runpcakernel.m

It is for Face Recognition

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Use Borg Linux Cluster
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
source /tools/env/borg.env
 bjobs
 bsuba -Is /tools/matlab/matlab.650/bin/matlab -nodisplay


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Feret images and ground truth 
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clear;
truth = load('faGndTruth.dat');
%truth = load('fbGndTruth.dat');
[n d]=size(truth);

offs = 811; 
[face]=showFeretFace(truth, offs);
figure;
for k=1:12
  subplot(3,4,k); showFeretFace(truth, offs+k);
end

% extract eyes-mouth geometry constraint
  reX = zeros(n,1); reY = zeros(n,1);
  leX = zeros(n,1); leY = zeros(n,1);
  moX = zeros(n,1); moY = zeros(n,1);
  ntX = zeros(n,1); ntY = zeros(n,1);
  d = zeros(n,1); a = zeros(n,1); b=zeros(n,1);
  min_d = zeros(n,1); max_d=zeros(n,1);

  reX = truth(:,2);reY = truth(:,3);
  leX = truth(:,4);leY = truth(:,5);
  moX = truth(:,6);moY = truth(:,7);
  ntX = truth(:,8);ntY = truth(:,9);

  axis ij; hold on;
  m=1;
  for k=1:n
     if (reX(k) ~= -1) 
         plot(reX(k), reY(k), 'or');
         plot(leX(k), leY(k), 'ob');
         plot(ntX(k), ntY(k), '.k');
         plot(moX(k), moY(k), '+m');

         d(m)=norm([leX(k) leY(k)]-[reX(k) reY(k)], 2);
         b(m)=norm([leX(k) leY(k)]-[moX(k) moY(k)], 2);
         a(m)=norm([moX(k) moY(k)]-[reX(k) reY(k)], 2);

         min_d(m) = min([d(m) a(m) b(m)]);
         max_d(m) = max([d(m) a(m) b(m)]);
         m=m+1;
     end
  end

  dr = max_d./min_d;

  % geometry
  figure; hold on;
  plot(a/d, b/d, '.b');
  plot(min_d, max_d, '.');
  ylabel('\fontsize{12} b/d');
  xlabel('\fontsize{12} a/d');
  title('\fontsize{12} eyes-mouth distance/scale likelihood ');
  

  % scale
  figure; 
  subplot(2,2,1); hold on;
  title('min:max d, scale=1');  plot(min_d, max_d, '.'); 
  subplot(2,2,2); hold on;title('min:max d, scale=0.8');plot(0.8*min_d, 0.8*max_d, '.');
  subplot(2,2,3); hold on;title('min:max d, scale=0.8^2');  plot(0.64*min_d, 0.64*max_d, '.');
  subplot(2,2,4); hold on;title('min:max d, scale=0.8^3');plot(0.64*0.8*min_d, 0.64*0.8*max_d, '.');
  
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% PCA on facials
% (1) creat face icons: 12x16
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clear;
%truth = load('faGndTruth.dat');
truth = load('fbGndTruth.dat');

n = length(truth(:,1));
W = 12;
H = 16;

m=1; 
offs=0;
for k=1:n
  face = showFeretFace(truth, k);
  [h w] = size(face);
  if h ~=0 & w ~=0
     fht(m) = h; fwid(m) = w; hwr(m) = h/w;  
     [icon vec]=scaleImage(face, W, H);
     %figure(1); subplot(4,5,k); colormap('gray'); imagesc(icon);
     %figure(2); subplot(4,5,k); imshow(face, 256);
     faceIcons(:,m)=vec';
     fprintf('\n >>k=%d,m=%d>> ', k, m);
     m=m+1;    
  end
end

%save 'fa-icons-w12h16-3d.dat' faceIcons -ascii;
save 'fb-icons-w12h16-3d.dat' faceIcons -ascii;

