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PLEŠKO, F.; GOLDMANN, T.; MALINKA, K.
Original Title
Facial Image Reconstruction and its Influence to Face Recognition
English Title
Type
Paper in proceedings outside WoS and Scopus
Original 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.
English abstract
Keywords
face recognition, neural network, face reconstruction, generativní adversariální síť, SFace, ArcFace, QMagFace
Key words in English
Authors
RIV year
2024
Released
20.08.2023
Publisher
Society for Informatics
Location
Darmstadt
ISBN
979-8-3503-3655-9
Book
2023 International Conference of the Biometrics Special Interest Group (BIOSIG)
Pages from
1
Pages to
4
Pages count
5
URL
https://ieeexplore.ieee.org/document/10346000
BibTex
@inproceedings{BUT186943, author="Filip {Pleško} and Tomáš {Goldmann} and Kamil {Malinka}", title="Facial Image Reconstruction and its Influence to Face Recognition", booktitle="2023 International Conference of the Biometrics Special Interest Group (BIOSIG)", year="2023", pages="1--4", publisher="Society for Informatics", address="Darmstadt", doi="10.1109/BIOSIG58226.2023", isbn="979-8-3503-3655-9", url="https://ieeexplore.ieee.org/document/10346000" }