The effects of sensor and feature level fusion methods in multimodal emotion analysis

Authors : Bahar Hatipoğlu Yılmqz, Cemal Köse

Pages : 1093-1099

Doi:10.28948/ngumuh.1469289

View : 108 | Download : 150

Publication Date : 2024-10-15

Article Type : Research Paper

Abstract :Fusion-based studies on multimodal emotion recognition (MER) are very popular nowadays. In this study, EEG signals and facial images are fused using Sensor Level Fusion (SLF) and Feature Level Fusion (FLF) methods for multimodal emotion recognition. The general procedure of the study is as follows. First, the EEG signals are converted into angle amplitude graph (AAG) images. Second, 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 on 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 an important contribution to the field of emotion recognition by providing an effective method.
Keywords : Çok modlu duygu tanıma, Sensör seviyesinde füzyon, Öznitelik seviyesinde füzyon, DEAP veri seti