[Numpy-discussion] PCA on set of face images
Peter Skomoroch
peter.skomoroch@gmail....
Fri Feb 29 13:56:20 CST 2008
Here is the page I referenced for the octave version ... it includes
examples very similar to what you want. I will be posting a very similar
example in Python later this month.
I don't have any Python code on hand for the Petland paper, but I think
matlab example should be easy to translate to scipy/matplotlib using the
montage function:
load faces.mat
%Form covariance matrix
C=cov(faces');
%build eigenvectors and eigenvalues
[E,D] = eig(C);
%sort based on eigenvalue
[B,index] = sortrows(D');
E2=E';
E2(index,:)';
eigensorted=E2(index,:)';
%show eigenfaces
clear Z;
for i=1:length(eigensorted)
Z(:,:,1,i)=reshape(eigensorted(:,i)-1.5*min(min(min(eigensorted))),
19,19);
end
montage(Z)
%show top 16 eigenfaces
clear Z;
for i=1:16
Z(:,:,1,i)=reshape(eigensorted(:,i)-min(min(min(eigensorted))), 19,19);
end
montage(Z)
On Fri, Feb 29, 2008 at 2:50 PM, Peter Skomoroch <peter.skomoroch@gmail.com>
wrote:
> RoyG,
>
> The timing of your question couldn't be better, I just did an blog post on
> this (I also plugged scipy and the EPD):
>
>
> http://www.datawrangling.com/python-montage-code-for-displaying-arrays.html
>
> The code basically replicates the matlab montage() function and approach
> to handling grayscale images using matplotlib.
>
> -Pete
>
>
> On Fri, Feb 29, 2008 at 2:15 PM, devnew@gmail.com <devnew@gmail.com>
> wrote:
>
> > hi guys
> > I have a set of face images with which i want to do face recognition
> > using Petland's PCA method.I gathered these steps from their docs
> >
> > 1.represent matrix of face images data
> > 2.find the adjusted matrix by substracting the mean face
> > 3.calculate covariance matrix (cov=A* A_transpose) where A is from
> > step2
> > 4.find eigenvectors and select those with highest eigenvalues
> > 5.calculate facespace=eigenvectors*A
> >
> >
> > when it comes to implementation i have doubts as to how i should
> > represent the matrix of face images?
> > using PIL image.getdata() i can make an array of each greyscale image.
> > Should the matrix be like each row contains an array representing an
> > image? That will make a matrix with rows=numimages and
> > columns=numpixels
> >
> > cavariancematrix =A *A_transpose will create a square matrix of
> > shape(numimages,numimages)
> > Using numpy.linalg.eigh(covariancematrix) will give eigenvectors of
> > same shape as the covariance matrix.
> >
> > I would like to know if this is the correct way to do this..I have no
> > big expertise in linear algebra so i would be grateful if someone can
> > confirm the right way of doing this
> >
> > RoyG
> > _______________________________________________
> > Numpy-discussion mailing list
> > Numpy-discussion@scipy.org
> > http://projects.scipy.org/mailman/listinfo/numpy-discussion
> >
>
>
>
> --
> Peter N. Skomoroch
> peter.skomoroch@gmail.com
> http://www.datawrangling.com
--
Peter N. Skomoroch
peter.skomoroch@gmail.com
http://www.datawrangling.com
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