Identity-Preserving Pose-Robust Face Hallucination Through Face Subspace Prior
This addresses the issue of identity loss in face super-resolution for applications like surveillance or biometrics, offering an incremental improvement over existing methods by focusing on person-specific features.
The paper tackles the problem of face hallucination (super-resolution) by introducing a novel approach that uses a face subspace prior to preserve identity-specific features and an efficient 3D dictionary alignment to handle uncontrolled conditions, resulting in detailed outputs that outperform state-of-the-art methods by significant margins in quantitative and qualitative evaluations.
Over the past few decades, numerous attempts have been made to address the problem of recovering a high-resolution (HR) facial image from its corresponding low-resolution (LR) counterpart, a task commonly referred to as face hallucination. Despite the impressive performance achieved by position-patch and deep learning-based methods, most of these techniques are still unable to recover identity-specific features of faces. The former group of algorithms often produces blurry and oversmoothed outputs particularly in the presence of higher levels of degradation, whereas the latter generates faces which sometimes by no means resemble the individuals in the input images. In this paper, a novel face super-resolution approach will be introduced, in which the hallucinated face is forced to lie in a subspace spanned by the available training faces. Therefore, in contrast to the majority of existing face hallucination techniques and thanks to this face subspace prior, the reconstruction is performed in favor of recovering person-specific facial features, rather than merely increasing image quantitative scores. Furthermore, inspired by recent advances in the area of 3D face reconstruction, an efficient 3D dictionary alignment scheme is also presented, through which the algorithm becomes capable of dealing with low-resolution faces taken in uncontrolled conditions. In extensive experiments carried out on several well-known face datasets, the proposed algorithm shows remarkable performance by generating detailed and close to ground truth results which outperform the state-of-the-art face hallucination algorithms by significant margins both in quantitative and qualitative evaluations.