Skip to main content
SpringerLink
Log in

Analysis of Local Binary Pattern for Facial Expression Recognition Using Patch Local Binary Pattern on Extended Cohn Kanade Database

  • Conference paper
  • First Online:
10th International Conference on Robotics, Vision, Signal Processing and Power Applications

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 547))

Abstract

Deriving an effective facial representation from original face images is a vital step for successful facial expression recognition. In this paper, we explore the effect of representing the information in facial expression recognition using local patch local binary pattern (LBP). The processed information with accurate representation of different expressions can discriminate and improve the overall facial expression recognition accuracy. The objective of this paper is to analyze the effect of using local region patch to represent facial features with local binary pattern as the feature extraction method. In our experiment, first facial landmark is being detected using cascade linear regression, followed by alignment and normalization. The LBP feature extraction is being performed on the holistic image, followed by patch of mouth and eyes. At the final stage support vector machine (SVM) has been used as a classifier to examine the recognition rate. The results are being validated using the extended Cohn-Kanade database. From the analysis, it is found that using local region LBP can significantly reduce the number of features to be fed into SVM. Hence the processing time is improved.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
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

  1. Kumari, J., Rajesh, R., Pooja, K.: Facial expression recognition: A survey. Procedia Comput. Sci. 58, 486–491 (2015)

    Article  Google Scholar 

  2. Shan, C., Gong, S., McOwan, P.W.: Facial expression recognition based on local binary patterns: a comprehensive study. Image Vis. Comput. 27(6), 803–816 (2009)

    Article  Google Scholar 

  3. Hassan, H., Yaacob, S., Radman, A., Suandi, S.A.: Eye state detection for driver inattention based on Lucas Kanade optical flow algorithm. In: International Conference on Intelligent and Advanced System ICIAS 2016, no. IEEE Xplore (2017)

    Google Scholar 

  4. Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Inlligence 28(12), 2037–2041 (2006)

    Article  Google Scholar 

  5. Pumlumchiak, T., Sirion, V.: Facial expression recognition using local gabor filters and PCA plus LDA. In: 9th International Conference on Information Technology and Electrical Engineering, pp. 1–6 (2017)

    Google Scholar 

  6. Liu, Y., Li, Y., Ma, X., Song, R.: Facial expression recognition with fusion features extracted from salient facial areas. Sensors 17(4), 1–19 (2017)

    Article  Google Scholar 

  7. Zhang, S., Zhao, X., Lei, B.: Robust facial expression recognition via compressive sensing. Sensors 12(3), 3747–3761 (2012)

    Article  Google Scholar 

  8. Guarnera, M., Hichy, Z., Cascio, M.I., Carrubba, S.: Facial expressions and ability to recognize emotions from eyes or mouth in children. Eur. J. Psychol. 11(2), 183–196 (2015)

    Article  Google Scholar 

  9. Ghimire, D., Jeong, S., Lee, J., Park, S.H.: Facial expression recognition based on local region specific features and support vector machines. Multimed Tools Appl. 76, 7803–7821 (2017)

    Article  Google Scholar 

  10. Lucey, P., Cohn, J.F., Kanade, T., Saragih, J., Ambadar, Z., Matthews, I.: The extended Cohn-Kanade dataset (CK +): a complete dataset for action unit and emotion-specified expression. In: Computer Vision and Pattern Recognition Workshops, pp. 94–101 (2010)

    Google Scholar 

  11. Pumlumchiak, T.: Facial expression recognition using local gabor filters and PCA plus LDA. In: 2017 9th International Conference on Information Technology and Electrical Engineering, pp. 1–6, Oct 2017

    Google Scholar 

  12. Asthana, A., Zafeiriou, S., Cheng, S., Pantic, M.: Incremental face alignment in the wild. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1859–1866 (2014)

    Google Scholar 

  13. Zhao, X., Zhang, S.: Facial expression recognition based on local binary patterns and kernel discriminant isomap. Sensors 11(10), 9573–9588 (2011)

    Article  Google Scholar 

  14. Ojala, T., Pietikasinen, M., Harwood, D.: A comparative study of texture measure with classification based on feature distributions. Pattern Recognit. 29(l), 51–59 (1996)

    Google Scholar 

  15. Cigdem Turan, K.-M.L.: Region-based feature fusion for facial-expression recognition. In: International Conference on Image Processing, pp. 5966–5970 (2014)

    Google Scholar 

Download references

Acknowledgements

The authors thank the Universiti Sains Malaysia for the material resources and expertise support in preparing this research proposal. This research is fully supported by research university individual grant from Universiti Sains Malaysia, Grant No. 304/PELECT/6316115.

Author information

Authors and Affiliations

  1. Malaysian Spanish Institute, Universiti Kuala Lumpur, Kulim, 09000, Kuala Lumpur, Kedah, Malaysia

    Halina Hassan

  2. School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Engineering Campus, 14300, Nibong Tebal, Pulau Pinang, Malaysia

    Halina Hassan & Shahrel Azmin Suandi

Authors
  1. Halina Hassan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shahrel Azmin Suandi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Halina Hassan or Shahrel Azmin Suandi .

Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, Penang, Malaysia

    Mohamad Adzhar Md Zawawi

  2. School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, Penang, Malaysia

    Soo Siang Teoh

  3. School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, Penang, Malaysia

    Noramalina Binti Abdullah

  4. School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal, Penang, Malaysia

    Mohd Ilyas Sobirin Mohd Sazali

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Hassan, H., Suandi, S.A. (2019). Analysis of Local Binary Pattern for Facial Expression Recognition Using Patch Local Binary Pattern on Extended Cohn Kanade Database. In: Zawawi, M., Teoh, S., Abdullah, N., Mohd Sazali, M. (eds) 10th International Conference on Robotics, Vision, Signal Processing and Power Applications. Lecture Notes in Electrical Engineering, vol 547. Springer, Singapore. https://doi.org/10.1007/978-981-13-6447-1_80

Download citation

Publish with us

Policies and ethics

Search

Navigation