W=20;H=30;
truth = load('fbGndTruth.dat');
[faces]=getFaceIcons(truth, W, H);
truth = load('faGndTruth.dat');
[faFaces]=getFaceIcons(truth, W, H);
save 'faFacesW20H30.dat' faFaces -ascii;
[T4, lat4]=pcaAnalysis(faFaces, 6);
save 'pcaFaceW20H30.dat' T4 -ascii;

uFaces =[];
uFaces = faFaces(1:300,:);
lFaces = faFaces(301:600,:);
figure; [T5, lat5]=pcaAnalysis(uFaces, 8);
save 'pcaUpperFaceW20H30.dat' T5 -ascii;

nFaces1 = randomSubImages('1839.jpg', 30, W, H);
nFaces2 = randomSubImages('1873.jpg', 30, W, H);
nFaces =  double([nFaces1, nFaces2]);
[d n]=size(faFaces);

% plot upper face on dim 4 and 5. 
figure; hold on; d1=1; d2=2;
title('\fontsize{12} upper-face projection 1-2');
for k=1:n
  prj = T5*uFaces(:,k);
  plot(prj(d1), prj(d2), '+r');
end
t = 20
for k=1:1800
  prj = T5*nFaces(t:t+300-1,k);
  plot(prj(d1), prj(d2), '.k');
end


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% PCA on facials
% (2) covar analysis
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clear;
first_use = 'n';

faIcons = load('fa-icons-w12h16-3d.dat');
%fbIcons = load('fb-icons-w12h16-3d.dat');

% get non face samples
if first_use == 'y'
    jpgPath = 'd:/data/Feret/jpeg/nonface/';
    jpgs = '1839';h=16; w=12; m=30;
    fname = sprintf('%s%s.jpg', jpgPath, jpgs);
    img = imread(fname, 'jpg');
    nfaces = getNonFaceImage(img, m, w, h);
    nface = double(nfaces);

    jpgs = '1873';h=16; w=12; m=30;
    fname = sprintf('%s%s.jpg', jpgPath, jpgs);
    img = imread(fname, 'jpg');
    nfaces2 = getNonFaceImage(img, m, w, h);
    nface2 = double(nfaces);

    % total 1800 nonfaces
    nface(:,901:1800)=nface2;

    save 'nonFaceW12H16.dat' nface -ascii;
else
    nface = load('nonFaceW12H16.dat');
end

  upFace = faIcons(1:96,:);
  loFace = faIcons(97:192,:);
  nUpFace = nface(1:96,:);
  nLoFace = nface(97:192,:);

if first_use == 'y'
    [T1, lat1]=pcaAnalysis(faIcons, 12);
    save 'pcaFaceW12H16.dat' T1 -ascii;
    save 'latFaceW12H16.dat' lat1 -ascii;

    figure; [T2, lat2]=pcaAnalysis(upFace, 8);
    save 'pcaUpperFaceW12H16.dat' T2 -ascii;
    save 'latUpperFaceW12H16.dat' lat2 -ascii;

    figure; [T3, lat3]=pcaAnalysis(loFace, 8);
    save 'pcaLowerFaceW12H16.dat' T3 -ascii;
    save 'latLowerFaceW12H16.dat' lat3 -ascii;
else
    T1 = load('pcaFaceW12H16.dat');
    T2 = load('pcaUpperFaceW12H16.dat');
    T3 = load('pcaLowerFaceW12H16.dat');
end

[d n] = size(faIcons);

d1=1; d2=2;
figure; hold on; axis xy; 
title('\fontsize{12} full-face projection 1-2');
for k=1:n
  prj = T1*faIcons(:,k);  plot(prj(d1), prj(d2), '+r');
end
for k=1:n
  prj = T1*nface(:,k); plot(prj(d1), prj(d2), '.b');  
end

% non-linear mapping
 prjFace = T1*faIcons;
 prjNonFace = T1*nface;
 meanPrjFace = mean(prjFace')';
 
 for k=1:n
   prjFace(:,k) = prjFace(:,k) - meanPrjFace;
   prjNonFace(:,k) = prjNonFace(:,k) - meanPrjFace;
 end

 % non-linear mapping
 x = prjFace(1:4,:);
 y = quadraticMap(x);
 ynf = quadraticMap(prjNonFace(1:4,:));
 
 [Tnl, latnl]=pcaAnalysis(y, 6);

d1=1; d2=3;
figure; hold on; axis xy; 
title('\fontsize{12} full-face non-linear projection 1-2');
for k=1:n
  prj = Tnl*y(:,k);  plot(prj(d1), prj(d2), '+r');
end
for k=1:n
  prj = Tnl*ynf(:,k); plot(prj(d1), prj(d2), '.b');  
end
 

