Multi-modal Approach for Affective Computing
This work addresses the challenge of improving emotion classification accuracy in affective computing, though it appears incremental by building on existing multi-modal datasets and methods.
This study tackled the problem of classifying human emotions by comparing single and multi-modal approaches using face videos and bio-sensing modalities, showing an increase in classification accuracy with a novel method for compensating physiological baseline.
Throughout the past decade, many studies have classified human emotions using only a single sensing modality such as face video, electroencephalogram (EEG), electrocardiogram (ECG), galvanic skin response (GSR), etc. The results of these studies are constrained by the limitations of these modalities such as the absence of physiological biomarkers in the face-video analysis, poor spatial resolution in EEG, poor temporal resolution of the GSR etc. Scant research has been conducted to compare the merits of these modalities and understand how to best use them individually and jointly. Using multi-modal AMIGOS dataset, this study compares the performance of human emotion classification using multiple computational approaches applied to face videos and various bio-sensing modalities. Using a novel method for compensating physiological baseline we show an increase in the classification accuracy of various approaches that we use. Finally, we present a multi-modal emotion-classification approach in the domain of affective computing research.