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Facial Image Reconstruction and its Influence to Face Recognition

2023-12-15International Conference of the Biometrics Special Interest Group (BIOSIG) 2023Code Available0· sign in to hype

Filip Pleško, Tomáš Goldmann, Kamil Malinka

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Abstract

This 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.

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