Learning Fractional Orthogonal Latent Consistent Features for Face Hallucination and Recognition

Face hallucination (FH) is a powerful technique to reconstruct high-resolution (HR) faces from low-resolution (LR) faces. Most of conventional FH techniques ignore the influence of small training data, which may lead to the bias of variance and covariance. In this paper, we propose a novel FH method via fractional orthogonal latent consistent features that we call fractional orthogonal partial least squares based FH (FOPLSFH). In the proposed FOPLS-FH, intra- and cross-resolution covariance matrices are re-estimated through fractional-order eigenvalues and singular values modeling. Experimental results on real-world face datasets demonstrate the effectiveness of the proposed FOPLS-FH method.