人脸识别MATLAB代码.doc

学习资料收集于网络，仅供参考1.色彩空间转换function r,g=rgb_RGB(Ori_Face)R=Ori_Face(:,:,1);G=Ori_Face(:,:,2);B=Ori_Face(:,:,3);R1=im2double(R); % 将uint8型转换成double型G1=im2double(G);B1=im2double(B);RGB=R1+G1+B1;row=size(Ori_Face,1); % 行像素column=size(Ori_Face,2); % 列像素for i=1:rowfor j=1:column rr(i,j)=R1(i,j)/RGB(i,j); gg(i,j)=G1(i,j)/RGB(i,j);endendrrr=mean(rr);r=mean(rrr);ggg=mean(gg);g=mean(ggg);2.均值和协方差t1=imread(D:matlab皮肤库1.jpg);r1,g1=rgb_RGB(t1);t2=imread(D:matlab皮肤库2.jpg);r2,g2=rgb_RGB(t2);t3=imread(D:matlab皮肤库3.jpg);r3,g3=rgb_RGB(t3);t4=imread(D:matlab皮肤库4.jpg);r4,g4=rgb_RGB(t4);t5=imread(D:matlab皮肤库5.jpg);r5,g5=rgb_RGB(t5);t6=imread(D:matlab皮肤库6.jpg);r6,g6=rgb_RGB(t6);t7=imread(D:matlab皮肤库7.jpg);r7,g7=rgb_RGB(t7);t8=imread(D:matlab皮肤库8.jpg);r8,g8=rgb_RGB(t8);t9=imread(D:matlab皮肤库9.jpg);r9,g9=rgb_RGB(t9);t10=imread(D:matlab皮肤库10.jpg);r10,g10=rgb_RGB(t10);t11=imread(D:matlab皮肤库11.jpg);r11,g11=rgb_RGB(t11);t12=imread(D:matlab皮肤库12.jpg);r12,g12=rgb_RGB(t12);t13=imread(D:matlab皮肤库13.jpg);r13,g13=rgb_RGB(t13);t14=imread(D:matlab皮肤库14.jpg);r14,g14=rgb_RGB(t14);t15=imread(D:matlab皮肤库15.jpg);r15,g15=rgb_RGB(t15);t16=imread(D:matlab皮肤库16.jpg);r16,g16=rgb_RGB(t16);t17=imread(D:matlab皮肤库17.jpg);r17,g17=rgb_RGB(t17);t18=imread(D:matlab皮肤库18.jpg);r18,g18=rgb_RGB(t18);t19=imread(D:matlab皮肤库19.jpg);r19,g19=rgb_RGB(t19);t20=imread(D:matlab皮肤库20.jpg);r20,g20=rgb_RGB(t20);t21=imread(D:matlab皮肤库21.jpg);r21,g21=rgb_RGB(t21);t22=imread(D:matlab皮肤库22.jpg);r22,g22=rgb_RGB(t22);t23=imread(D:matlab皮肤库23.jpg);r23,g23=rgb_RGB(t23);t24=imread(D:matlab皮肤库24.jpg);r24,g24=rgb_RGB(t24);t25=imread(D:matlab皮肤库25.jpg);r25,g25=rgb_RGB(t25);t26=imread(D:matlab皮肤库26.jpg);r26,g26=rgb_RGB(t26);t27=imread(D:matlab皮肤库27.jpg);r27,g27=rgb_RGB(t27);r=cat(1,r1,r2,r3,r4,r5,r6,r7,r8,r9,r10,r11,r12,r13,r14,r15,r16,r17,r18,r19,r20,r21,r22,r23,r24,r25,r26,r27);g=cat(1,g1,g2,g3,g4,g5,g6,g7,g8,g9,g10,g11,g12,g13,g14,g15,g16,g17,g18,g19,g20,g21,g22,g23,g24,g25,g26,g27);m=mean(r,g)n=cov(r,g)3.求质心function xmean, ymean = center(bw)bw=bwfill(bw,holes);area = bwarea(bw); m n =size(bw);bw=double(bw);xmean =0; ymean = 0;for i=1:m,for j=1:n,xmean = xmean + j*bw(i,j);ymean = ymean + i*bw(i,j);end;end;if(area=0) xmean=0; ymean=0;else xmean = xmean/area; ymean = ymean/area; xmean = round(xmean); ymean = round(ymean); end4. 