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【人脸识别】基于 Gabor+SV和PCA+SVM实现人脸识别matlab源码含 GUI

2021-08-05 00:05 作者:Matlab工程师  | 我要投稿

一、简介

Gabor+SVM:利用Gabor程序实现对人脸的特征提取,然后用SVM进行分类;
1 Gabor
Gabor 特征提取算法可以在不同方向上描述局部人脸特征,对光照、遮挡以及表情变换等情况具有较强的鲁棒性,即Gabor算法在异常和危险情况下具有较强的系统生存的能力。

1.1 一维Gabor核:
其由一个高斯核与一个复数波的乘积定义为如下公式:

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其中w(t)是高斯函数,s(t)是复数波,两者的一维数学表达式定义如下:

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我们将s(t)代入一维Gabor公式可得下式:

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我们将上述一维情况推广到二维
二维复数波定义如下,其中(x,y)表示空间域坐标,(u0,v0)表示频率域坐标。

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二维高斯函数定义如下,其中σx,σy 分别为在x,y两个方向上的尺度参数,用来控制高斯函数在两个方向上的“展布”形状。(x0,y0)为高斯函数的中心点。K为高斯核的幅度的比例。

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但是由于高斯函数还有旋转的操作,所以我们对坐标进行如下的变换:

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由此,我们得到了坐标变换后的高斯函数公式,其中θ表示高斯核顺时针旋转的角度。

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1.2 二维Gabor核
类似一维 Gabor 核,我们将二维高斯函数与二维复数波相乘,就得到了二维的Gabor核:

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一个Gabor核能获取到图像某个频率邻域的响应情况,这个响应结果可以看做是图像的一个特征。如果我们用多个不同频率的Gabor核去获取图像在不同频率邻域的响应情况,最后就能形成图像在各个频率段的特征,这个特征就可以描述图像的频率信息了。

下图展示了一系列具有不同频率的 Gabor 核,用这些核与图像卷积,我们就能得到图像上每个点和其附近区域的频率分布情况。

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经过 Gabor 滤波获到的人脸图像信息包含实部和虚部两部分,分别代表不同局部的人脸特征信息,为了提取更加全面的人脸特征信息,一般会采用两种特征值相结合的方法,比如幅值和相位信息。但 Gabor 的相位信息会因为人脸空间位置发生改变而不太稳定。Gabor 幅值信息变化相对稳定,并且充分反映了人脸图像的能量谱。因此采取 Gabor 幅值特征。经过Gabor幅值特征处理,得到了人脸 Gabor 特征信息。5 个尺度,8 个方向的 Gabor 特征提取图如下所示:

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2 PCA+SVM:
2.1 PCA
主成分分析(Principal Component Analysis, 简称PCA)是常用的一种降维方法.
算法步骤:

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2.2 SVM介绍
支持向量机(Support Vector Machines, 简称SVM)是一种二类分类模型.
划分超平面为:

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3 人脸识别步骤
将每张人脸图片(m,nm,n)读取并展开成(m×n,1m×n,1), 假设总有ll张图片, 所有排列到一起, 一列为一张图片, 最终形成一个(m×n,l)(m×n,l) 的矩阵作为原始数据;
数据中心化: 计算平均脸, 所有列都减去张平均脸;
计算矩阵的协方差矩阵/散布矩阵, 求出特征值及特征向量, 并将其从大到小排列取前K个特征; (到这步特征已将至K维)
计算中心化后的数据在K维特征的投影;
基于上一步的数据进行 One-VS-One Multiclass SVM模型训练;
读取用于测试的人脸图片, 同训练图片一样处理;
利用训练出的模型对测试图片进行分类;
计算准确率.

