Skip to main content

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 614))

  • 329 Accesses

Abstract

Facial Emotion recognition (FER) is a significant research domain in computer vision. FER is considered a challenging task due to emotion-related differences such as heterogeneity of human faces, differences in images due to lighting conditions, angled faces, head poses, different background settings, etc. Moreover, there is also a need for a generalized and efficient model for emotion identification. So, this paper presents a novel, efficient, and generalized DarkSiL (DS) detector for FER that is robust to variation in illumination conditions, face orientation, gender, different ethnicities, and varied background settings. We have introduced a low-cost, smooth, bounded below, and unbounded above Sigmoid-weighted linear unit function in our model to improve efficiency as well as accuracy. The performance of the proposed model is evaluated on four diverse datasets including CK + , FER-2013, JAFFE, and KDEF datasets and achieved an accuracy of 99.6%, 64.9%, 92.9%, and 91%, respectively. We also performed a cross-dataset evaluation to show the generalizability of our DS detector. Experimental results prove the effectiveness of the proposed framework for the reliable identification of seven different classes of emotions.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Similar content being viewed by others

References

  1. Ramdhani B, Djamal EC, Ilyas R (2018, August). Convolutional neural networks models for facial expression recognition. In 2018 International Symposium on Advanced Intelligent Informatics (SAIN). IEEE, pp 96–101

    Google Scholar 

  2. Mehta D, Siddiqui MFH, Javaid AY (2018) Facial emotion recognition: A survey and real-world user experiences in mixed reality. Sensors 18(2):416

    Article  Google Scholar 

  3. Kola DGR, Samayamantula SK (2021) A novel approach for facial expression recognition using local binary pattern with adaptive window. Multimed Tools Appl 80(2):2243–2262

    Article  Google Scholar 

  4. Niu B, Gao Z, Guo B (2021). Facial expression recognition with LBP and ORB features. Comput Intell Neurosci

    Google Scholar 

  5. Liu K, Zhang M, Pan Z (2016, September). Facial expression recognition with CNN ensemble. In 2016 International Conference on Cyberworlds (CW), IEEE. pp 163–166

    Google Scholar 

  6. Jain DK, Shamsolmoali P, Sehdev P (2019) Extended deep neural network for facial emotion recognition. Pattern Recogn Lett 120:69–74

    Article  Google Scholar 

  7. Wang H, Zhang F, Wang L (2020, January) Fruit classification model based on improved Darknet53 convolutional neural network. In 2020 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), IEEE. pp 881–884

    Google Scholar 

  8. Lucey P, Cohn JF, Kanade T, Saragih J, Ambadar Z, Matthews I (2010, June). The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression. In 2010 IEEE Computer Society Conference on Computer Vision And Pattern Recognition-Workshops, IEEE. pp 94–101

    Google Scholar 

  9. Lyons MJ, Kamachi M, Gyoba J (2020) Coding facial expressions with Gabor wavelets (IVC special issue). arXiv preprint arXiv:2009.05938

  10. Goodfellow IJ, Erhan D, Carrier PL, Courville A, Mirza M, Hamner B, Bengio Y (2013, November) Challenges in representation learning: A report on three machine learning contests. In International Conference on Neural Information Processing, pp. 117–124. Springer, Berlin, Heidelberg

    Google Scholar 

  11. Lundqvist D, Flykt A, Öhman A (1998) Karolinska directed emotional faces. Cogn Emot

    Google Scholar 

  12. Sun Z, Hu ZP, Wang M, Zhao SH (2017) Individual-free representation-based classification for facial expression recognition. SIViP 11(4):597–604

    Article  Google Scholar 

  13. Lim N (2016) Cultural differences in emotion: differences in emotional arousal level between the East and the West. Integr Med Res 5(2):105–109

    Article  Google Scholar 

  14. Liew CF, Yairi T (2015) Facial expression recognition and analysis: a comparison study of feature descriptors. IPSJ transactions on computer vision and applications 7:104–120

    Article  Google Scholar 

  15. Hussain SA, Al Balushi ASA (2020). A real time face emotion classification and recognition using deep learning model. In Journal of physics: Conference Series 1432(1), p 012087. IOP Publishing

    Google Scholar 

  16. Williams T, Li R (2018, February) Wavelet pooling for convolutional neural networks. In International Conference on Learning Representations

    Google Scholar 

  17. Jung H, Lee S, Yim J, Park S, Kim J (2015). Joint fine-tuning in deep neural networks for facial expression recognition. In Proceedings of the IEEE International Conference on Computer Vision, pp 2983–2991

    Google Scholar 

  18. Talegaonkar I, Joshi K, Valunj S, Kohok R, Kulkarni A (2019, May) Real time facial expression recognition using deep learning. In Proceedings of International Conference on Communication and Information Processing (ICCIP)

    Google Scholar 

  19. Elfwing S, Uchibe E, Doya K (2018) Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. Neural Netw 107:3–11

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Multimedia Signal Processing Research Lab at the University of Engineering and Technology, Taxila, Pakistan.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ali Javed .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dar, T., Javed, A. (2023). DarkSiL Detector for Facial Emotion Recognition. In: Anwar, S., Ullah, A., Rocha, Á., Sousa, M.J. (eds) Proceedings of International Conference on Information Technology and Applications. Lecture Notes in Networks and Systems, vol 614. Springer, Singapore. https://doi.org/10.1007/978-981-19-9331-2_7

Download citation

Publish with us

Policies and ethics