runpcakernel.m

It is for Face Recognition

    subplot(2,2,k);hold on; 
    plot(nT2Faces(2*k-1,:), nT2Faces(2*k,:), '.b');
    plot(t2Faces(2*k-1,:), t2Faces(2*k,:), '+r');
    str=sprintf('%s%d-%d', '\fontsize{12}',2*k-1, 2*k); xlabel(str);
end
title('\fontsize{12} Upper Face Distribution on PCA 3-5');

figure; hold on;
d1=4; d2=7; 
plot(nT2Faces(d1,:), nT2Faces(d2,:), '.b'); 
plot(t2Faces(d1,:), t2Faces(d2,:), '+r');
xlabel('\fontsize{12} Upper (T2) Face Distribution on PCA 4-7');

% plot lower face on dim 1 and 2. 
figure; 
for k=1:4
    subplot(2,2,k);hold on; 
    plot(nT3Faces(2*k-1,:), nT3Faces(2*k,:), '.b');
    plot(t3Faces(2*k-1,:), t3Faces(2*k,:), '+r');
    str=sprintf('%s%d-%d', '\fontsize{12}',2*k-1, 2*k); xlabel(str);
end
title('\fontsize{12} Lower (T3) Face Distribution on PCA 3-5');

figure;hold on;d1=1;d2=6;
plot(nT3Faces(d1,:), nT3Faces(d2,:), '.b');
plot(t3Faces(d1,:), t3Faces(d2,:), '+r');
title('\fontsize{12} Lower (T3) Face Distribution on PCA 1-6');

p=8;q=4;alpha=0.02; beta=0.1; thres=0.5;
[relevance] = relevanceRegression( p, q, alpha, beta, thres)

%%%%%%%%%%%%%%%%%%%%%%%%%%%
% non-linear mapping
% 12-11-2003.
%%%%%%%%%%%%%%%%%%%%%%%%%%%
CovT1Faces = cov(t1Faces');
CovT2Faces = cov(t2Faces');
CovT3Faces = cov(t3Faces');

StdT1Faces = sqrt(diag(CovT1Faces))
% [258.6135   99.7751   92.8625   79.4257   60.8324   54.7351   53.1098   46.3724   45.6027   41.6553   40.5613   38.7417];
StdT2Faces = sqrt(diag(CovT2Faces))
% [191.6423   92.2207   75.4775   58.2254   49.4999   41.9451   39.8454   37.1355];
StdT3Faces = sqrt(diag(CovT3Faces))
% [187.1485   67.5272   54.5260   49.3976   46.3355   40.8735   34.2275   30.2965]


for k=1:length(faces(1,:))
  x = faces(:,0+k);
  px(k)=faceProb(T1, T2, T3, mT1Face, mT2Face, mT3Face, x, '.b');
end

for k=1:length(nFaces(1,:))
  y = nFaces(:,0+k);
  py(k)=faceProb(T1, T2, T3, mT1Face, mT2Face, mT3Face, y, '.r');
end

for k=1:1000
  x = faces(:,0+k);
  y = nFaces(:,0+k);
  figure(1);
  title('\fontsize{12}nonfaces distrb');
  subplot(2,1,2);px(k)=faceProb(T1, T2, T3, mT1Face, mT2Face, mT3Face, y, '.b');
  title('\fontsize{12}faces distrb');
  subplot(2,1,1);py(k)=faceProb(T1, T2, T3, mT1Face, mT2Face, mT3Face, x, '.r');
  %figure(2); hold on;
  %faceProb(T1, T2, T3, mT1Face, mT2Face, mT3Face, y, '.b');
  %faceProb(T1, T2, T3, mT1Face, mT2Face, mT3Face, x, '.r');
end
title('\fontsize{12}faces vs nonfaces distrb');

%%%%%%%%%%%%%%%%%%%%%%%%%%%
% face detection
% 12-11-2003.
%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [pos]=faceDetection(img, sWin, T1, T2, T3, mT1Face, mT2Face, mT3Face)
 dbg = 'y';
 if dbg == 'y'   
   sWin= [24, 32; 30, 45]; 
   img = imread('k:/work/Face/matlab/seinfeld3.jpg', 'jpg');
   imshow(img, [0 255]);     
 end

 % const
 W = 12; H=16; 

 % var
 [h w] = size(img);
 nScale = length(sWin(:,1)); 
 cnt = 0;

 hold on;
 pos=[];
 for s=1:nScale
   % search window size
   sW=sWin(1,s); sH=sWin(2,s);
   fprintf('\n scale=%d, sW=%d sH=%d', s, sW, sH);
   % search
   for y=1:h-sH
   for x=1:w-sW
      face = img(y:y+sH-1,x:x+sW-1);
      [icon, vec]=scaleImage(face, W, H);
      prob = faceProb(T1, T2, T3, mT1Face, mT2Face, mT3Face, vec, '.r');
      if prob == 1
        cnt = cnt+1;
        pos(cnt,1:4) = [x y sW sH];   
        fprintf('+');  
        %plotBox(x,y,sW,sH, '-r');   
      end     
   end
   fprintf('\n >');
   end
 end



