matlab实现基于主成分分析的人脸识别

matlab实现基于主成分分析的人脸识别

MATLAB代码如下

function varargout = face_recognition(varargin)
% FACE_RECOGNITION MATLAB code for face_recognition.fig
%      FACE_RECOGNITION, by itself, creates a new FACE_RECOGNITION or raises the existing
%      singleton*.
%
%      H = FACE_RECOGNITION returns the handle to a new FACE_RECOGNITION or the handle to
%      the existing singleton*.
%
%      FACE_RECOGNITION('CALLBACK',hObject,eventData,handles,...) calls the local
%      function named CALLBACK in FACE_RECOGNITION.M with the given input arguments.
%
%      FACE_RECOGNITION('Property','Value',...) creates a new FACE_RECOGNITION or raises the
%      existing singleton*.  Starting from the left, property value pairs are
%      applied to the GUI before face_recognition_OpeningFcn gets called.  An
%      unrecognized property name or invalid value makes property application
%      stop.  All inputs are passed to face_recognition_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 face_recognition

% Last Modified by GUIDE v2.5 20-Sep-2017 23:33:17

% Begin initialization code - DO NOT EDIT
gui_Singleton = 1;
gui_State = struct('gui_Name',       mfilename, ...
                   'gui_Singleton',  gui_Singleton, ...
                   'gui_OpeningFcn', @face_recognition_OpeningFcn, ...
                   'gui_OutputFcn',  @face_recognition_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 face_recognition is made visible.
function face_recognition_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 face_recognition (see VARARGIN)

% Choose default command line output for face_recognition
handles.output = hObject;

% Update handles structure
guidata(hObject, handles);


clear all;


global coeff
global scores
global training_count
global samples_mean

%%
%%训练,
% 人脸库中的15个人来自Yale人脸数据库，另外还采集了2个人的样本，每个人脸的样本数量为11，(可以根据需要再采集一些人脸样本，命名规则为 0xx/xx.jpg)。
%输入人脸样本数
people_count = input('请输入样本数,(输入0为默认值15,当前样本库有18个人) :    ');
fprintf('您输入的样本数为:  %d nn',people_count);
%每类样本数
face_count_per_people=11;

%输入每类训练样本比例
%training_ratio=.20;
training_ratio = input('请输入每类训练样本比例（输入范围为0-1):    ');
if training_ratio< 1 && training_ratio>0
    fprintf('您输入的每类训练样本比例为:  %.1f nn',training_ratio);
else 
    fprintf('您输入的每类训练样本有误，系统取默认值为0.5 nn');
    training_ratio = 0.5;
end
 
disp('Waiting for training.......');

% 能量，即较大特征值之和占所有特征值之和的比例。
energy=0.9;
% 每类训练样本数
training_count=floor(face_count_per_people*training_ratio); %floor向后取整,training_ratio=0.7时，training_count为7
%训练样本数据，每行是一个样本
training_samples=[];

% 训练
for i=1:people_count
    for j=1:training_count
        img=im2double(imread(['样本库' sprintf('%03d',i) '' sprintf('%02d',j) '.jpg']));%读取的地址为 0xx/xx.jpg
        img=imresize(img,[10 10]); % 图像缩放至至10*10
        %若是彩色图像，则灰度化
        if ndims(img)==3
            img=rgb2gray(img);
        end
        training_samples=[training_samples;img(:)'];%将缩放后的10*10图像转为一行，一行代表一副图片的信息，并将其放入training_samples中
    end
end

%求取训练样本均值，每行一个样本，每列一类特征
samples_mean=mean(training_samples);%对每一列取平均，得到每个字段的平均值
%whos training_samples

%调用matlab的pca函数
%coeff是主成分系数矩阵，即变换（投影）矩阵
%scores是训练样本投影后的矩阵
%latent是协方差矩阵的特征值，降序排列
[coeff,scores,latent]=pca(training_samples);%对样本进行求协方差矩阵，求平均值和协方差
% whos coeff%100*100  
%10*10的图片变换成一行后是100维，training_samples为  [people_count*training_count 100] 其协方差矩阵的特征向量即为coeff是100*100
% whos latent%100*1

%寻找占了energy比例的下标，即主成分就取到这么多维
idx=find(cumsum(latent)./sum(latent)>energy,1); 
coeff=coeff(:,1:idx);  %取出的主成分系数矩阵
scores=scores(:,1:idx); %训练样本投影矩阵
 %figure;
 %latent
 %latent(1:idx,:)
 %subplot 211;hist(latent);title('样本中的主特征值');
 %subplot 212;hist(latent(1:idx,:));title('主成分中的主特征值');
disp('Training has Done');
%%





% UIWAIT makes face_recognition wait for user response (see UIRESUME)
% uiwait(handles.figure1);


% --- Outputs from this function are returned to the command line.
function varargout = face_recognition_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;

%%显示提示信息
global training_count
str = sprintf('当前每个人的训练样本数为:%d，测试样本数为:%d，请从后%d张人脸中选择测试',training_count,11-training_count,11-training_count);
set(handles.text2,'string',str);

%%

% --- 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)

%%
%从测试样本中选取人脸
global img_target
[filename, pathname] = uigetfile({'*.jpg';'*.bmp'},'选择人脸');
str = [pathname, filename];
img_target = imread(str);
axes( handles.axes1);
img_target=im2double(img_target);
%若是彩色图像，则灰度化
if ndims(img_target)==3
    img_target=rgb2gray(img_target);
end
imshow(img_target,[]);
%缩放为10*10
img_target=imresize(img_target,[10 10]); % 图像缩放至至10*10



%%

% --- 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)

%%
% 测试，进行人脸匹配
global img_target
global coeff
global scores
global training_count
global samples_mean

%测试样本减去训练样本均值，然后投影，得到投影后的样本表示
score=(img_target(:)'-samples_mean)*coeff;  % [1 100]*[100 100]
%计算测试样本和每个训练样本之间的欧式距离（计算平方值）
%然后利用最近邻分类器对测试样本进行分类
score_repeat = repmat(score,size(scores,1),1);%把1*idx行的一张图片扩展成people_count*training_count*idx，每一行都一样
distance = (scores-score_repeat).^2;
disp(distance);
[~,idx]=min(  sum(distance,2)) ;%对每一行求和，求得最小的一行，就是相当于找最近似的图片
%根据idx找出最近似的训练样本
i = ceil(idx/training_count);%向后取整
j = idx - (i-1)*training_count;
img_dst=im2double(imread(['样本库' sprintf('%03d',i) '' sprintf('%02d',j) '.jpg']));
%将寻找到的结果显示出来
if ndims(img_dst)==3
     img_dst=rgb2gray(img_dst);
end
axes( handles.axes2);
imshow(img_dst,[]);
%%       

结果截图：

具体源码和训练库在：
https://download.csdn.net/download/qq_44250808/18624399

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