Skip to main content

Brief Technical Analysis of Facial Expression Recognition

  • Conference paper
  • First Online:
Intelligent Computing and Internet of Things (ICSEE 2018, IMIOT 2018)

Abstract

Facial expression recognition (FER) is the current hot research topic, and it is widely used in the fields of pattern recognition, computer vision and artificial intelligence. As it is an important part of intelligent human-computer interaction technology, the FER has received widespread attention in recent years, and researchers in different fields have proposed many approaches for it. This paper reviews recent developments on FER approaches and the key technologies involved in the FER system: face detection and preprocessing, facial expression feature extraction and facial expression classification, which are analyzed and summarized in detail. Finally, the state-of-the-art of the FER is summarized, and its future development direction is pointed out.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

References

  1. Mehrabian, A.: Communication without words. Psychol. Today 2, 53–55 (1968)

    Google Scholar 

  2. Ekman, P., Friesen, W.V.: Constants across cultures in the face and emotion. J. Pers. Soc. Psychol. 17, 124–129 (1971)

    Article  Google Scholar 

  3. Ekman, P.: Facial Action Coding System. A Technique for the Measurement of Facial Action (1978)

    Google Scholar 

  4. Hu, P.: Application research on face detection technology based on OpenCV in mobile augmented reality. Int. J. Sign. Process. Image Process. Pattern Recogn. 8, 249–256 (2015)

    Google Scholar 

  5. Shbib, R., Zhou, S.: Facial expression analysis using active shape model. Int. J. Sign. Process. Image Process. Pattern Recogn. 8, 9–22 (2016)

    Google Scholar 

  6. Li, Y., Liu, W., Li, X., Huang, Q., Li, X.: GA-SIFT: a new scale invariant feature transform for multispectral image using geometric algebra. Inf. Sci. 281, 559–572 (2014)

    Article  MathSciNet  Google Scholar 

  7. Chao, W.L., Ding, J.J., Liu, J.Z.: Facial expression recognition based on improved local binary pattern and class-regularized locality preserving projection. Signal Process. 117, 1–10 (2015)

    Article  Google Scholar 

  8. Ghimire, D.: Recognition of facial expressions based on tracking and selection of discriminative geometric features. Int. J. Multimed. Ubiquitous Eng. 10, 35–44 (2015)

    Article  Google Scholar 

  9. Dewan, M.A.A., Granger, E., Marcialis, G.L., Sabourin, R., Roli, F.: Adaptive appearance model tracking for still-to-video face recognition. Pattern Recogn. 49, 129–151 (2016)

    Article  Google Scholar 

  10. Zafeiriou, S., Pitas, I.: Discriminant graph structures for facial expression recognition. IEEE Trans. Multimed. 10, 1528–1540 (2008)

    Article  Google Scholar 

  11. Siddiqi, M.H., Ali, R., Khan, A.M., Park, Y.T., Lee, S.: Human facial expression recognition using stepwise linear discriminant analysis and hidden conditional random fields. IEEE Trans. Image Process. 24, 1386–1398 (2015)

    Article  MathSciNet  Google Scholar 

  12. Zhao, X.: Facial expression recognition using local binary patterns and discriminant kernel locally linear embedding. EURASIP J. Adv. Signal Process. 2012, 20 (2012)

    Article  Google Scholar 

  13. Wang, S., Yang, H., Li, H.: Facial expression recognition based on incremental isomap with expression weighted distance. J. Comput. 8, 2051–2058 (2013)

    Google Scholar 

  14. Taheri, S., Qiu, Q., Chellappa, R.: Structure-preserving sparse decomposition for facial expression analysis. IEEE Trans. Image Process. 23, 3590–3603 (2014)

    Article  MathSciNet  Google Scholar 

  15. Wu, C., Wang, S., Ji, Q.: Multi-instance hidden Markov model for facial expression recognition. In: IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, pp. 1–6 (2015)

    Google Scholar 

  16. Bahari, F., Janghorbani, A.: EEG-based emotion recognition using recurrence plot analysis and K nearest neighbor classifier. In: Biomedical Engineering, pp. 228–233 (2013)

    Google Scholar 

  17. Wang, F., He, K., Liu, Y., Li, L., Hu, X.: Research on the selection of kernel function in SVM based facial expression recognition. In: Industrial Electronics and Applications, pp. 1404–1408 (2013)

    Google Scholar 

  18. Schapire, R.E.: Explaining AdaBoost. Springer, Heidelberg (2013)

    Book  Google Scholar 

  19. Owusu, E., Zhan, Y., Mao, Q.R.: A neural-AdaBoost based facial expression recognition system. Expert Syst. Appl. 41, 3383–3390 (2014)

    Article  Google Scholar 

  20. Lopes, A.T., Aguiar, E.D., Souza, A.F.D., Oliveira-Santos, T.: Facial expression recognition with convolutional neural networks: coping with few data and the training sample order. Pattern Recogn. 61, 610–628 (2017)

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by Natural Science Foundation of Shanghai (No. 18ZR1415100), National Science Foundation of China (61473182, 61773253), Science and Technology Commission of Shanghai Municipality (15JC1401900) and Key research and development project of Yantai (2017ZH061).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Aolei Yang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Xu, L., Yang, A., Fei, M., Zhou, W. (2018). Brief Technical Analysis of Facial Expression Recognition. In: Li, K., Fei, M., Du, D., Yang, Z., Yang, D. (eds) Intelligent Computing and Internet of Things. ICSEE IMIOT 2018 2018. Communications in Computer and Information Science, vol 924. Springer, Singapore. https://doi.org/10.1007/978-981-13-2384-3_28

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-2384-3_28

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2383-6

  • Online ISBN: 978-981-13-2384-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics