Rigid Regression for Facial Image Interpolation with Local Structure Prior

Abstract

As an important biometric technique, face recognition has received substantial attention from both research communities and the market in the past three decades. However, the poor resolution of captured face images can seriously degrade the performance of conventional FR systems. To this end, many image super-resolution (SR) methods have been proposed recently. In this paper, we regard the face image SR as a kind of image interpolation problem for domain specific image. A missing intensity interpolation method based on Rigid Regression (RR) using a Local Structure Prior (LSP) is presented, which is called RRLSP for short. In order to interpolate missing intensities within a target high-resolution (HR) image, we assume that face image patches at the same position share the similar local structure, which is called LSP, and use the RR to learn the relationship between the low-resolution (LR) pixels and the missing HR pixels of one position patch with the LSP. Performance comparison with existing algorithms shows the effectiveness of the proposed method for face image SR in general.