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Division of Motor Research, Pathophysiology and Biomechanics, Clinic for Trauma, Hand and Reconstructive Surgery, University Hospital Jena, Jena, Germany

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1 Papers

Let’s Get the FACS Straight: Reconstructing Obstructed Facial Features

Topics: Image Enhancement and Restoration; Face and Expression Recognition; Medical Image Applications; Transfer Learning

Authors: Tim Büchner ¹ , Sven Sickert ¹ , Gerd Volk ² , Christoph Anders ³ , Orlando Guntinas-Lichius ³ and Joachim Denzler ²

Affiliations: ¹ Computer Vision Group, Friedrich Schiller University Jena, Jena, Germany , ² Department of Otolaryngology, University Hospital Jena, Jena, Germany , ³ Division of Motor Research, Pathophysiology and Biomechanics, Clinic for Trauma, Hand and Reconstructive Surgery, University Hospital Jena, Jena, Germany

Digital Object Identifier: 10.5220/0011619900003417

Proceedings of VISAPP 2023/VISIGRAPP, ISBN: 978-989-758-634-7

Quick Abstract

The human face is one of the most crucial parts in interhuman communication. Even when parts of the face are hidden or obstructed the underlying facial movements can be understood. Machine learning approaches often fail in that regard due to the complexity of the facial structures. To alleviate this problem a common approach is to fine-tune a model for such a specific application. However, this is computational intensive and might have to be repeated for each desired analysis task. In this paper, we propose to reconstruct obstructed facial parts to avoid the task of repeated fine-tuning. As a result, existing facial analysis methods can be used without further changes with respect to the data. In our approach, the restoration of facial features is interpreted as a style transfer task between different recording setups. By using the CycleGAN architecture the requirement of matched pairs, which is often hard to fullfill, can be eliminated. To proof the viability of our approach, we compare our reconstructions with real unobstructed recordings. We created a novel data set in which 36 test subjects were recorded both with and without 62 surface electromyography sensors attached to their faces. In our evaluation, we feature typical facial analysis tasks, like the computation of Facial Action Units and the detection of emotions. To further assess the quality of the restoration, we also compare perceptional distances. We can show, that scores similar to the videos without obstructing sensors can be achieved.