Hallucinating multiple occluded face images of different resolutions

Learning-based super-resolution has recently been proposed for enhancing human face images, known as “face hallucination”. In this paper, we propose a novel algorithm to super-resolve face images given multiple partially occluded inputs at different lower resolutions. By integrating hierarchical patch-wise alignment and inter-frame constraints into a Bayesian framework, we can probabilistically align multiple input images at different resolutions and recursively infer the high-resolution face image. We address the problem of fusing partial imagery information through multiple frames and discuss the new algorithm’s effectiveness when encountering occluded low-resolution face images. We show promising results compared to those of existing face hallucination methods from both simulated facial database and live video sequences.