求偏转角度function theta = orient(bw,xmean,ymean)m n =size(bw);bw=double(bw);a = 0; b = 0; c = 0;for i=1:m,for j=1:n,a = a + (j - xmean)2 * bw(i,j);b = b + (j - xmean) * (i - ymean) * bw(i,j);c = c + (i - ymean)2 * bw(i,j);end;end;b = 2 * b;theta = atan(b/(a-c)/2;theta = theta*(180/pi); % 从幅度转换到角度5. 找区域边界function left, right, up, down = bianjie(A)m n = size(A);left = -1;right = -1;up = -1;down = -1;for j=1:n, for i=1:m, if (A(i,j) = 0) left = j; break; end; end; if (left = -1) break; end;end;for j=n:-1:1,for i=1:m,if (A(i,j) = 0) right = j; break;end;end;if (right = -1) break; end;end;for i=1:m,for j=1:n, if (A(i,j) = 0) up = i; break; end;end; if (up = -1) break; end;end;for i=m:-1:1, for j=1:n, if (A(i,j) = 0) down = i; break; end; end; if (down = -1) break; end;end;6. 求起始坐标function newcoord = checklimit(coord,maxval)newcoord = coord;if (newcoordmaxval) newcoord=maxval;end;7.模板匹配function ccorr, mfit, RectCoord = mobanpipei(mult, frontalmodel,ly,wx,cx, cy, angle)frontalmodel=rgb2gray(frontalmodel); model_rot = imresize(frontalmodel,ly wx,bilinear); % 调整模板大小model_rot = imrotate(model_rot,angle,bilinear); % 旋转模板l,r,u,d = bianjie(model_rot); % 求边界坐标bwmodel_rot=imcrop(model_rot,l u (r-l) (d-u); % 选择模板人脸区域modx,mody =center(bwmodel_rot); % 求质心morig, norig = size(bwmodel_rot); % 产生一个覆盖了人脸模板的灰度图像mfit = zeros(size(mult);mfitbw = zeros(size(mult);limy, limx = size(mfit);% 计算原图像中人脸模板的坐标startx = cx-modx;starty = cy-mody;endx = startx + norig-1;endy = starty + morig-1;startx = checklimit(startx,limx);starty = checklimit(starty,limy);endx = checklimit(endx,limx);endy = checklimit(endy,limy);for i=starty:endy,for j=startx:endx,mfit(i,j) = model_rot(i-starty+1,j-startx+1);end;end;ccorr = corr2(mfit,mult) % 计算相关度l,r,u,d = bianjie(bwmodel_rot);sx = startx+l;sy = starty+u;RectCoord = sx sy (r-1) (d-u); % 产生矩形坐标8.主程序clear;fname,pname=uigetfile(*.jpg;*.bmp;*.tif;*.gif,Please choose a color picture.); % 返回打开的图片名与图片路径名u,v=size(fname); y=fname(v); % 图片格式代表值 switch y case 0 errordlg(You Should Load Image File First.,Warning.); caseg;G;p;P;f;F; % 图片格式若是JPG/jpg、BMP/bmp、TIF/tif或者GIF/gif，才打开 I=cat(2,pname,fname); Ori_Face=imread(I); subplot(2,3,1),imshow(Ori_Face);otherwise errordlg(You Should Load Image File First.,Warning.); endR=Ori_Face(:,:,1);G=Ori_Face(:,:,2);B=Ori_Face(:,:,3);R1=im2double(R); % 将uint8型转换成double型处理 G1=im2double(G);B1=im2double(B);RGB=R1+G1+B1;m= 0.4144,0.3174; % 均值n=0.0031,-0.0004;-0.0004,0.0003; % 方差row=size(Ori_Face,1); % 行像素数column=size(Ori_Face,2); % 列像素数for i=1:row