基于CNN卷积神经网络人脸朝向识别系统（GUI界面）【源码34期】

matlab代码

171人浏览 · 2026-06-28 08:51:29

matlab代码 · 2026-06-28 08:51:29 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。系统由两大核心模块组成：模型训练模块（main.m）负责构建和训练CNN网络，该网络采用420×420×1的灰度图像输入，包含多层卷积、批归一化、ReLU激活及池化操作，最终通过全连接层输出5分类结果（对应不同人脸朝向），训练过程使用SGDM优化器，迭代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，调用已训练模型进行识别，识别结果以文本和弹窗形式展示，同时提供模型加载和测试集整体准确率计算功能，便于用户验证模型性能

二、部分源码

function pushbutton1_Callback(hObject, eventdata, handles) % 选取图像

% 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)

[fn,pn,~]=uigetfile('*.bmp','请选择所要识别的图像');

I = imread([pn fn]);

axes(handles.axes1);

imshow(I,[]);

title('所选图像');

handles.I = I;

guidata(gcbo,handles);

% --- Executes on button press in pushbutton2.

function pushbutton2_Callback(hObject, eventdata, handles) % 进行识别

% 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)

load("CNNnet.mat");

y_pred = classify(CNNnet,handles.I);

% disp(y_pred);

set(handles.edit1,'string',y_pred);

result=strcat('人脸朝向：',string(y_pred),'');

msgbox(result,'识别结果','warn')

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

% --- Executes on button press in pushbutton3.

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);

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模型能够有效学习人脸朝向特征，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，用户只需选取图像即可获得识别结果，并伴有提示弹窗增强交互体验。系统适用于人脸朝向识别的研究演示与教学实验，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。