Deep Facial Reconstruction
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Journal Title
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Sähkötekniikan korkeakoulu |
Master's thesis
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Authors
Date
2022-01-24
Department
Major/Subject
Autonomous Systems
Mcode
ELEC3055
Degree programme
Master's Programme in ICT Innovation
Language
en
Pages
56
Series
Abstract
Everyone wants their images to look as good as possible when they post on social media. It is not always possible to retake a picture or to manually edit small mistakes afterwards. This paper investigates the viability of using deep learning to perform this task for a type of edit. More specifically, we want to “photoshop” a face onto a subject wearing a facemask. To explore this possibility, we examined techniques and methodologies from facial attribute removal and image completion. Then, we structurally test and implement the most promising ideas. The result is a CycleGAN model capable of reconstructing a realistic looking face. The model matches performance with state-of-the-art image completion and outperforms models in facial attribute removal.Description
Supervisor
Kannala, JuhoThesis advisor
Hadid, AbdenourKeywords
computer vision, image completion, generative adversarial network, GAN, CycleGAN, DMFN