Skip to main content
SpringerLink
Log in

Emotion recognition system for E-learning environment based on facial expressions

  • Application of soft computing
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

The popularity of E-learning has grown significantly due to the continuous growth of Internet usage and related technologies. However, the lack of face-to-face interaction in E-Learning poses a challenge in detecting the emotions of the learners. Although existing emotion recognition systems can identify the six universal emotions, they are unable to recognize the emotions specific to the E-learning environment, such as Confusion, Boredom, Concentration, and Self-Confidence. To tackle this challenge, a new emotion recognition system is proposed that considers multiple portions of the face. The proposed method utilizes the Viola–Jones algorithm for face detection, local binary patterns (LBP) for extracting the local facial features, and fuzzy neural network for classification. The experimental results demonstrate that the proposed system performs better than existing methods, achieving a higher prediction accuracy of 91.25%.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Data availability

The datasets generated during the current study are not publicly available but are available from the corresponding author on reasonable request.

References

  • Ashwin TS, Jose J, Raghu G, Reddy GRM (2015) An E-learning system with multifacial emotion recognition using supervised machine learning. In: IEEE seventh international conference on technology for education

  • Ayvaz U, Gürüler H, Devrim MO (2017) Use of facial emotion recognition in E-learning systems. Inf Technol Learn Tools 60(4)

  • Deshmukh SP, Patwardhan MS, Mahajan AR (2018) Feedback based real time facial and head gesture recognition for E-learning system. In: International conference on data science and management of data

  • Huang D, Shan C, Ardebilian M, Wang Y, Chen L (2011) Local binary patterns and its application to facial image analysis: a survey. IEEE Trans Syst Man Cybern Part C (Appl Rev) 41(6)

  • Kahraman C, Bolturk E, Onar SC, Oztaysi B (2020) Modeling humanoid robots facial expressions using pythagorean fuzzy sets. J Intell Fuzzy Syst 39(5):6507–6515

    Article  MATH  Google Scholar 

  • Kavitha S, Paul V, Jothi Swaroopan NM (2017) Biometric emotion recognition using adaptive neuro fuzzy inference system. Middle-East J Sci Res 25(8):1644–1649

    Google Scholar 

  • Krithika LB, Lakshmi Priya GG (2016) Student emotion recognition system (SERS) for E-learning improvement based on learner concentration metric. Proc Comput Sci 85:767–776

    Article  Google Scholar 

  • Viola PA, Jones MJ (2001) Rapid object detection using a boosted cascade. In: IEEE computer society conference on computer vision and pattern recognition CVPR, pp 511–518

  • Yang D, Alsadoon A, Prasad PWC, Singh AK, Elchouemi A (2018) An emotion recognition model based on facial recognition in virtual learning environment. Proc Comput Sci 125:2–10

    Article  Google Scholar 

Download references

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, NIT Nagaland, Chumukedima, Dimapur, 797103, India

    Farzana Begum, Arambam Neelima & J. Arul Valan

Authors
  1. Farzana Begum
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arambam Neelima
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. Arul Valan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Farzana Begum, Dr. Arambam Neelima and Dr. J. Arul Valan. The first draft of the manuscript was written by Farzana Begum, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript

Corresponding author

Correspondence to Farzana Begum.

Ethics declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Begum, F., Neelima, A. & Valan, J.A. Emotion recognition system for E-learning environment based on facial expressions. Soft Comput 27, 17257–17265 (2023). https://doi.org/10.1007/s00500-023-08058-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-023-08058-3

Keywords

Search

Navigation