SSR2: Sparse Signal Recovery for Single-Image Super-Resolution on Faces With Extreme Low Resolutions

• NCJ Number: 254136
• Author(s): Ramzi Abiantun; Felix Juefei-Xu; Utsav Prabhu; Marios Savvides
• Date Published: June 2019
• Length: 17 pages
• Grant Number(s): 2013-IJ-CX-K005
• Agencies: NIJ-Sponsored
• Publication Type: Research (Applied/Empirical); Report (Study/Research); Report (Grant Sponsored); Program/Project Description
• Award: A Simultaneous Low Resolution and Off-Pose Angle Face Matching Algorithm as an Investigative Lead Generative Tool for Law Enforcement

Annotation

Since automatic face recognition in the wild still suffers from low-quality, low resolution, noisy, and occluded input images that can severely impact identification accuracy, this article presents a novel technique that improves the quality of such low-resolution face images beyond the current state of the art.

Abstract

The project modeled the correlation between high and low resolution faces in a multi-resolution pyramid and recovered the original structure of an un-seen extreme low-resolution face image. By exploiting domain knowledge of the structure of the input signal and using sparse recovery optimization algorithms, the project recovered a consistent sparse representation of the extreme low-resolution signal. The proposed super-resolution method is robust to noise and face alignment, and it can handle extreme low-resolution faces up to 16x magnification factor with just seven pixels between the eyes. Moreover, the formulation of the proposed algorithm enables simultaneous occlusion removal capability, a desirable property that other super-resolution algorithms do not possess. Most importantly, the project shows that the demonstrated method generalizes on real-world low-quality surveillance images. (publisher abstract modified)