Face recognition in video with closed-loop super-resolution
Video-based face recognition has received significant attention in the past few years. However, the facial images in a video sequence acquired from a distance are usually small in size and their visual quality is low. Enhancing low-resolution (LR) facial images from a video sequence is of importance for performing face recognition. Registration is a critical step in super-resolution (SR) of facial images from a video which requires precise pose alignment and illumination normalization. Unlike traditional approaches that perform tracking for each frame before using a SR method, in this paper, we present an incremental super-resolution technique in which SR and tracking are linked together in a closed-loop system. An incoming video frame is first registered in pose and normalized for illumination, and then combined with the existing super-resolved texture. This super-resolved texture, in turn, is used to improve the estimate of illumination and motion parameters for the next frame. This process passes on the benefits of the SR result to the tracking module and allows the entire system to reach its potential. We show results on a low-resolution facial video. We demonstrate a significant improvement in face recognition rates with the super-resolved images over the images without super-resolution.