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dc.contributor.authorÇakır, Duygu
dc.contributor.authorArıca, Nafiz
dc.date.accessioned2025-04-10T07:04:57Z
dc.date.available2025-04-10T07:04:57Z
dc.date.issued2023en_US
dc.identifier.citationCakir, D., & Arica, N. (2023, December). Style Transfer to Enhance Data Augmentation for Facial Action Unit Detection. In International Conference on Robotics and Networks (pp. 101-111). Cham: Springer Nature Switzerland.en_US
dc.identifier.isbn978-303164494-8
dc.identifier.urihttps://hdl.handle.net/20.500.12960/1757
dc.description.abstractIn recent decades, advances in fields such as robotics, computer graphics, and computer vision have led to an increase in interest in the study of image recognition among scientists. One of the difficulties in image recognition is obtaining a sufficiently large and well-balanced dataset to improve the effectiveness, robustness, and reliability of machine learning models. This paper explores a research approach that addresses this issue by style transfer and observes how different styles affect the recognition rates on facial recognition tasks. This study claims that synthetic data augmentation can improve the model’s performance. The experiments were conducted on three different datasets by transferring two different image styles over the datasets, and it has been observed that the synthetic data generation on facial datasets proves to be a tool that significantly improves the accuracy of machine learning models in image recognition on micro-mimic movements (facial action units) of the face when the original data is not distorted much and has a lower impact when a more complex style is applied.en_US
dc.language.isoengen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofEAI/Springer Innovations in Communication and Computingen_US
dc.relation.isversionof10.1007/978-3-031-64495-5_8en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectData augmentationen_US
dc.subjectFacial action unit recognitionen_US
dc.subjectStyle transferen_US
dc.titleStyle Transfer to Enhance Data Augmentation for Facial Action Unit Detectionen_US
dc.typeconferenceObjecten_US
dc.authorid0000-0002-3810-5866en_US
dc.departmentMühendislik Fakültesi, Bilişim Sistemleri Mühendisliğien_US
dc.contributor.institutionauthorArıca, Nafiz
dc.identifier.startpage101en_US
dc.identifier.endpage111en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US


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