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In recent years, various Blind Face Restoration (BFR) techniques weredeveloped. These techniques transform low quality faces suffering from multipledegradations to more realistic and natural face images with high perceptualquality. However, it is crucial for the task of face verification to not onlyenhance the perceptual quality of the low quality images but also to improvethe biometric-utility face quality metrics. Furthermore, preserving thevaluable identity information is of great importance. In this paper, weinvestigate the impact of applying three state-of-the-art blind facerestoration techniques namely, GFP-GAN, GPEN and SGPN on the performance offace verification system under very challenging environment characterized byvery low quality images. Extensive experimental results on the recentlyproposed cross-quality LFW database using three state-of-the-art deep facerecognition models demonstrate the effectiveness of GFP-GAN in boostingsignificantly the face verification accuracy.

Boosting Cross-Quality Face Verification using Blind Face Restoration