Ori_Face(:,:,2);B=Ori_Face(:,:,3);R1=im2double(R); % 将uint8型转换成double型处理G1=im2double(G);B1=im2double(B);RGB=R1+G1+B1;m=[ 0.4144,0.3174]; % 均值n=[0.0031,-0.0004;-0.0004,0.0003]; % 方差row=size(Ori_Face,1); % 行像素数column=size(Ori_Face,2); % 列像素数for i=1:rowfor j=1:columnif RGB(i,j)==0rr(i,j)=0;gg(i,j)=0;elserr(i,j)=R1(i,j)/RGB(i,j); % rgb归一化 gg(i,j)=G1(i,j)/RGB(i,j);x=[rr(i,j),gg(i,j)];p(i,j)=exp((-0.5)*(x-m)*inv(n)*(x-m)'); % 皮肤概率服从高斯分布endendendsubplot(2,3,2);imshow(p); % 显示皮肤灰度图像low_pass=1/9*ones(3);image_low=filter2(low_pass, p); % 低通滤波去噪声subplot(2,3,3);imshow(image_low);% 自适应阀值程序previousSkin2 = zeros(i,j);changelist = [];for threshold = 0.55:-0.1:0.05two_value = zeros(i,j);two_value(find(image_low>threshold)) = 1;change = sum(sum(two_value - previousSkin2));changelist = [changelist change];previousSkin2 = two_value;end[C, I] = min(changelist);optimalThreshold = (7-I)*0.1two_value = zeros(i,j);two_value(find(image_low>optimalThreshold)) = 1; % 二值化subplot(2,3,4);imshow(two_value); % 显示二值图像frontalmodel=imread('E:\我的照片\人脸模板.jpg'); % 读入人脸模板照片FaceCoord=[];imsourcegray=rgb2gray(Ori_Face); % 将原照片转换为灰度图像[L,N]=bwlabel(two_value,8); % 标注二值图像中连接的部分,L 为数据矩阵，N为颗粒的个数for i=1:N,[x,y]=find(bwlabel(two_value)==i); % 寻找矩阵中标号为i的行和列的下标bwsegment = bwselect(two_value,y,x,8); % 选择出第i个颗粒numholes = 1-bweuler(bwsegment,4); % 计算此区域的空洞数if (numholes >= 1) % 若此区域至少包含一个洞，则将其选出进行下一步运算RectCoord = -1;[m n] = size(bwsegment);[cx,cy]=center(bwsegment); % 求此区域的质心bwnohole=bwfill(bwsegment,'holes'); % 将洞封住（将灰度值赋为1）justface = uint8(double(bwnohole) .* double(imsourcegray)); % 只在原照片的灰度图像中保留该候选区域angle = orient(bwsegment,cx,cy); % 求此区域的偏转角度bw = imrotate(bwsegment, angle, 'bilinear');bw = bwfill(bw,'holes');[l,r,u,d] =bianjie(bw);wx = (r - l +1); % 宽度ly = (d - u + 1); % 高度wratio = ly/wx % 高宽比if ((0.8<=wratio)&(wratio<=2))% 如果目标区域的高度/宽度比例大于0.8且小于2.0，则将其选出进行下一步运算S=ly*wx; % 计算包含此区域矩形的面积A=bwarea(bwsegment); % 计算此区域面积if (A/S>0.35)[ccorr,mfit, RectCoord] = mobanpipei(justface,frontalmodel,ly,wx, cx,cy, angle);endif (ccorr>=0.6)mfitbw=(mfit>=1);invbw = xor(mfitbw,ones(size(mfitbw)));source_with_hole = uint8(double(invbw) .* double(imsourcegray));final_image = uint8(double(source_with_hole) + double(mfit));subplot(2,3,5);imshow(final_image); % 显示覆盖了模板脸的灰度图像imsourcegray = final_image;subplot(2,3,6);imshow(Ori_Face); % 显示检测效果图end;if (RectCoord ~= -1)FaceCoord = [FaceCoord; RectCoord];endendendend% 在认为是人脸的区域画矩形[numfaces x] = size(FaceCoord);for i=1:numfaces,hd = rectangle('Position',FaceCoord(i,:));set(hd, 'edgecolor', 'y');end人脸检测是人脸识别、人机交互、智能视觉监控等工作的前提。
