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Face Image Frontalization Application
 Tichý, Filip ; Malinka, Kamil (referee) ; Goldmann, Tomáš (advisor)
This work focuses on implementing an application for face frontalization using the CFR-GAN project and rotating the 3D face model followed by rendering. The aim of this work is to evaluate the impact of the application on face recognition accuracy based on the Fidentis dataset. The results are presented in the form of box plots, which depict the Euclidean distances between the generated frontalized images and the real images. It was found that when frontalizing using the rotation of a 3D model from high angles of rotation, the success of facial recognition process increases. Conversely, when frontalizing using the Complete Face Recovery GAN projekt, the recognition success signiĄcantly decreases. The VGG Face algorithm was used for comparing the images. The entire application is implemented in Python using commonly available libraries.
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