The Effects of Sensor and Feature Level Fusion Methods in Multimodal Emotion Analysis

Fusion-based multimodal emotion recognition (MER) studies are very popular nowadays. In this study, EEG signals and face images are fused with sensor level fusion (SLF) and feature level fusion (FLF) methods for multimodal emotion recognition. The general flow of the study is as follows. Firstly, EEG signals are converted into angle amplitude graph (AAG) images. Secondly, the most unique ones are automatically identified from all face images obtained from video recordings. Then, these modalities are fused separately using SLF and FLF methods. The fusion approaches were used to combine the obtained data and perform classification on the integrated data. The experiments were performed with the publicly available DEAP dataset. The highest accuracy was 82.14% with 5.26 standard deviations for SLF and 87.62% with 6.74 standard deviations for FLF. These results show that this study makes a significant contribution to the field of emotion recognition by providing an effective method.