基于CNN卷积神经网络人脸朝向识别系统数字图像处理（GUI界面）【源码35期】

matlab代码

214人浏览 · 2026-07-02 20:37:41

matlab代码 · 2026-07-02 20:37:41 发布

一、项目简介

本系统基于MATLAB深度学习工具箱，设计并实现了一个基于卷积神经网络（CNN）的人脸朝向识别系统。系统包含两大核心模块：模型训练模块（main.m）自动加载按文件夹分类的图像数据集，按8:2比例划分为训练集与测试集，构建包含多层卷积、批归一化、ReLU激活及池化操作的CNN网络，输入层采用420×420×1的灰度图像，输出层为5分类（对应不同人脸朝向），利用SGDM优化器训练25个周期后保存模型参数；图形用户界面模块（page.m与page.fig）基于GUIDE框架开发，用户可通过界面按钮选取BMP格式图像，系统自动显示原始图像，并提供灰度化与二值化预处理功能，调用已训练模型进行识别后将结果显示于界面中，同时支持测试集整体准确率的一键计算与展示

二、部分源码

function pushbutton3_Callback(hObject, eventdata, handles) % 显示整个测试集的结果

% 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)

%% 加载数据

allImages = imageDatastore('dbx', ...

'IncludeSubfolders' ,true, ...

'LabelSource' , 'foldernames' );% 图像加载为图像数据存储

% imageDatastore函数会根据文件夹名称自动标记图像

% 划分训练集(80%)和测试集(20%)

[imgsTrain,imgsTest] = splitEachLabel(allImages,0.8,'randomized');

load("CNNnet.mat");

y_pred = classify(CNNnet,imgsTest); % 使用训练好的网络测试

accuracy = mean(y_pred == imgsTest.Labels);% 计算准确率

set(handles.text2,'string',['总体准确率: ',num2str(100*accuracy),'%'],'FontSize',12);

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 during object creation, after setting all properties.

function text1_CreateFcn(hObject, eventdata, handles)

% hObject handle to text1 (see GCBO)

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

% handles empty - handles not created until after all CreateFcns called

% --- Executes during object deletion, before destroying properties.

function text5_DeleteFcn(hObject, eventdata, handles)

% hObject handle to text5 (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 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 I

global Data

if size(I,3) ==3

Data = rgb2gray(I);

else

Data = I;

end

axes(handles.axes2)

imshow(Data);

title('灰度图像');

% --- Executes on button press in pushbutton5.

function pushbutton5_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 Data

thresh = graythresh(Data);

It=im2bw(Data,thresh);

axes(handles.axes3)

imshow(It);title('二值图像');

三、运行结果

四、总结

该系统在测试集上达到了100%的识别准确率，表明所构建的CNN模型能够有效提取人脸朝向特征，对当前数据集具有良好的分类能力。界面设计简洁直观，操作流程清晰，用户可依次完成图像选取、预处理可视化和朝向识别，交互体验良好。系统适用于人脸朝向识别的研究演示与教学实验场景，后续可考虑引入更多样化的数据集、增加数据增强策略以提高模型泛化能力，或进一步优化网络结构以降低计算开销，便于向嵌入式或实时应用场景迁移。