Learning Simultaneous Facial Canonical Correlation Representation for Face Hallucination

Abstract

The low resolution (LR) problem is rather challenging in face analysis. Most existing face hallucination methods assume that LR face images have only one resolution, but multiple resolutions may be available from different sources. To solve this issue, we propose a novel simultaneous facial canonical correlation representation learning method for face hallucination, which seeks latent correlation subspaces for multi-resolution views. Our method jointly solves multiple linear transformations by optimizing a correlation summation criterion of all pairs of resolutions. The neighborhood reconstruction is used to infer the HR facial canonical correlation representation of LR face inputs. Extensive experimental results show the superiority of our proposed method in terms of quantitative and qualitative evaluations.