Prediction of eigenvalues and regularization of eigenfeatures for human face verification

We present a prediction and regularization strategy for alleviating the conventional problems of LDA and its variants. A procedure is proposed for predicting eigenvalues using few reliable eigenvalues from the range space. Entire eigenspectrum is divided using two control points, however, the effective low-dimensional discriminative vectors are extracted from the whole eigenspace. The estimated eigenvalues are used for regularization of eigenfeatures in the eigenspace. These prediction and regularization enable to perform discriminant evaluation in the full eigenspace. The proposed method is evaluated and compared with eight popular subspace based methods for face verification task. Experimental results on popular face databases show that our method consistently outperforms others.