% plot upper face on dim 4 and 5. 
figure; hold on; d1=1; d2=2;
title('\fontsize{12} upper-face projection 1-2');
for k=1:n
  prj = T2*upFace(:,k);
  plot(prj(d1), prj(d2), '+r');
end
for k=1:n
  prj = T2*nLoFace(:,k);
  plot(prj(d1), prj(d2), '.k');
end

% plot lower face on dim 4 and 5. 
figure;
hold on;d1=1; d2=2;
for k=1:n
  prj = T3*loFace(:,k);
  plot(prj(5), prj(6), '+r');
end
for k=1:n 
  prj = T3*nLoFace(:,k);
  plot(prj(d1), prj(d2), '.k');
end
title('\fontsize{12} Lower Face Distribution on 1-2');


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% PCA on facials
% (3) non-face data
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

  jpgs =['1839', '1842', '1873', '1875'];
  jpgPath = 'd:/data/Feret/jpeg/nonface/';
  jpgs = '1875';
  n = 30; h=24; w=16;
  fname = sprintf('%s%s.jpg', jpgPath, jpgs);
  img = imread(fname, 'jpg');
  imshow(img, 256);


  nfaces2 = getNonFaceImage(img, 30, w, h);

  for j=1:8
      figure(j);
      for k=1:9
        subplot(3,3,k); loadATTFace(j,k);
      end
  end

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Kernel PCA 
% 12-11-2003.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
clear;
first_use = 'n';

% build face icons
if first_use == 'y'
  W = 12; H = 16; faces=[];
  truth = load('faGndTruth.dat');
  [faces]=getFaceIcons(truth, W, H);
  save 'faFacesW12H16.dat' faces -ascii;
end

% get non-face icons
if first_use == 'y'
  W=12; H=16;
  nFaces1 = randomSubImages('1839.jpg', 30, W, H);
  nFaces2 = randomSubImages('1873.jpg', 30, W, H);
  nFaces =  double([nFaces1, nFaces2]);
  save 'nonFacesW12H16.dat' nFaces -ascii;
end

% PCA
if first_use == 'y'
    [T1, lat1, mf]=pcaAnalysis(faces, 12);
    save 'pcaFaceW12H16.dat' T1 -ascii;
    save 'latFaceW12H16.dat' lat1 -ascii;

    figure; [T2, lat2, muf]=pcaAnalysis(upFaces, 8);
    save 'pcaUpperFaceW12H16.dat' T2 -ascii;
    save 'latUpperFaceW12H16.dat' lat2 -ascii;

    figure; [T3, lat3, mlf]=pcaAnalysis(loFaces, 8);
    save 'pcaLowerFaceW12H16.dat' T3 -ascii;
    save 'latLowerFaceW12H16.dat' lat3 -ascii;
end


% load data
clear;
W=12; H=16; 
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');

% load PCA
T1 = load('pcaFaceW12H16.dat');
T2 = load('pcaUpperFaceW12H16.dat');
T3 = load('pcaLowerFaceW12H16.dat');
mT1Face = T1*mFace; mT2Face = T2*mUpFace; mT3Face = T3*mLoFace;





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

figure; d1=1; d2=2;
% plot whole face on dim 1 and 2. 
for k=1:6
    subplot(3,2,k); hold on; 
    plot(nT1Faces(2*k-1,:), nT1Faces(2*k,:), '.b'); 
    plot(t1Faces(2*k-1,:), t1Faces(2*k,:), '+r');
    str=sprintf('%s%d-%d', '\fontsize{12}',2*k-1, 2*k); xlabel(str);
end
title('\fontsize{12} Face Distribution on PCA');

figure; 
d1=5; d2=9; hold on;
plot(nT1Faces(d1,:), nT1Faces(d2,:), '.b'); 
plot(t1Faces(d1,:), t1Faces(d2,:), '+r');
xlabel('\fontsize{12} Face Distribution on PCA 5-9');

% plot upper face on dim 1 and 2. 
figure; 
for k=1:4