This might be a silly quesntion but recently I've been trying to program the eigenface algorithm using PCA, so I arranged the face vectors vertically in a matrix X such as: X = [x1,x2,x3,...,xn]; In this case, what would be the wright way of computing the covariance matrix? cov(X) or cov(X')?
I believe that eigen faces need a non-linear version of PCA. See two examples here
for some of the seminal papers that implement such non-linear PCA approaches on faces.