用matlab实现人脸识别
用matlab实现人脸识别最近一直在搞这个东西，从一开始什么都不会到现在的能在被人的基础之上改一些代码。
感觉有了不小的进步，现在把这些代码贴出来分享给大家。
先贴一个FLD的完整代码吧！load('orldata.mat');facenumber=length(orldata);for i=1:facenumber%载入人脸库facedatabase{i}=double(orldata{i});endnclass = 40;%类别数nsampleeachclass = 10; %每一类中人脸数neachtrain =4; %每类中选取的用于训练的样本数neachtest = nsampleeachclass-neachtrain; %每类中用于识别的样本数height = 112; %样本的高度width = 92; %样本的宽度for i=1:nclassfor j=1:neachtraingnd((i-1)*neachtrain+j,1)=i;endendfor k=1:20 %随机抽样20次a=rand(1,nsampleeachclass);[a,index]=sort(a);for i=1:nclass %把训练样本与识别样本的每一个点变成一个列向量for j=1:neachtraintrainSample((i-1)*neachtrain+j,:)=reshape(facedatabase{(i-1)* (nsampleeachclass)+index(j)},1,height*width);%reshape函数把图像矩阵转化为行向量；%trainX(:,:,(i-1)*neachtrain+j)=facedatabase{(i-1)*(nsampleea chclass)+index(j)};endfor j=1:neachtesttestSample((i-1)*neachtest+j,:)=reshape(facedatabase{(i-1)*(nsampleeachclass)+index(neachtrain+j)},1 ,height*width);%testX(:,:,(i-1)*neachtest+j)=facedatabase{(i-1)*(nsampleeach class)+index(neachtrain+j)};endendPCAoptions = [];PCAoptions.PCARatio = 0.98;[eigvector_PCA, eigvalue_PCA, meanData, new_X] =PCA(trainSample,PCAoptions);[Wlda, Xlda,r] = LDA1(new_X',gnd, nclass) ;vec=eigvector_PCA*Wlda;[a,b]=size(Wlda);for d=2:2:bnewTrainSample = trainSample*vec(:,1:d);newTestSample = testSample*vec(:,1:d);classification=classif(newTrainSample,newTestSample);suc(k,d/2) = success(classification, neachtrain, neachtest); %每次随机选训练样本对应不同d的成功率clear newTrainSample newTestSample classification;endclear trainSample testSample vec val;endavesuc=mean(suc);disp('Recognition rate:');avesuc其中函数LDA1的代码如下：%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%function [Wlda, Xlda,r] = LDA1( X, XClass, classCount) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%-input X 输入样本按列堆积的矩阵 d*n维% XClass 每一列所属的类别向量% classCount 样本类别总数%-output% Wlda 投影矩阵% Xlda X在Wlda上的投影系数% r 类间散布矩阵Sb的秩，即判别函数的个数？？？%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%[d,n] = size( X ); % 样本维数d和个数nc = classCount; % 样本的类别数if d < cerror( 'the dimension of training sample must be not less than c!' ); end% 求总体均值及各类样本均值，m = zeros(d,1);m = mean(X,2); % 求总体样本均值，d维列向量XMean = zeros(d,c); % c类样本的均值矩阵for i = 1:ctempMatrix = X(:, find( XClass==i ) ); % 把属于第i类的所有样本储存到tempMatrix中XMean(:,i) = mean( tempMatrix, 2 ); % 计算第i类样本均值，存到XMean第i列end% 求类内散布矩阵SwY=X;for i = 1:nY(:,i) = Y(:,i) - XMean(:,XClass(i)); % 每个样本减去各自所属类的均值endSw = Y * Y.'; % 总类内散布矩阵，d*d维% 求类间散布矩阵SbSb = zeros(d,d);for i = 1:cNi = length( find( XClass == i ) ); % 计算第i类的样本的个数NiSb = Sb + Ni * (XMean(:,i) - m) * (XMean(:,i) - m).'; % 即Sb=N1*(m1-m)*(m1-m)'+...