Hallucinating very low-resolution face image by 16x magnification with age-based attribute

Abstract

Face Hallucination is a kind of super-resolution that restores very low-resolution face images to high- resolution face images. This field plays an important role in face recognition and restoration in situations where face shape is incomplete. However accurate restoration is difficult because the learning takes place while the unique facial features caused by age, such as wrinkles in the skin, are ignored. To solve the above problem, we obtained the age attribute value of the LR image and constructed a pipeline network that can restore the face image including the age attribute using a combination of existing deep learning methods. The predicted age attribute is divided into two groups, young and old. The aging network, last step in the pipeline, was applied only when the image had an old attribute. Lastly, restored images are compared qualitatively and quantitatively with images created by the existing methods. Our method can maintain and restore personality that can come with age, such as wrinkles, and SSIM values are higher overall than other methods.