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What

In this paper, we study face hallucination, or synthesizing a high-resolution face image from an input low-resolution image, with the help of a large collection of other high-resolution face images. Our theoretical contribution is a two-step statistical modeling approach that integrates both a global parametric model and a local nonparametric model. Our practical contribution is a robust warping algorithm to align the low-resolution face images to obtain good hallucination results. The effectiveness of our approach is demonstrated by extensive experiments with high-quality hallucinated face images with no manual alignment.

Why

Many computer vision tasks require inferring the missing high-resolution image from the low-resolution input. Of particular interest is to infer high-resolution (abbr. high-res) face images from low-resolution (abbr. low-res) ones. This problem was introduced by Baker and Kanade [1] as face hallucination. This technique has broad applications in image enhancement, image compression and face recognition. It can be especially useful in a surveillance system where the resolution of face image are normally low in videos, but the details of facial features which can be found in the potential high-res image are crucial for identification and further analysis.