Verification of human faces using predicted eigenvalues

Bappaditya Mandal, Xudong Jiang, Alex ChiChung Kot

Abstract: To alleviate the conventional problems of LDA and its variants, we propose a procedure of predicting eigenvalues using few reliable eigenvalues from the range space. Partitioning of entire eigenspace is performed using two control points, however, the effective low dimensional discriminative vectors are extracted from the whole eigenspace. This prediction strategy enables to perform discriminant evaluation in the full eigenspace. The proposed method is evaluated and compared with 8 popular subspace based methods for face verification task. Experimental results on popular face databases show that our method consistently outperforms others.