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Cross-subject multimodal emotion recognition based on hybrid fusion
journal contributionposted on 16.09.2020 by Yucel Cimtay, Erhan Ekmekcioglu, Seyma Caglar Ozhan
Any type of content formally published in an academic journal, usually following a peer-review process.
Multimodal emotion recognition has gained traction in affective computing research community to overcome the limitations posed by the processing a single form of data and to increase recognition robustness. In this study, a novel emotion recognition system is introduced, which is based on multiple modalities including facial expressions, galvanic skin response (GSR) and electroencephalogram (EEG). This method follows a hybrid fusion strategy and yields a maximum one-subject-out accuracy of 81.2% and a mean accuracy of 74.2% on our bespoke multimodal emotion dataset (LUMED-2) for 3 emotion classes: sad, neutral and happy. Similarly, our approach yields a maximum one-subject-out accuracy of 91.5% and a mean accuracy of 53.8% on the Database for Emotion Analysis using Physiological Signals (DEAP) for varying numbers of emotion classes, 4 in average, including angry, disgust, afraid, happy, neutral, sad and surprised. The presented model is particularly useful in determining the correct emotional state in the case of natural deceptive facial expressions. In terms of emotion recognition accuracy, this study is superior to, or on par with, the reference subject-independent multimodal emotion recognition studies introduced in the literature.
This work was supported by an Institutional Links grant, ID 352175665, under the Newton - Katip Celebi partnership between the UK and Turkey. The grant is funded by the UK Department of Business, Energy and Industrial Strategy (BEIS) and The Scientific and Technological Research Council of Turkey (TUBITAK) and delivered by the British Council. For further information, please visit www.newtonfund.ac.uk.
- Loughborough University London