+Nc*(mc-m)*(mc-m)'end% % 也可以利用St=Sb+Sw 如下计算% % St = ( Xpca - kron( ones(1,N), m) ) * ( Xpca - kron( ones(1,N), m) ).'; % % Sb = St - Sw;% 求Sw^-1*Sb的特征值和特征向量% [V,D] = eig(Sw^(-1)*Sb)，注意到 [V,D] = EIG(A,B)表示 A*V = B*V*D [V,D] = eig(Sb,Sw); % 即 [V,D] = eig(Sw^(-1)*Sb)Ddiag = diag(D); % 取特征值为列向量Ddiag = Ddiag.'; % 变为行向量[Ddiag, Index] = sort( Ddiag, 'descend' ); % 按降序排列特征值% 求Wlda，Xldar = rank( Sb ); % 求Sb的秩r（r<=c-1），非零的特征值只有r个，只需要求对应的r个特征向量Wlda = zeros( d, r );Xlda = zeros( r, n );Wlda = V(:,Index(1:r)); % 投影矩阵（d*r维）为前r个最大特征值对应的特征向量Wlda = Wlda ./ (ones(size(Wlda, 1), 1) * sqrt(sum(Wlda .^ 2, 1))); % 把投影矩阵的每一列都除以该列的 2-norm(也就是通常的所有元素的平方求和再开根号),即标准化Xlda = Wlda.' * X;。
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class3=floor(index2(3)/5)+1;
if class1~=class2 && class2~=class3
class=class1;
elseif class1==class2
% calc xmean,sigma and its eigen decomposition
allsamples=[];%所有训练图像
for i=1:40
for j=1:5
a=imread(strcat('D:\rawdata\ORL\s',num2str(i),'\',num2str(j),'.pgm'));
i = i + 1;
end
% add by wolfsky 就是下面两行代码，将训练样本对坐标系上进行投影,得到一个 M*p 阶矩阵allcoor
allcoor = allsamples * base;
accu = 0;
% 测试过程
for i=1:40
for j=6:10 %读入40 x 5 副测试图像
a=imread(strcat('D:\rawdata\ORL\s',num2str(i),'\',num2str(j),'.pgm'));
b=a(1:10304);
b=double(b);
tcoor= b * base; %计算坐标，是1×p阶矩阵
for k=1:200
mdist(k)=norm(tcoor-allcoor(k,:));
end;
%三阶近邻
[dist,index2]=sort(mdist);
class1=floor( index2(1)/5 )+1;
p = p + 1;
dsum_extract = sum(dsort(1:p));
end
i=1;
% (训练阶段)计算特征脸形成的坐标系
while (i<=p && dsort(i)>0)
base(:,i) = dsort(i)^(-1/2) * xmean' * vsort(:,i); % base是N×p阶矩阵，除以dsort(i)^(1/2)是对人脸图像的标准化，详见《基于PCA的人脸识别算法研究》p31
% imshow(a);
b=a(1:112*92); % b是行矢量 1×N，其中N＝10304，提取顺序是先列后行，即从上到下，从左到右
b=double(b);
allsamples=[allsamples; b]; % allsamples 是一个M * N 矩阵，allsamples 中每一行数据代表一张图片，其中M＝200
end
end
samplemean=mean(allsamples); % 平均图片，1 × N
for i=1:200 xmean(i,:)=allsamples(i,:)-samplemean; % xmean是一个M × N矩阵，xmean每一行保存的数据是“每个图片数据-平均图片”
end;
class=class1;
elseif class2==class3
class=class2;
end;
if class==i
accu=accu+1;
end;
end;
end;
accuracy=accu/200 %输出识别率
Байду номын сангаас
sigma=xmean*xmean'; % M * M 阶矩阵
[v d]=eig(sigma);
d1=diag(d);
[d2 index]=sort(d1); %以升序排序
cols=size(v,2);% 特征向量矩阵的列数
for i=1:cols
vsort(:,i) = v(:, index(cols-i+1) ); % vsort 是一个M*col(注:col一般等于M)阶矩阵，保存的是按降序排列的特征向量,每一列构成一个特征向量
dsort(i) = d1( index(cols-i+1) ); % dsort 保存的是按降序排列的特征值，是一维行向量
end %完成降序排列
%以下选择90%的能量
dsum = sum(dsort);
dsum_extract = 0;
p = 0;
while( dsum_extract/dsum < 0.9)