新建文件夹.rar_face recognition_人脸 签到_人脸签到_人脸识别系统_签到系统

function varargout = untitled(varargin) % UNTITLED MATLAB code for untitled.fig % UNTITLED, by itself, creates a new UNTITLED or raises the existing % singleton*. % % H = UNTITLED returns the handle to a new UNTITLED or the handle to % the existing singleton*. % % UNTITLED('CALLBACK',hObject,eventData,handles,...) calls the local % function named CALLBACK in UNTITLED.M with the given input arguments. % % UNTITLED('Property','Value',...) creates a new UNTITLED or raises the % existing singleton*. Starting from the left, property value pairs are % applied to the GUI before untitled_OpeningFcn gets called. An % unrecognized property name or invalid value makes property application % stop. All inputs are passed to untitled_OpeningFcn via varargin. % % *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one % instance to run (singleton)". % % See also: GUIDE, GUIDATA, GUIHANDLES % Edit the above text to modify the response to help untitled % Last Modified by GUIDE v2.5 10-Jul-2017 20:49:19 % Begin initialization code - DO NOT EDIT gui_Singleton = 1; gui_State = struct('gui_Name', mfilename, ... 'gui_Singleton', gui_Singleton, ... 'gui_OpeningFcn', @untitled_OpeningFcn, ... 'gui_OutputFcn', @untitled_OutputFcn, ... 'gui_LayoutFcn', [] , ... 'gui_Callback', []); if nargin && ischar(varargin{1}) gui_State.gui_Callback = str2func(varargin{1}); end if nargout [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:}); else gui_mainfcn(gui_State, varargin{:}); end % End initialization code - DO NOT EDIT % --- Executes just before untitled is made visible. function untitled_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 untitled (see VARARGIN) % Choose default command line output for untitled handles.output = hObject; % Update handles structure guidata(hObject, handles); % UIWAIT makes untitled wait for user response (see UIRESUME) % uiwait(handles.figure1); % --- Outputs from this function are returned to the command line. function varargout = untitled_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 structure varargout{1} = handles.output; % --- Executes on button press in pushbutton1. function pushbutton1_Callback(hObject, eventdata, handles) % hObject handle to pushbutton1 (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) function pushbutton1_Callback(hObject, eventdata, handles) % hObject handle to pushbutton1 (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) %1.“测试图片”按钮 %% 读取待查找图片 global im;%由于要在两个按钮函数中使用，故使用全局变量 [filename, pathname]=... uigetfile({'*.pgm'},'选择图片'); str = [pathname, filename];%合成路径+文件名 im = imread(str);%读取图片 axes( handles.axes1);%使用第一个axes imshow(im);title('待查找')%显示图片 % --- Executes on button press in pushbutton2. function pushbutton2_Callback(hObject, eventdata, handles) % hObject handle to pushbutton2 (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) function pushbutton2_Callback(hObject, eventdata, handles) % hObject handle to pushbutton2 (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles structure with handles and user data (see GUIDATA) %2.“匹配照片”按钮 %% PCA人脸识别 global im; xunlian=[];%训练图像矩阵 %%%%%%%%%%%%%%提取图片 for i=1:40 for j=1:10 a=imread(strcat('人脸识别课设\Data\ORL\s',num2str(i),'\',num2str(j),'.pgm')); b=a(1:112*92); % b是行矢量1×N，其中N＝10304 b=double(b); xunlian=[xunlian; b]; % xunlian是一个M * N矩阵，每一行数据一张图片，M＝200 end; end; xunlian=xunlian'; %每一列为一张图片 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%’ X=zeros(10304,40);%初始化变量X，训练样本中元素由每个人的平均图像向量组成 for i=1:40 Yangben=xunlian(:,10*(i-1)+1: 10*(i-1)+5);%把样本集xunlian中的每个人的前五张图片组成训练集 X(:,i)=mean(Yangben,2);%求得每个人的平均图像向量 end meanvetor=mean(X,2);%求得总体的平均图像向量 for i=1:40 X(:,i)=X(:,i)-meanvetor;%求得训练集，由每个人平均图像向量减去总体平均图像向量得到 end pmetrix=X'*X; %求X’*X，应用的原理是奇异值分解的推论 [vet vetvalue]=eig(pmetrix);%求特征值和特征向量 vet=fliplr(vet); vetvalue=fliplr(vetvalue);%对特征值由大到小排列，相应的特征向量也进行变换 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%55 Cvetvalue=sum(vetvalue); Ctotal=sum(Cvetvalue,2); for i=1:40 parttotal=sum(Cvetvalue(1:i),2); ratio=parttotal/Ctotal; if ratio>0.99 break; end end %选择最大的前n个特征值，由这些值的和在总特征值中占得比例大于99% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% U=X*vet; U=orth(U);%应用奇异值分解的推论得到正交化的特征脸 A= U(:,1:i)';%选择前n个特征值对应的特征向量组成特征脸 y= A*X;%求训练集中各图像在特征脸空间中的坐标 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% a=im; b=a(1:10304); b=double(b); b=b'; ceshi=b -meanvetor;%待识别图像减去总体平均图像向量 zbceshi=A*ceshi;%求得其在特征脸空间的坐标 for k=1:40 wucha=zbceshi-y(:,k); wuchametrix(:,k)=wucha'*wucha; end %计算该坐标与训练集中各图像坐标之间的距离 [h,I]=min(wuchametrix); %最近邻法进行人脸识别 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% path = strcat('人脸识别课设\Data\ORL\s',num2str(I)); matchImg=imread(strcat(path,'\',num2str(1),'.pgm'));%识别为某个人后，输出此人的第一张图 axes(handles.axes2); imshow(matchImg); title('从人脸库中匹配出的人脸图像');