%%%%%%%%%%%%%%%%%%%%%%%%%%%
% face detection: SVM
% 02-09-2004.
%%%%%%%%%%%%%%%%%%%%%%%%%%%
clear;
path(path, 'd:\work\SVMToolbox');
W=12; H=16; 
% load PCA transforms
T1 = load('pcaFaceW12H16.dat');
T2 = load('pcaUpperFaceW12H16.dat');
T3 = load('pcaLowerFaceW12H16.dat');

% load face/nonface training data
faces = load('faFacesW12H16.dat'); 
[d n]=size(faces);
upFaces=faces(1:d/2,1:n);loFaces=faces(1+d/2:192,1:n);
mFace = mean(faces')'; mUpFace = mean(upFaces')'; mLoFace = mean(loFaces')';
nFaces = load('nonFacesW12H16.dat');
mT1Face = T1*mFace; mT2Face = T2*mUpFace; mT3Face = T3*mLoFace;

% compute transformed faces/nonfaces
t1Faces= T1*faces;      nT1Faces=T1*nFaces;
t2Faces= T2*upFaces;    nT2Faces=T2*nFaces(1:96,:);
t3Faces= T3*loFaces;    nT3Faces=T3*nFaces(97:192,:);

% remove mean
for j=1:length(faces(1,:))
 t1Faces(:,j)=t1Faces(:,j) - mT1Face;
 t2Faces(:,j)=t2Faces(:,j) - mT2Face;
 t3Faces(:,j)=t3Faces(:,j) - mT3Face;
end

% remove mean
for j=1:length(nFaces(1,:))
 nT1Faces(:,j)= nT1Faces(:,j)- mT1Face;
 nT2Faces(:,j)= nT2Faces(:,j)- mT2Face;
 nT3Faces(:,j)= nT3Faces(:,j)- mT3Face;
end

saveModel = 'n';
if saveModel == 'y
% save models
save 't1Faces.dat' t1Faces -ascii;
save 't2Faces.dat' t2Faces -ascii;
save 't3Faces.dat' t3Faces -ascii;

save 'nT1Faces.dat' nT1Faces -ascii;
save 'nT2Faces.dat' nT2Faces -ascii;
save 'nT3Faces.dat' nT3Faces -ascii;
end

% train SVM 1
X1 = [t1Faces, nT1Faces]';
Y1 = [ones(length(t1Faces(1,:)), 1)', -1.*ones(length(nT1Faces(1,:)),1)']';
ker = 'poly', u=2, v=1;
C= 10;
[nsv, alpha, b0] = svc(X1,Y1, ker, C);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% face detect
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clear;
img = imread('faceTest2.jpg', 'jpg');
sWin = [24 32;29 36];
styl = '-r';
svmFaceDetection(img, sWin, styl);

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% face recog
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clear;
% nfaceW16H24;
% save 'nfacew16h24.mat' nfacew16h24;

load 'nfacew16h24.mat';

truth = load('feretGndTruth.dat');
[n w]=size(truth);
offs = 400;
fw = 20; fh = 30; side = 0.5; top= 1.2; low = 1.8;
for k=1:60
  subplot(6,10,k); 
  [icon]=getFeretFaceIcon(truth, 'y', offs+k, side, top, low, fw, fh);
  axis off;
end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%  compute face icons 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clear;
path(path, 'k:/work/SVMToolbox/svm');
truth = load('feretGndTruth.dat');
[n w]=size(truth);
m=0; cnt=0; doHistEq='n';
%side = 0.5; top= 1.2; low = 1.8; fw=12; fh=18; 
side = 0.4; top= 0.6; low = 0.6; fw=15; fh=20; 
% test
view_face = 'y';
if view_face == 'y'
offs = 300;
for k=1:24
 subplot(4,6,k); getFeretFaceIcon(truth, doHistEq, offs+k, side, top, low, fw, fh);
 axis off;
end
end

for k=1:n
   if k == 2438     % bad annotation
     icon == -1;
   else
     [icon]=getFeretFaceIcon(truth, doHistEq, k, side, top, low,fw, fh);
   end
   if icon ~= -1
      m=m+1; fprintf('.');
      faceIcons(m,1:fw*fh)=icon;      
   else
      fprintf('x');
   end
   cnt = cnt + 1;
   if  cnt >= 100
     cnt =0; fprintf('\n');
   end
end
size(faceIcons)
%save 'faceIconsW12H18HistEqed.mat' faceIcons;
save 'faceIconsW12H18NoHistEq.mat' faceIcons;

