  In recent years, various Blind Face Restoration (BFR) techniques were
developed. These techniques transform low quality faces suffering from multiple
degradations to more realistic and natural face images with high perceptual
quality. However, it is crucial for the task of face verification to not only
enhance the perceptual quality of the low quality images but also to improve
the biometric-utility face quality metrics. Furthermore, preserving the
valuable identity information is of great importance. In this paper, we
investigate the impact of applying three state-of-the-art blind face
restoration techniques namely, GFP-GAN, GPEN and SGPN on the performance of
face verification system under very challenging environment characterized by
very low quality images. Extensive experimental results on the recently
proposed cross-quality LFW database using three state-of-the-art deep face
recognition models demonstrate the effectiveness of GFP-GAN in boosting
significantly the face verification accuracy.