for j=1:column if RGB(i,j)=0 rr(i,j)=0;gg(i,j)=0; else rr(i,j)=R1(i,j)/RGB(i,j); % rgb归一化 gg(i,j)=G1(i,j)/RGB(i,j); x=rr(i,j),gg(i,j); p(i,j)=exp(-0.5)*(x-m)*inv(n)*(x-m); % 皮肤概率服从高斯分布 end endendsubplot(2,3,2);imshow(p); % 显示皮肤灰度图像low_pass=1/9*ones(3);image_low=filter2(low_pass, p); % 低通滤波去噪声subplot(2,3,3);imshow(image_low); % 自适应阀值程序previousSkin2 = zeros(i,j);changelist = ;for threshold = 0.55:-0.1:0.05two_value = zeros(i,j);two_value(find(image_lowthreshold) = 1;change = sum(sum(two_value - previousSkin2);changelist = changelist change;previousSkin2 = two_value;endC, I = min(changelist);optimalThreshold = (7-I)*0.1two_value = zeros(i,j);two_value(find(image_lowoptimalThreshold) = 1; % 二值化subplot(2,3,4);imshow(two_value); % 显示二值图像frontalmodel=imread(E:我的照片人脸模板.jpg); % 读入人脸模板照片FaceCoord=;imsourcegray=rgb2gray(Ori_Face); % 将原照片转换为灰度图像L,N=bwlabel(two_value,8); % 标注二值图像中连接的部分,L为数据矩阵，N为颗粒的个数 for i=1:N,x,y=find(bwlabel(two_value)=i); % 寻找矩阵中标号为i的行和列的下标bwsegment = bwselect(two_value,y,x,8); % 选择出第i个颗粒numholes = 1-bweuler(bwsegment,4); % 计算此区域的空洞数if (numholes = 1) % 若此区域至少包含一个洞，则将其选出进行下一步运算RectCoord = -1;m n = size(bwsegment);cx,cy=center(bwsegment); % 求此区域的质心bwnohole=bwfill(bwsegment,holes); % 将洞封住（将灰度值赋为1）justface = uint8(double(bwnohole) .* double(imsourcegray); % 只在原照片的灰度图像中保留该候选区域angle = orient(bwsegment,cx,cy); % 求此区域的偏转角度 bw = imrotate(bwsegment, angle, bilinear);bw = bwfill(bw,holes);l,r,u,d =bianjie(bw);wx = (r - l +1); % 宽度ly = (d - u + 1); % 高度wratio = ly/wx % 高宽比 if (0.8=wratio)&(wratio0.35) ccorr,mfit, RectCoord = mobanpipei(justface,frontalmodel,ly,wx, cx,cy, angle); end if (ccorr=0.6) mfitbw=(mfit=1); invbw = xor(mfitbw,ones(size(mfitbw); source_with_hole = uint8(double(invbw) .* double(imsourcegray); final_image = uint8(double(source_with_hole) + double(mfit); subplot(2,3,5);imshow(final_image); % 显示覆盖了模板脸的灰度图像 imsourcegray = final_image; subplot(2,3,6);imshow(Ori_Face); % 显示检测效果图 end; if (RectCoord = -1) FaceCoord = FaceCoord; RectCoord; end endendend% 在认为是人脸的区域画矩形 numfaces x = size(FaceCoord); for i=1:numfaces, hd = rectangle(Position,FaceCoord(i,:); set(hd, edgecolor, y); end人脸检测是人脸识别、人机交互、智能视觉监控等工作的前提。近年来,在模式识别与计算机视觉领域,人脸检测己经成为一个受到普遍重视、研究十分活跃的方向。 本文针对复杂背景下的彩色正面人脸图像,将肤色分割、模板匹配与候选人脸图像块筛选结合起来,构建了人脸检测实验系统,并用自制的人脸图像数据库在该系统下进行了一系列的实验统计。 本文首先介绍了人脸检测技术研究的背景和现状,阐明人脸检测技术发展的重要意义,对目前常用的一些检测算法进行了总结,然后着重阐述了基于肤色分割和模板匹配验证的人脸检测算法。 肤色是人脸重要特征,在通过肤色采样统计和聚类分析后,确立一种在YCb