%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%  PCA
%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clear;
load 'faceIconsW16H24NoHistEq.mat';
x = double(faceIcons');
[T, lat, mx]=pcaAnalysis(x, 24);
save 'meanfaceIconsW16H24NoHistEq.dat' mx -ascii;
save 'pcafaceIconsW16H24NoHistEq.dat' T -ascii;
% proj icons to PCA space
[d n]=size(x);
figure; hold on;
for k=1:n
  y(:,k) = T*(x(:,k)-mx');
  plot(y(1,k), y(2,k), '.');
end
save 'pctfaceIconsW16H24NoHistEq.dat' y -ascii;

clear;
load 'faceIconsW16H24HistEqed.mat';
x = double(faceIcons');
[T, lat, mx]=pcaAnalysis(x, 24);
save 'meanfaceIconsW16H24HistEqed.dat' mx -ascii;
save 'pcafaceIconsW16H24HistEqed.dat' T -ascii;
% proj icons to PCA space
[d n]=size(x);
hold on;
for k=1:n
  y(:,k) = T*(x(:,k)-mx');
  plot(y(1,k), y(2,k), '.r');
end
save 'pctfaceIconsW16H24HistEqed.dat' y -ascii;

clear;
x = load('pctfaceIconsW16H24NoHistEq.dat');
y = load('pctfaceIconsW16H24HistEqed.dat');
plot(x(1,:), x(2,:), '.'); hold on; plot(y(1,:), y(2,:), '.r');

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% SVM
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% make data
clear;
path(path, 'k:/work/SVMToolbox');

n1 = 20; n2 = 30;
axis([0 40 0 40]); hold on;
fprintf('\n input samples for class +1: ');
for j=1:n1
       [X(j,1) X(j,2)] = ginput(1);
       plot(X(j,1), X(j,2), '+r');
       Y(j) = 1;fprintf('+');
end
fprintf('\n input samples for class -1: ');
for j=n1+1:n1+n2
       [X(j,1) X(j,2)] = ginput(1);
       plot(X(j,1), X(j,2), '.b');
       Y(j) = -1; fprintf('.');
end    
Y = Y';  

C= inf; % no slack, alpha >=0

[N, alpha, b0] = svm(X, Y, C);

face=load('faFacesW12H16.dat');
nface = load('nonFaceW12H16.dat');

% proj:
face = load('t2face.dat');
nface = load('t2nface.dat');
hold on; d1=4; d2=7;
plot(face(d1,:), face(d2,:), '+r');plot(nface(d1,:), nface(d2,:), '.b');

X=[face nface]';
Y=[ones(1, 1118) -ones(1,1800)]';
C=50;
[N, alpha, b0] = svm(X, Y, C);
save 'alphaC50.mat' alpha;




%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% sort samples by mean luma
% switch model in SVM
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clear;
load 'nfacew16h24.mat'; 
load 'faceIconsW16H24NoHistEq.mat'; 
face = faceIcons;
nface = nfacew16h24'; 

% sort face by mean luma
luma = mean(face');
%stem(luma, '.');
[v, sort_indx]=sort(luma);
max_v = max(v); min_v = min(v); std_v = std(v); mean_v=mean(v);

m=0; v0 = 80; v1 = 150;
for k=1:length(v)
  if v(k) > v0 & v(k) < v1
     m=m+1; sface(m,:)=face(sort_indx(k),:);
  end
end

mf =load('meanfaceIconsW16H24NoHistEq.dat');
T  =load('pcafaceIconsW16H24NoHistEq.dat');

% proj nonface
nface = double(nface);
for k=1:length(nface(:,1))
  pctNFace(:,k) = T*(nface(k,:)-mf)';
end
% proj face
sface = double(sface);
for k=1:length(sface(:,1))
  pctFace(:,k) = T*(sface(k,:)-mf)';
end

hold on; d1=5; d2=9; 
plot(pctNFace(d1,:), pctNFace(d2,:), '.');
offs=1; n=400;
plot(pctFace(d1,offs:offs+n), pctFace(d2,offs:offs+n), '+r');
offs=401; 
plot(pctFace(d1,offs:offs+n), pctFace(d2,offs:offs+n), '+m');
offs=801; 
plot(pctFace(d1,offs:offs+n), pctFace(d2,offs:offs+n), '+g');
offs=1201; 
plot(pctFace(d1,offs:offs+n), pctFace(d2,offs:offs+n), '+y');
offs=1601; 
plot(pctFace(d1,offs:offs+n), pctFace(d2,offs:offs+n), '+k');

xstr = sprintf('%s comp no. %d', '\fontsize{12}',d1);
ystr = sprintf('%s comp no. %d', '\fontsize{12}',d2);
xlabel(xstr); ylabel(ystr);