Enhancing Face Recognition in Low-Quality Images Based on Restoration and 3D Multiview Generation

Face recognition in low-quality images presents a significant challenge, particularly when images or videos are captured under adverse conditions such as atmosphere turbulence due to long distance capture causing noise, blurring, and distortion in low resolution videos. This paper proposes a novel and comprehensive approach to address the challenges in atmosphere turbulence, leveraging a multi-stage process that includes weak-strong image restoration using Generative Adversarial Network (GAN) based model and Stable Diffusion (SD) based model, 3D face view generation with Neural Radiance Fields (NeRF), and adaptive face recognition on fine-augmented datasets. Our methodology shows substantial improvements in face recognition accuracy on average from 55.6% to 74.7% on the level-3 turbulence images of LFW, CFP, CALFW, as well as improvements on datasets of TinyFace, the BRIAR BRC1, and the BRIAR BGC, demonstrating the performance enhancement in extremely challenging conditions.