Abstract
In the current context, as the metaverse emerges as an immersive digital space for social and professional interactions, understanding and appropriately responding to human emotions becomes critical. This paper presents an innovative approach for emotion detection and analysis in virtual environments, combining facial recognition technologies and physiological signal analysis through deep learning algorithms. Thus, this approach enhances interaction and customization in the metaverse, highlighting the importance of addressing these concerns to maximize the potential of emotional detection.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Mystakidis, S.: Metaverse. Encyclopedia 2(1), 486–497 (2022)
Naz, A., Kopper, R., McMahan, R.P., Nadin, M.: Emotional qualities of VR space. In: 2017 IEEE Virtual Reality (VR), pp. 3–11. IEEE (2017)
Jaiswal, A., Raju, A.K., Deb, S.: Facial emotion detection using deep learning. In: 2020 International Conference for Emerging Technology (INCET), pp. 1–5. IEEE (2020)
Egger, M., Ley, M., Hanke, S.: Emotion recognition from physiological signal analysis: a review. Electron. Notes Theor. Comput. Sci. 343, 35–55 (2019)
Russell, J.A.: A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161 (1980)
Costa, A., Rincon, J.A., Carrascosa, C., Julian, V., Novais, P.: Emotions detection on an ambient intelligent system using wearable devices. Future Gener. Comput. Syst. 92, 479–489 (2019)
Hutter, F., Hoos, H.H., Leyton-Brown, K.: Sequential model-based optimization for general algorithm configuration. In: Coello, C.A.C. (ed.) LION 2011. LNCS, vol. 6683, pp. 507–523. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25566-3_40
Beliakov, G., Pradera, A., Calvo, T.: Aggregation Functions: A Guide for Practitioners, vol. 18 (2007)
Calvo, T., Kolesárová, A., Komorníková, M., Mesiar, R.: Aggregation operators: properties, classes and construction methods. Aggreg. Oper. New Trends Appl. 97(1), 3–104 (2002)
Yager, R.R.: On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Trans. Syst. Man Cybern. 18(1), 183–190 (1988)
Yager, R.: Quantifier guided aggregation using OWA operators. Int. J. Intell. Syst. 11(1), 49–73 (1996)
Acknowledgements
This work was partially supported with grants TED2021-131295B-C32 and PID2021-123673OB-C31 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”, PROMETEO grant CIPROM/2021/077 from the Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital - Generalitat Valenciana and Early Research Project grant PAID-06-23 by the Vice Rectorate Office for Research from Universitat Politècnica de València (UPV).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Rincon, J.A., Marco-Detchart, C., Julian, V. (2024). Towards Enhanced Emotional Interaction in the Metaverse. In: Ferrández Vicente, J.M., Val Calvo, M., Adeli, H. (eds) Artificial Intelligence for Neuroscience and Emotional Systems. IWINAC 2024. Lecture Notes in Computer Science, vol 14674. Springer, Cham. https://doi.org/10.1007/978-3-031-61140-7_45
Download citation
DOI: https://doi.org/10.1007/978-3-031-61140-7_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-61139-1
Online ISBN: 978-3-031-61140-7
eBook Packages: Computer ScienceComputer Science (R0)