[4Q3-IS-2d] Machine learning

[4Q3-IS-2d-02] A Comparative Study of Content Dependent and Independent Emotion Recognition using Convolutional Neural Network Based on DEAP Dataset

〇Zhiying Huang¹, Ao Guo², Jianhua Ma¹ (1. Hosei University, 2. Nagoya University)

キーワード：emotion recognition, content independent, convolutional neural network, physiological signal, human computer interaction

Current research on emotion recognition has mainly focused on content dependent emotion recognition, where a model is trained and tested using user data from the same content sources (e.g., watch a movie or play a game). To provide cross-content services due to users’ emotions anywhere, it is necessary for a model to recognize users’ emotions in different content sources (i.e., content independent). Since limited studies have focused on content independent recognition, whether such emotion recognition has a competitive performance with content dependent emotion recognition is still unclear. To address this issue, we performed a comparative study of content dependent and independent emotion recognition by building CNN-based models from DEAP dataset. The DEAP dataset contains physiological data collected from 32 individuals while they were watching different videos. The data collected while watching a specific video is regarded as a single content. We built content independent model with leave-one-content-out approach. That is, using physiological data from one specific content for testing, and using the data from the remaining contents for training. As a result, we noticed that the performance of content independent recognition is significantly lower than that of content dependent recognition. We also identified that users’ emotions can be easily recognized in certain contents.