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Map** Action Units to Valence and Arousal Space Using Machine Learning

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Biologically Inspired Cognitive Architectures 2023 (BICA 2023)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1130))

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Abstract

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 map** 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 map**. We test different types of models against the task, such as tree-based models and linear models, and make conclusions about the optimal model.

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Acknowledgement

This study was supported by the Russian Science Foundation grant no. 22-11-00213, https://rscf.ru/en/project/22-11-00213/.

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Authors and Affiliations

  1. Physical Department, Leninskiye Gory, M.V.Lomonosov Moscow State University, Moscow, 119991, Russian Federation

    Ismail M. Gadzhiev & Alexander S. Makarov

  2. D.V.Skobeltsyn Institute of Nuclear Physics, Leninskiye Gory, M.V.Lomonosov Moscow State University, Moscow, 119991, Russian Federation

    Ismail M. Gadzhiev & Sergei A. Dolenko

  3. National Research Nuclear University MEPhI, Kashirskoye Shosse, 31, Moscow, 115409, Russian Federation

    Ismail M. Gadzhiev, Daria V. Tikhomirova & Alexei V. Samsonovich

Authors
  1. Ismail M. Gadzhiev
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  2. Alexander S. Makarov
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  3. Daria V. Tikhomirova
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  4. Sergei A. Dolenko
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  5. Alexei V. Samsonovich
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Corresponding author

Correspondence to Ismail M. Gadzhiev .

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Editors and Affiliations

  1. Cybernetics Department, Moscow Engineering Physics Institute, Moscow, Russia

    Alexei V. Samsonovich

  2. College of Science and Technology, Ningbo University, Ningbo, China

    Tingting Liu

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Gadzhiev, I.M., Makarov, A.S., Tikhomirova, D.V., Dolenko, S.A., Samsonovich, A.V. (2024). Map** Action Units to Valence and Arousal Space Using Machine Learning. In: Samsonovich, A.V., Liu, T. (eds) Biologically Inspired Cognitive Architectures 2023. BICA 2023. Studies in Computational Intelligence, vol 1130. Springer, Cham. https://doi.org/10.1007/978-3-031-50381-8_35

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