【图像识别】基于PCA+LDA算法人脸识别matlab代码

1 简介

随着社会的进步和科技的发展,生物特征的识别应用越来越多.如海关及公安系统等需要提供准确的身份认证,日常生活中,人们的安全,财产,隐私等都需要利用先进的科技手段进行保护.因此如何能够高效,快速,可靠识别生物身份的系统被人们普遍研究.本文寻求了一种主成分分析(PCA)和线性判别分析(LDA)相结合的方法对人脸进行识别.

主成分分析（Principal Component Analysis）算法简称 PCA，是一种有效的特征提取方法，算法将人脸图像转换成一个列向量，经过变换后可以有效降低其维数，又能保留有效的识别信息，这些有效信息构成了一组特征图像，并且这些信息对人脸的光照、表情、姿态等具有相当的不敏感性。PCA 变换的目的是通过线性变换，找到一组最优的单位正交向量基（即主成分），用线性组合来重构与原样本均方差的误差最小的一种变换方法。数学上，PCA 通过求解投影矩阵来获得低维度的特征向量。对于某 K 维向量来说，在原有的K 维坐标中它的特征向量在各个维度上的变化均不明显，我们可以通过改变其所处的坐标轴，来使它的特征向量在某些坐标轴上的变化更加明显。例如，在X 轴、Y 轴平面上，将一个椭圆倾斜放置在第一象限内，X、Y 轴上的投影变化度都不是很大，但在将椭圆的长轴作为 X 轴后，椭圆在 X 轴的投影范围变得更大，方差也更大，但在 Y 轴的投影范围变得更小，方差也更小。这时我们就可以消除掉 Y 轴的特征信息，只保留 X 轴的特征信息，这便完成了高维信息向低维的转换。

2 部分代码

function varargout = faceCore(varargin)

% FACECORE M-file for faceCore.fig

%     FACECORE, by itself, creates a new FACECORE or raises the existing

%     singleton*.

%

%     H = FACECORE returns the handle to a new FACECORE or the handle to

%     the existing singleton*.

%

%     FACECORE('CALLBACK',hObject,eventData,handles,...) calls the local

%     function named CALLBACK in FACECORE.M with the given input arguments.

%

%     FACECORE('Property','Value',...) creates a new FACECORE or raises the

%     unrecognized property name or invalid value makes property application

%

%     instance to run (singleton)".

%

% See also: GUIDE, GUIDATA, GUIHANDLES

% Copyright

% Edit the above text to modify the response to help faceCore

% Last Modified by GUIDE v2.5 11-Apr-2021 23:45:58

% Begin initialization code - DO NOT EDIT

gui_Singleton = 1;

gui_State = struct('gui_Name',       mfilename, ...

'gui_Singleton',  gui_Singleton, ...

'gui_OpeningFcn', @faceCore_OpeningFcn, ...

'gui_OutputFcn',  @faceCore_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 faceCore is made visible.

function faceCore_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 faceCore (see VARARGIN)

% Choose default command line output for faceCore

handles.output = hObject;

% Update handles structure

guidata(hObject, handles);

% UIWAIT makes faceCore wait for user response (see UIRESUME)

% uiwait(handles.figure1);

% --- Outputs from this function are returned to the command line.

function varargout = faceCore_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)

global TrainDatabasePath ;

TrainDatabasePath = uigetdir(strcat(matlabroot,'\work'), '训练库路径选择...' );

% --- 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)

global TestDatabasePath;

TestDatabasePath = uigetdir(strcat(matlabroot,'\work'), '测试库路径选择...');

% --- Executes on button press in pushbutton3.

%function pushbutton3_Callback(hObject, eventdata, handles)

% hObject   handle to pushbutton3 (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles   structure with handles and user data (see GUIDATA)

%[filename,pathname]=uigetfile({'*.jpg';'*.bmp'},'••••••••');

%str=[pathname filename];

%im=imread(str);

%axes(handles.axes1);

%imshow(im);

% --- Executes on button press in pushbutton4.

function pushbutton4_Callback(hObject, eventdata, handles)

% hObject   handle to pushbutton4 (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles   structure with handles and user data (see GUIDATA)

global TrainDatabasePath ;

global TestDatabasePath;

global T;

T = CreateDatabase(TrainDatabasePath);

%[m V_PCA V_Fisher ProjectedImages_Fisher] = FisherfaceCore(T);

% --- Executes on button press in pushbutton5.

function pushbutton9_Callback(hObject, eventdata, handles)

% hObject   handle to pushbutton5 (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles   structure with handles and user data (see GUIDATA)

global im;

[filename,pathname]=uigetfile({'*.jpg';'*.bmp'},'选择测试图片...');

str=[pathname  filename];

im=imread(str);

axes(handles.axes1);

imshow(im);

% --- Executes on button press in pushbutton6.

function pushbutton6_Callback(hObject, eventdata, handles)

% hObject   handle to pushbutton6 (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles   structure with handles and user data (see GUIDATA)

%T = CreateDatabase(TrainDatabasePath);

global T;

global im;

global TrainDatabasePath ;

[m V_PCA V_Fisher ProjectedImages_Fisher] = FisherfaceCore(T);

OutputName = Recognition(im, m, V_PCA, V_Fisher, ProjectedImages_Fisher);

SelectedImage = strcat(TrainDatabasePath,'\',OutputName);

SelectedImage = imread(SelectedImage);

axes(handles.axes2);

imshow(SelectedImage);

%title('Equivalent Image');

% --- Executes on button press in pushbutton7.

function pushbutton7_Callback(hObject, eventdata, handles)

% hObject   handle to pushbutton7 (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles   structure with handles and user data (see GUIDATA)

% clear all;

% clc

% close(gcf);

global T;

global TrainDatabasePath ;

[m V_PCA V_Fisher ProjectedImages_Fisher] = FisherfaceCore(T);

file_path =  '.\TestDatabase\';% 图像文件夹路径

img_path_list = dir(strcat(file_path,'*.jpg'));%获取该文件夹中所有jpg格式的图像

img_num = length(img_path_list);%获取图像总数量

s=0;%识别成功个数

if img_num > 0 %有满足条件的图像

for j = 1:img_num %逐一读取图像

image_name = img_path_list(j).name;% 图像名

im =  imread(strcat(file_path,image_name));

[OutputName,indx] = Recognition(im, m, V_PCA, V_Fisher, ProjectedImages_Fisher);

if indx==j*2-1||indx==2*j

s=s+1;

end

end

end

shibielv=s/img_num*100;%识别率

set(handles.text3,'String',['识别率=',num2str(shibielv),'%']);

% --- Executes on button press in pushbutton10.

function pushbutton10_Callback(hObject, eventdata, handles)

% hObject   handle to pushbutton10 (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles   structure with handles and user data (see GUIDATA)

3 仿真结果

4 参考文献

[1]樊春玲, and 陈秀霆. "基于PCA和LDA的人脸识别系统设计." 控制工程 19.4(2012):4.

部分理论引用网络文献，若有侵权联系博主删除。