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

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??