人脸识别LBP的MATLAB实现

人脸识别一般包括人脸检测、识别2大方面，其中有些还需要对人脸进行校正，定位以便达到更准确。本文主要在检测和识别2个方面，做个简单的实时的实现。

%% 导入图像imds = imageDatastore('E:\vs2010_work\test1\test1\data',...    'LabelSource','foldernames',...    'includesubfolder',true,...    'FileExtensions','.jpg');

文件夹名自己命名知道是某个人就好了，即标签。导入的图像形如
%% 检测并保存人脸为png格式faceDetector = vision.CascadeObjectDetector;for i = 1:length(imds.Files)    I = readimage(imds,i);    bboxes = step(faceDetector, I);    maxRect = [];    for j = 1:size(bboxes,1)        if bboxes(j,3)>=bboxes(1,3)            maxRect = bboxes(j,:);        end    end    if ~isempty(maxRect)        faceImg = imcrop(I,maxRect);        [dstPath,imageName,~] = fileparts(imds.Files{i});        imwrite(faceImg,fullfile(dstPath,[imageName,'.png']));    endend%% 导入检测到的人脸图像pngimdsFace = imageDatastore('E:\vs2010_work\test1\test1\data',...    'LabelSource','foldernames',...    'includesubfolder',true,...    'FileExtensions','.png',...    'ReadFcn',@IMAGERESIZE);[imdsTrain,imdsTest] = splitEachLabel(imdsFace,10);%% 画图看训练的数据集numsCategories = length(categories(imdsTrain.Labels));I = zeros(150,150,3,numsCategories*10);% 图像大小为150*150*3，有numsCategories*10张图像for i = 1:numsCategories*10    I(:,:,:,i) = readimage(imdsTrain,i);endmontage(uint8(I),'size',[numsCategories,10]);%% 提取LBP特征numTrain = length(imdsTrain.Labels);image1 = rgb2gray(readimage(imdsTrain,1));feature1 = extractLBPFeatures(image1, 'Upright',true,'cellsize',[18 18]);featuresMat = zeros(numTrain,length(feature1));for i = 1:numTrain    image = rgb2gray(readimage(imdsTrain,i));    featuresMat(i,:) = extractLBPFeatures(image, 'Upright',true,'cellsize',[18 18]);end%% testnumCorrect = 0;numTest =  length(imdsTest.Files);for i = 1:numTest     grayTest = rgb2gray(readimage(imdsTest,i));    featureTest = extractLBPFeatures(grayTest,'Upright',true,'cellsize',[18 18]);    actualLabel = imdsTest.Labels(i);        %% predict    minDist = realmax;    minIndex = 1;    for j =1:numTrain        currentDist = sum((featureTest-featuresMat(j,:)).^2);        if currentDist<minDist            minDist = currentDist;            predictLabel = imdsTrain.Labels(j);% categorical类型            minIndex = j;        end    end        %% 统计正确的个数    grayActual = rgb2gray(readimage(imdsTrain,minIndex));    figure;imshowpair(grayActual,grayTest,'montage');    if predictLabel == actualLabel        numCorrect = numCorrect+1;    endenddisp(['正确率：',num2str(numCorrect/numTest)])%% -----------------------------------隔断-----------------%% 识别并显示cam = webcam();% 摄像头接口，没有的话从matlab central网站搜索下载detector = vision.CascadeObjectDetector('lbpcascade_frontalface.xml');detector.MinSize = [110,110];videoPlayer = vision.VideoPlayer;fig = figure;axes('parent',fig)while ishandle(fig)    I = snapshot(cam);    bboxes = step(detector, I);    maxRect = [];    for j = 1:size(bboxes,1)        if bboxes(j,3)>=bboxes(1,3)            maxRect = bboxes(j,:);        end    end        if ~isempty(maxRect)&&(maxRect(1,3)>100)        faceImg = imcrop(I,maxRect);        grayTest = rgb2gray(faceImg);        grayTest = imresize(grayTest,[150,150]);        featureTest = extractLBPFeatures(grayTest,'Upright',true,'cellsize',[18 18]);                %% predict        minDist = realmax;        minIndex = 1;        for j =1:numTrain            currentDist = sum((featureTest-featuresMat(j,:)).^2);            if currentDist<minDist                minDist = currentDist;                predictLabel = imdsTrain.Labels(j);% categorical类型                minIndex = j;            end        end                strLabels = char(predictLabel);        RGB = insertObjectAnnotation(I,'rectangle',maxRect,strLabels,'color','g');        step(videoPlayer,RGB);    endend

其中用到的IMAGERESIZE函数为：

function output = IMAGERESIZE(input)input = imread(input);output = imresize(input,[150,150]);