I=imread('D:\Capture.PNG');
faceDetector = vision.CascadeObjectDetector();
bbox = step(faceDetector,I);
B=insertObjectAnnotation(I, 'rectangle', bbox, 'face');
imshow(I)
title('detected faces');
n=size(bbox,1);
str_n=num2str(n);
str=strcat('number of detected faces are', str_n);
disp(str);
[v d] = eig(double(I2(1:132,1:132)));
function [eigvector, eigvalue, elapse] = KDA(options,gnd,data)
if ~exist('data','var')
global data;
end
if (~exist('options','var'))
options = [];
end
if ~isfield(options,'Regu') | ~options.Regu
bPCA = 1;
else
bPCA = 0;
if ~isfield(options,'ReguAlpha')
options.ReguAlpha = 0.01;
end
end
if isfield(options,'Kernel') & options.Kernel
K = data;
clear data;
K = max(K,K');
elapse.timeK = 0;
else
[K, elapse.timeK] = constructKernel(data,[],options);
end
tmp_T = cputime;
nSmp = size(K,1);
if length(gnd) ~= nSmp
error('gnd and data mismatch!');
end
classLabel = unique(gnd);
nClass = length(classLabel);
Dim = nClass - 1;
K_orig = K;
sumK = sum(K,2);
H = repmat(sumK./nSmp,1,nSmp);
K = K - H - H' + sum(sumK)/(nSmp^2);
K = max(K,K');
clear H;
if bPCA
[U,D] = eig(K);
D = diag(D);
maxEigValue = max(abs(D));
eigIdx = find(abs(D)/maxEigValue < 1e-6);
if length(eigIdx) < 1
[dump,eigIdx] = min(D);
end
D (eigIdx) = [];
U (:,eigIdx) = [];
elapse.timePCA = cputime - tmp_T;
tmp_T = cputime;
Hb = zeros(nClass,size(U,2));
for i = 1:nClass,
index = find(gnd==classLabel(i));
classMean = mean(U(index,:),1);
Hb (i,:) = sqrt(length(index))*classMean;
end
[dumpVec,eigvalue,eigvector] = svd(Hb,'econ');
eigvalue = diag(eigvalue);
if length(eigvalue) > Dim
eigvalue = eigvalue(1:Dim);
eigvector = eigvector(:,1:Dim);
end
eigvector = (U.*repmat((D.^-1)',nSmp,1))*eigvector;
else
Hb = zeros(nClass,nSmp);
for i = 1:nClass,
index = find(gnd==classLabel(i));
classMean = mean(K(index,:),1);
Hb (i,:) = sqrt(length(index))*classMean;
end
B = Hb'*Hb;
T = K*K;
elapse.timePCA = cputime - tmp_T;
tmp_T = cputime;
for i=1:size(T,1)
T(i,i) = T(i,i) + options.ReguAlpha;
end
B = double(B);
T = double(T);
B = max(B,B');
T = max(T,T');
option = struct('disp',0);
[eigvector, eigvalue] = eigs(B,T,Dim,'la',option);
eigvalue = diag(eigvalue);
end
tmpNorm = sqrt(sum((eigvector'*K_orig).*eigvector',2));
eigvector = eigvector./repmat(tmpNorm',size(eigvector,1),1);
elapse.timeMethod = cputime - tmp_T;
elapse.timeAll = elapse.timeK + elapse.timePCA + elapse.timeMethod;