Filip Pleško, Tomas GoldmannDamer, NaserGomez-Barrero, MartaRaja, KiranRathgeb, ChristianSequeira, Ana F.Todisco, MassimilianoUhl, Andreas2023-12-122023-12-122023978-3-88579-733-31617-5468https://dl.gi.de/handle/20.500.12116/43278This paper focuses on reconstructing damaged facial images using GAN neural networks. In addition, the effect of generating the missing part of the face on face recognition is investigated. The main objective of this work is to observe whether it is possible to increase the accuracy of face recognition by generating missing parts while maintaining a low false accept rate (FAR). A new model for generating the missing parts of a face has been proposed. For face-based recognition, state-of-the-art solutions from the DeepFace library and the QMagFace solution have been used.enFace and gesture recognitionBiometric performance measurement; Biometric sample qualityText/Conference Paper