Super-Resolution of Facial Images in Video with Expression Changes

Super-resolution (SR) of facial images from video suffers from facial expression changes. Most of the existing SR algorithms for facial images make an unrealistic assumption that the ¿perfect¿ registration has been done prior to the SR process. However, the registration is a challenging task for SR with expression changes. This paper proposes a new method for enhancing the resolution of low-resolution (LR) facial image by handling the facial image in a non-rigid manner. It consists of global tracking, local alignment for precise registration and SR algorithms. A B-spline based resolution aware incremental free form deformation (RAIFFD) model is used to recover a dense local non-rigid flow field. In this scheme, low-resolution image model is explicitly embedded in the optimization function formulation to simulate the formation of low resolution image. The results achieved by the proposed approach are significantly better as compared to the SR approaches applied on the whole face image without considering local deformations. The results are also compared with two state-of-the-art SR algorithms to show the effectiveness of the approach in super-resolving facial images with local expression changes.