this is a matlab code for ICA face recognition... after i allign faces and mark features i run a runica.m file but in spherex.m the value of x,mx,wz and c become zero and all the matrix values become inf and NaN... what is the problem?

% Script to run ICA on a matrix of images. Original by Tony Bell. % Modified by Marian Stewart Bartlett.

%Assumes image gravalues are in rows of x. Note x gets overwritten. %Will find N independent components, where N is the number of images.

%There must be at least 5 times as many examples (cols of x) as the %dimension of the data (rows of x).

N=size(x,1); P=size(x,2); M=N; %M is dimension of the ICA output spherex; % remove first and second order stats from x xx=inv(wz)*x; % xx thus holds orig. data, w. mean extracted.

%******** setup various variables w=eye(N); count=0; perm=randperm(P); sweep=0; Id=eye(M); oldw=w; olddelta=ones(1,N*M); angle=1000; change=1000;

%******** Train. outputs a report every F presentations. % Watch "change" get small as it converges. Try annealing learning % rate, L, downwards to 0.0001 towards end. % For large numbers of rows in x (e.g. 200), you need to use a low % learning rate (I used 0.0005). Reduce if the output blows % up and becomes NAN. If you have fewer rows, use 0.001 or larger.

B=50; L=0.0005; F=5000; for I=1:1000, sep96; end; B=50; L=0.0003; F=5000; for I=1:200, sep96; end; B=50; L=0.0002; F=5000; for I=1:200, sep96; end; B=50; L=0.0001; F=5000; for I=1:200, sep96; end;

%******** uu=w*wz*xx; % make separated output signals. cov(uu') % check output covariance. Should approximate 3.5*I.

%%%%%%%%% % To obtain the ICA representations described in

% SPHEREX - spheres the training vector x. % Requires x, P, to be predefined, and defines mx, c, wz.

fprintf('\nsubtracting mean\n'); mx=mean(x'); x=x-(ones(P,1)*mx)'; fprintf('calculating whitening filter\n'); c=cov(x'); wz=2*inv(sqrtm(c)); fprintf('whitening\n'); x=wz*x;