二、源代码

function varargout = pjimage(varargin) % PJIMAGE MATLAB code for pjimage.fig %      PJIMAGE, by itself, creates a new PJIMAGE or raises the existing %      singleton*. % %      H = PJIMAGE returns the handle to a new PJIMAGE or the handle to %      the existing singleton*. % %      PJIMAGE('CALLBACK',hObject,eventData,handles,...) calls the local %      function named CALLBACK in PJIMAGE.M with the given input arguments. % %      PJIMAGE('Property','Value',...) creates a new PJIMAGE or raises the %      existing singleton*.  Starting from the left, property value pairs are %      applied to the GUI before pjimage_OpeningFcn gets called.  An %      unrecognized property name or invalid value makes property application %      stop.  All inputs are passed to pjimage_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 pjimage % Last Modified by GUIDE v2.5 11-Jun-2018 08:06:08 % Begin initialization code - DO NOT EDIT gui_Singleton = 1; gui_State = struct('gui_Name',       mfilename, ...                   'gui_Singleton',  gui_Singleton, ...                   'gui_OpeningFcn', @pjimage_OpeningFcn, ...                   'gui_OutputFcn',  @pjimage_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 pjimage is made visible. function pjimage_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 pjimage (see VARARGIN) % Choose default command line output for pjimage handles.output = hObject; % Update handles structure guidata(hObject, handles); % UIWAIT makes pjimage wait for user response (see UIRESUME) % uiwait(handles.figure_pjimage); % --- Outputs from this function are returned to the command line. function varargout = pjimage_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; % -------------------------------------------------------------------- function m_file_Callback(hObject, eventdata, handles) % hObject    handle to m_file (see GCBO) % eventdata  reserved - to be defined in a future version of MATLAB % handles    structure with handles and user data (see GUIDATA) % -------------------------------------------------------------------- function m_file_open_Callback(hObject, eventdata, handles) % hObject    handle to m_file_open (see GCBO) % eventdata  reserved - to be defined in a future version of MATLAB % handles    structure with handles and user data (see GUIDATA) % -------------------------------------------------------------------- function m_file_save_Callback(hObject, eventdata, handles) % hObject    handle to m_file_save (see GCBO) % eventdata  reserved - to be defined in a future version of MATLAB % handles    structure with handles and user data (see GUIDATA) % -------------------------------------------------------------------- function m_file_exit_Callback(hObject, eventdata, handles) % hObject    handle to m_file_exit (see GCBO) % eventdata  reserved - to be defined in a future version of MATLAB % handles    structure with handles and user data (see GUIDATA) % --- 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) figure(1); for i = 1:40    a = imread(strcat('C:\Users\lenovo\Desktop\人脸识别\人脸识别程序\ORL\s', num2str(i), '\1.pgm'));    subplot(5,8,i);    imshow(a); end % --- 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) figure(2); r = round(112 / 2); c = round(92 / 2); gamma = 0.5; theta = pi / 8; a = sqrt(2); fmax = 0.22; for u = 0 : 4     f = a ^ (-u) * fmax;     lambda = 1 / f;     for v = 0 : 7         sigma = 0.56 * lambda;         GK = getGaborKernel(r ,c ,v * theta ,sigma ,lambda ,gamma);%得到一个方向一个尺度的Gabor图像         subplot(5,8, u*8 + v + 1);         imshow(GK);     end end % --- 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) p = imread('C:\Users\lenovo\Desktop\人脸识别\人脸识别程序\ORL\s1\1.pgm'); p = double(p); [m , n] = size(p); r = round(m / 2); c = round(n / 2); gamma = 0.5; theta = pi / 8; a = sqrt(2); fmax = 0.22; figure(3); for u = 0 : 4     f = a ^ (-u) * fmax;     lambda = 1 / f;     for v = 0 : 7         sigma = 0.56 * lambda;         GK = getGaborKernel(r ,c ,v * theta ,sigma ,lambda ,gamma);%得到一个方向一个尺度的Gabor图像         x = conv2(p,GK,'same');%原图像与Gabor图像进行卷积   112 92         subplot(5, 8, u*8 + v +1);         imshow(x);     end end % --- Executes during object deletion, before destroying properties. function axes1_DeleteFcn(hObject, eventdata, handles) % hObject    handle to axes1 (see GCBO) % eventdata  reserved - to be defined in a future version of MATLAB % handles    structure with handles and user data (see GUIDATA) function edit1_Callback(hObject, eventdata, handles) % hObject    handle to edit1 (see GCBO) % eventdata  reserved - to be defined in a future version of MATLAB % handles    structure with handles and user data (see GUIDATA) % Hints: get(hObject,'String') returns contents of edit1 as text %        str2double(get(hObject,'String')) returns contents of edit1 as a double % --- Executes during object creation, after setting all properties. function edit1_CreateFcn(hObject, eventdata, handles) % hObject    handle to edit1 (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 function edit2_Callback(hObject, eventdata, handles) % hObject    handle to edit2 (see GCBO) % eventdata  reserved - to be defined in a future version of MATLAB % handles    structure with handles and user data (see GUIDATA) % Hints: get(hObject,'String') returns contents of edit2 as text %        str2double(get(hObject,'String')) returns contents of edit2 as a double % --- Executes during object creation, after setting all properties. function edit2_CreateFcn(hObject, eventdata, handles) % hObject    handle to edit2 (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 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) global ttlabel; global prelabel; % global ct; % global gam; trainLabel = []; k = 1; v = 1; %共280张图片 for i = 1 : 40                                  %40个人    for j = 1 : 7                                 %每个人7张照片        a = imread(strcat('C:\Users\lenovo\Desktop\人脸识别\人脸识别程序\ORL\s', num2str(i),'\', num2str(j), '.pgm'));        a = double(a);        [m,n] = size(a);                trainvector = GetOneImageVector(a);        trainX(:, k) = trainvector;        k = k + 1;        %加标签            trainLabel = [trainLabel v];            %1X280    end    v = v + 1; end %归一化  均值向量  方差向量   trainx = Normalize(trainX);       %6440X280 % ct =str2double(get(handles.edit3,'String')); % gam = str2double(get(handles.edit4,'String')); %使用SVM得到模型 model = svmtrain(trainLabel', trainx','-s 0 -t 2 -c 1000 -g 0.0001'); % set(handles.edit1,'string',model); %处理测试集 u = 1; t = 1; testLabel = []; for i = 1:40    for j = 8:10        a = imread(strcat('C:\Users\lenovo\Desktop\人脸识别\人脸识别程序\ORL\s', num2str(i),'\', num2str(j), '.pgm'));        a = double(a);        [m,n] = size(a);                  testvector = GetOneImageVector(a);        testX(:, u) = testvector;        u = u + 1;        testLabel = [testLabel t];    end     t = t + 1; end

三、运行结果

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【人脸识别】基于 Gabor+SV和PCA+SVM实现人脸识别matlab源码含 GUI的评论 (共 条)

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