High-Resolution Image Enumeration for Low-Resolution Face Recognition

Can Chen, Scott McCloskey

Abstract: Image enhancement is a long-studied problem that is understood to be fundamentally ill-posed, meaning that there are many high-quality images that are consistent with any given low-quality observation. When image enhancement is applied to biometric samples, such as sharpening or super-resolving a face image, this ill-posedness is often ignored and a single high-quality sample is estimated without ensuring that it's consistent with the observation. In this work, we describe a method to enumerate multiple high-quality samples from a single input, all of which are consistent with the low-quality input, and use this to estimate confidence in a face-based search result. This method quantifies the sample- and gallery-conditioned uncertainty by enumerating multiple high-quality images from a single low-quality sample, ensuring that each is consistent with the input. We demonstrate this by first showing that, even with modern deep-face features, low-resolution face recognition is still ill-posed even when applied to frontal face images. We enumerate multiple high-resolution (HR) images that are consistent with a low-resolution (LR) face sample, represent these with modern deep features, and demonstrate a subject-specific ill-posedness in recognition.