Face verification is different from face identification task. Some traditional subspace methods that work well in
face identification may suffer from severe over-fitting problem when applied for the verification task. Conventional discriminative
methods such as linear discriminant analysis (LDA) and its variants are highly sensitive to the training data,
which hinders them from achieving high verification accuracy. This work proposes an eigenspectrum model that alleviates
the over-fitting problems by replacing the unreliable small and zero eigenvalues with the model values. It also enables
the discriminant evaluation in the whole space to extract the low dimensional features effectively. The proposed approach
is evaluated and compared with 8 popular subspace based methods for a face verification task. Experimental results on
three face databases show that the proposed method consistently outperforms others.