[4D3-OS-4b] Affective Computing(2/3)

[4D3-OS-4b-05] A Method of Ensemble Learning Towards Emotion Recognition Considering Individual Differences

〇Akihiro Matsufuji¹, Erina Kasano¹, Eri Sato-Shimokawara¹, Toru Yamaguchi¹ (1. Tokyo Metropolitan University)

Keywords:Affective Computing, Non-Verbal Information, Multi-Modal Learning

It is desirable for interactive robots and artificial agents to take into account the emotion and provides appropriate empathetic output to the user. The development of machine learning and deep learning technologies helped to have a big advance in the research field of emotion understanding by machine. However, these sophisticated technologies still struggling with individual differences in emotion. In this paper, we present an ensemble learning method towards emotion recognition considering the individual differences. Our proposed method divided the training data into each person's training data, and train the independent multi-models corresponding to each person as submodels of ensemble learning architecture. Furthermore, we implemented the dynamic weight decision for selecting the appropriate submodel to recognize the user's emotion using a few samples of the user's emotional behavior. As a result, our architecture performed well than the conventional machine learning model.