Mapping Action Units to Valence and Arousal Space Using Machine Learning

Авторы: Gadzhiev Ismail M., Makarov Alexander S., Tikhomirova Daria V., Dolenko Sergei A., Samsonovich Alexei V.
Сборник: Biologically Inspired Cognitive Architectures 2023 - Proceedings of the 14th Annual Meeting of the BICA Society
Серия: Studies in Computational Intelligence
Том: 1130
Год издания: 2024
Издательство: Springer Nature
Местоположение издательства: Switzerland
Первая страница: 348
Последняя страница: 355
DOI: 10.1007/978-3-031-50381-8_35
Аннотация: There are a lot of studies researching automated recognition of emotions. Emotions are represented as points in an emotion space. The emotion space itself is represented by different types of models. One is Facial Action Units System, another is Valence-Arousal-Dominance model. This study aims to create a mapping between these two emotion spaces. The data for the study was collected in a series of experiments with real humans, where both types of measurements were collected simultaneously. Given the data, we study the ability of machine learning models to create this type of mapping. We test different types of models against the task, such as tree-based models and linear models, and make conclusions about the optimal model.
Добавил в систему: Доленко Сергей Анатольевич

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