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Graph Emotion Distribution Learning Using EmotionGCN

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Proceedings of the Future Technologies Conference (FTC) 2022, Volume 1 (FTC 2022 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 559))

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Abstract

Emotion of a person can be identified by the patterns in their ability to think, respond, communicate, or behave in a social environment. It is highly influenced by the emotion, making decisions, and exhibiting behaviors with the surroundings and other well-being. The emotion identification of person is very much useful in the case medical domain. The problem in detecting the emotions from these data is a single image can exhibit different emotions for different persons in their own perspective. With the advancement in the field of computer vision, the adoption of deep convolutional network paved a way to creating a convolutional neural network model which can learn these emotions from the input given and a graph convolutional network to estimate the probability distribution of whole data of emotions as well as the form the data of each emotion separately. The distribution of each emotion can be converted to a graph-based data so that it can be stored and used to train new models without the need for long training time. These graph data are more compatible and paves the way to perform further psychological analysis to extract patterns in them.

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Authors and Affiliations

  1. Department of Computer Science, Amrita Vishwa Vidyapeetham, Kerala, India

    A. Revanth & C. P. Prathibamol

Authors
  1. A. Revanth
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  2. C. P. Prathibamol
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Corresponding author

Correspondence to A. Revanth .

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Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Revanth, A., Prathibamol, C.P. (2023). Graph Emotion Distribution Learning Using EmotionGCN. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2022, Volume 1. FTC 2022 2022. Lecture Notes in Networks and Systems, vol 559. Springer, Cham. https://doi.org/10.1007/978-3-031-18461-1_14

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