Is this the right way of projecting the training set into the eigespace?

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I have computed PCA using the following :
function [signals,V] = pca2(data)
[M,N] = size(data);
data = reshape(data, M*N,1);
% subtract off the mean for each dimension
mn = mean(data,2);
data = bsxfun(@minus, data, mean(data,1));
% construct the matrix Y
Y = data'*data / (M*N-1);
[V D] = eigs(Y, 10); % reduce to 10 dimension
% project the original data
signals = data * V;
My question is:
Is "signals" is the projection of the training set into the eigenspace?
I saw in "Amir Hossein" code that "centered image vectors" that is "data" in the above code needs to be projected into the "facespace" by multiplying in the eigenspace basis's. I don't really understand why is the projection done using centered image vectors? Isn't "signals" enough for classification??

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