Image Enhancement for Face Recognition in Adverse Environments

Abstract

Face recognition in adverse environments, such as at long distances or in low light conditions, remains a challenging task for current state-of-the-art face matching algorithms. The facial images taken in these conditions are often low resolution and low quality due to the effects of atmospheric turbulence and/or insufficient amount of light reaching the camera. In this work, we use an atmospheric turbulence mitigation algorithm (MPE) to enhance low resolution RGB videos of faces captured either at long distances or in low light conditions. Due to its interactive nature, MPE is tuned to work well in each specific environment. We also propose three image enhancement techniques that further improve the images produced by MPE: two for low light imagery (MPEf and fMPE) and one for long distance imagery (MPEh). Experimental results show that all three methods significantly improve the image quality and face recognition performance, allowing effective face recognition in almost complete darkness (at close range) or at distances up to 200m (in daylight).