Face image super-resolution with pose via nuclear norm regularized structural orthogonal Procrustes regression

In real applications, the observed low-resolution face images usually have pose variations. Conventional learning-based methods ignore these variations; thus, the hallucinated high-resolution faces are not reasonable for the following recognition task. For recognition purpose, we prefer to obtain near-frontal faces. To this end, we propose a nuclear norm regularized structural orthogonal Procrustes regression (N2SOPR) approach in this work to acquire pose-robust feature representations for face hallucination with pose. The orthogonal Procrustes regression is used to seek an appropriate transformation between two data matrixes. Additionally, the nuclear norm regularization is imposed on the representation residual to preserve image structural property. We also impose a low-rank restraint on the combination weight to automatically cluster each input into the same subspace with the training samples. Both hallucination and recognition experiments conducted on common face databases have verified that our N2SOPR can obtain reasonable performance than some related methods.