Skip to main content
SpringerLink
Log in

Building Resources for Emotion Detection

  • Chapter
  • First Online:
Multimodal Affective Computing

  • 159 Accesses

Abstract

This section of the book describes the methodology used in constructing emotion detection resources; methods for representing the data; multimodal emotion recognition systems, especially those focused on learning-oriented emotions; and techniques for incorporating these systems into intelligent learning environments.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Bahreini, K., Nadolski, R., & Westera, W. (2016). Towards multimodal emotion recognition in e-learning environments. Interactive Learning Environments, 24(3), 590–605.

    Article  Google Scholar 

  • Baştanlar, Y., & Özuysal, M. (2014). Introduction to machine learning. In miRNomics: MicroRNA biology and computational analysis (pp. 105–128).

    Google Scholar 

  • Chen, J., Wang, C., Wang, K., Yin, C., Zhao, C., Xu, T., Zhang, X., Huang, Z., Liu, M., & Yang, T. (2021). Heu emotion: A large-scale database for multimodal emotion recognition in the wild. Neural Computing and Applications, 33, 8669–8685.

    Article  Google Scholar 

  • Cowen, A. S., & Keltner, D. (2017). Self-report captures 27 distinct categories of emotion bridged by continuous gradients. Proceedings of the National Academy of Sciences, 114(38), E7900–E7909.

    Article  Google Scholar 

  • D’Mello, S., & Graesser, A. (2012). Dynamics of affective states during complex learning. Learning and Instruction, 22(2), 145–157.

    Article  Google Scholar 

  • Domínguez-Jiménez, J. A., Campo-Landines, K. C., Martínez-Santos, J. C., Delahoz, E. J., & Contreras-Ortiz, S. H. (2020). A machine learning model for emotion recognition from physiological signals. Biomedical Signal Processing and Control, 55, 101646.

    Article  Google Scholar 

  • Ekman, P. (1992). An argument for basic emotions. Cognition and Emotion, 6(3–4), 169–200.

    Article  Google Scholar 

  • Elices, M., Soler, J., Fernández, C., Martín-Blanco, A., Portella, M. J., Pérez, V., Álvarez, E., & Pascual, J. C. (2012). Physiological and self-assessed emotional responses to emotion-eliciting films in borderline personality disorder. Psychiatry Research, 200(2–3), 437–443.

    Article  Google Scholar 

  • Fischer, A. H., Pauw, L. S., & Manstead, A. S. (2019). Emotion recognition as a social act: The role of the expresser-observer relationship in recognizing emotions. In The social nature of emotion expression: What emotions can tell us about the world (pp. 7–24).

    Google Scholar 

  • Javeed, A., Khan, S. U., Ali, L., Ali, S., Imrana, Y., & Rahman, A. (2022). Machine learning-based automated diagnostic systems developed for heart failure prediction using different types of data modalities: A systematic review and future directions. Computational and Mathematical Methods in Medicine, 2022, 9288452.

    Article  Google Scholar 

  • Koelstra, S., Muhl, C., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., & Patras, I. (2011). Deap: A database for emotion analysis; using physiological signals. IEEE Transactions on Affective Computing, 3(1), 18–31.

    Article  Google Scholar 

  • Kumar, P., Khokher, V., Gupta, Y., & Raman, B. (2021). Hybrid fusion based approach for multimodal emotion recognition with insufficient labeled data. In 2021 IEEE International Conference on Image Processing (ICIP) (pp. 314–318). IEEE.

    Google Scholar 

  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.

    Article  Google Scholar 

  • Liu, S., Gao, P., Li, Y., Fu, W., & Ding, W. (2023). Multi-modal fusion network with complementarity and importance for emotion recognition. Information Sciences, 619, 679–694.

    Article  Google Scholar 

  • Maeng, J.-H., Kang, D.-H., & Kim, D.-H. (2020). Deep learning method for selecting effective models and feature groups in emotion recognition using an Asian multimodal database. Electronics, 9(12), 1988.

    Article  Google Scholar 

  • Maxwell, A. E., Warner, T. A., & Fang, F. (2018). Implementation of machine-learning classification in remote sensing: An applied review. International Journal of Remote Sensing, 39(9), 2784–2817.

    Article  Google Scholar 

  • McDuff, D., El Kaliouby, R., & Picard, R. W. (2012). Crowdsourcing facial responses to online videos. IEEE Transactions on Affective Computing, 3(4), 456–468.

    Article  Google Scholar 

  • Mohammad, S. M. (2022). Ethics sheet for automatic emotion recognition and sentiment analysis. Computational Linguistics, 48(2), 239–278.

    Article  Google Scholar 

  • Pekrun, R. (2014). Emotions and learning. Harvard Educational Review, 25, 95–104.

    Google Scholar 

  • Petrantonakis, P. C., & Hadjileontiadis, L. J. (2011). A novel emotion elicitation index using frontal brain asymmetry for enhanced EEG-based emotion recognition. IEEE Transactions on Information Technology in Biomedicine, 15(5), 737–746.

    Article  Google Scholar 

  • Ragot, M., Martin, N., Em, S., Pallamin, N., & Diverrez, J.-M. (2018). Emotion recognition using physiological signals: Laboratory vs. wearable sensors. In Advances in Human Factors in Wearable Technologies and Game Design: Proceedings of the AHFE 2017 International Conference on Advances in Human Factors and Wearable Technologies, July 17–21, 2017, The Westin Bonaventure Hotel, Los Angeles, California, USA 8 (pp. 15–22). Springer.

    Google Scholar 

  • Saxena, A., Khanna, A., & Gupta, D. (2020). Emotion recognition and detection methods: A comprehensive survey. Journal of Artificial Intelligence and Systems, 2(1), 53–79.

    Article  Google Scholar 

  • Sebe, N., Cohen, I., & Huang, T. S. (2005). Multimodal emotion recognition. In Handbook of pattern recognition and computer vision (pp. 387–409). World Scientific.

    Google Scholar 

  • Shu, L., Xie, J., Yang, M., Li, Z., Li, Z., Liao, D., Xu, X., & Yang, X. (2018). A review of emotion recognition using physiological signals. Sensors, 18(7), 2074.

    Article  Google Scholar 

  • Somarathna, R., Bednarz, T., & Mohammadi, G. (2022). Virtual Reality for Emotion Elicitation – a Review. IEEE Transactions on Affective Computing, 1–21. https://doi.org/10.1109/taffc.2022.3181053

  • Zhao, S., Jia, G., Yang, J., Ding, G., & Keutzer, K. (2021). Emotion recognition from multiple modalities: Fundamentals and methodologies. IEEE Signal Processing Magazine, 38(6), 59–73.

    Article  Google Scholar 

  • Zupan, B., & Babbage, D. R. (2017). Film clips and narrative text as subjective emotion elicitation techniques. The Journal of Social Psychology, 157(2), 194–210.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Instituto Tecnológico de Culiacán, Culiacán, Sinaloa, Mexico

    Ramón Zatarain Cabada & Héctor Manuel Cárdenas López

  2. Instituto Nacional de Astrofísica, Puebla, Puebla, Mexico

    Hugo Jair Escalante

Authors
  1. Ramón Zatarain Cabada
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Héctor Manuel Cárdenas López
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hugo Jair Escalante
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Cabada, R.Z., López, H.M.C., Escalante, H.J. (2023). Building Resources for Emotion Detection. In: Multimodal Affective Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-32542-7_8

Download citation

Publish with us

Policies and ethics

Search

Navigation