05 Fakultät Informatik, Elektrotechnik und Informationstechnik

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Publication Type: search.filters.UBSTyp.Abschlussarbeit (Bachelor)Institute: search.filters.UBSInstitut.Institut für Künstliche IntelligenzAuthor: search.filters.author.Nasimzada, Jonas

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    Domain adaptation methods for emotion and pain recognition via video games
    (2024) Nasimzada, Jonas
    Seeing the patient’s emotional and physical condition is crucial when designing patient-computer interaction systems. However, gathering large datasets in sensitive situations like filming a person in pain can be challenging and ethically questionable. The primary aim of this study is to assess the possibility of using synthetic data as an alternative data source to create models capable of effectively recognizing patient pain. Initially, a synthetic dataset was generated as the foundation for model development. To maintain the relevance of the synthetically generated dataset’s diversity, a 3D model of real people was created by extracting facial landmarks from a source dataset and generating 3D meshes using EMOCA (Emotion Driven Monocular Face Capture and Animation) [1] [2]. Meanwhile, facial textures were sourced from publicly available datasets like CelebHQ [3] and FFHQ-UV [4]. An efficient pipeline was created for human mesh and texture generation, resulting in a dataset of 8,600 synthetic human heads generated in approximately 2 hours per perspective and texture. The datasets encompass varying facial textures and perspectives and total over 300 GB. This approach enhances gender and ethnic diversity while introducing perspectives from previously unseen viewpoints. Combining the 3D models with the extracted textures created new characters with varying facial textures but identical facial expressions. The study aims to bridge the gap between synthetic data and real-world medical contexts using domain adaptation methods, like Domain Mapping. This approach eliminates the need for human participants and addresses ethical issues associated with traditional data collection methods. Different combinations of datasets, encompassing various textures and perspectives, were utilized to train models and assess the feasibility of synthetic data for domain adaptation (Domain Mapping) with real human data as input video. However, incorporating synthetic and real data leads to improved pain recognition capabilities. This combined approach can leverage the strengths of both real and synthetic datasets, resulting in a more robust and effective model for pain recognition.