Skip to main content

Advertisement

SpringerLink
Log in

MDTP: a novel multi-directional triangles pattern for face expression recognition

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Face expression recognition is a key-subject of machine learning, and the primary issue on it is lack of accuracy. This paper proposes a novel face expression recognition method using Decision Based Rule-Oriented Median Filter (DBROMF) and Multi-Directional Triangles Pattern (FER-MDTP). The DBROMF is a novel noise reduction method which removes the impulse noise from the facial images. This FER method enriched with a new image descriptor (MDTP) which is structured via multi-directional triangle pattern to provide a superior image description. An unparalleled novel algorithm to locate the human face organ viz. lip and eyeball stuffed in this research by getting assistance with fuzzy edge strength and abbreviated as an MDTP-FES method. These landmarks of features extracted, and the histogram oriented features tailored with the classification division. The support vector neural network classifier (SVNN) is integrated to conduct the classification job. The JAFFE, CK, TFEID, and ADFES databases are linked to perform the simulation which telescoped with six face expressions. The proposed method lifts the accuracy to a significant range than the existing state-of-the-art methods.

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

Similar content being viewed by others

References

  1. Bettadapura V (2012) Face Expression Recognition and Analysis: The State of the Art. 1–27

  2. Chu WS, Torre FD, Cohn JF (2017) Selective Transfer Machine for Personalized Facial Expression Analysis. IEEE Trans Pattern Anal Mach Intell 39:529–545. https://doi.org/10.1109/TPAMI.2016.2547397

    Article  Google Scholar 

  3. Yan H, Ang MH, Poo AN (2012) Adaptive discriminative metric learning for facial expression recognition. IET Biometrics 1:160–167. https://doi.org/10.1049/iet-bmt.2012.0006

    Article  Google Scholar 

  4. Zhang T, Zheng W, Cui Z et al (2016) A Deep Neural Network Driven Feature Learning Method for Multi-view Facial Expression Recognition. IEEE Trans Multimed 18:2528–2536. https://doi.org/10.1109/TMM.2016.2598092

    Article  Google Scholar 

  5. Mistry K, Zhang L, Neoh SC et al (2017) A Micro-GA Embedded PSO Feature Selection Approach to Intelligent Facial Emotion Recognition. IEEE Trans Cybern 47:1496–1509

    Article  Google Scholar 

  6. Hsieh C, Hsih M (2016) Effective Semantic Features for Facial Expressions Recognition using SVM. Multimed Tools Appl 75:6663–6682. https://doi.org/10.1007/s11042-015-2598-1

    Article  Google Scholar 

  7. Yi J, Mao X, Chen L et al (2014) Facial Expression Recognition Considering Individual Differences in Facial Structure and Texture. IET Comput Vis 8:429–440. https://doi.org/10.1049/iet-cvi.2013.0171

    Article  Google Scholar 

  8. Meng H, Bianchi-Berthouze N, Deng Y et al (2016) Time-Delay Neural Network for Continuous Emotional Dimension Prediction From Facial Expression Sequences. IEEE Trans Cybern 46:916–929. https://doi.org/10.1109/TCYB.2015.2418092

    Article  Google Scholar 

  9. Liu Z, Wu M, Cao W, Chen L (2017) A Facial Expression Emotion Recognition Based Human-robot Interaction System. IEEE/CAA J Autom Sin 4:668–676

    Article  Google Scholar 

  10. Muhammad G, Alsulaiman M, Amin SU et al (2017) A Facial-Expression Monitoring System for Improved Healthcare in Smart Cities. IEEE Access 5:10871–10881. https://doi.org/10.1109/ACCESS.2017.2712788

    Article  Google Scholar 

  11. Munir A, Hussain A, Khan SA, Nadeem M (2018) Illumination invariant facial expression recognition using selected merged binary patterns for real world images. Opt - Int J Light Electron Opt 158:1016–1025. https://doi.org/10.1016/j.ijleo.2018.01.003

    Article  Google Scholar 

  12. Zhang K, Huang Y, Du Y, Wang L (2017) Facial Expression Recognition Based on Deep Evolutional Spatial-Temporal Networks. IEEE Trans Image Process 29:4193–4203. https://doi.org/10.1109/TIP.2017.2689999

    Article  MathSciNet  MATH  Google Scholar 

  13. Ding Y, Zhao Q, Li B, Yuan X (2017) Facial Expression Recognition From Image Sequence Based on LBP and Taylor Expansion. IEEE Access 5:19409–19419

    Article  Google Scholar 

  14. Uddin MZ, Khaksar W, Torresen J (2017) Facial Expression Recognition Using Salient Features and Convolutional Neural Network. IEEE Access 5:26146–26161

    Article  Google Scholar 

  15. Yang B, Cao J, Ni R, Zhang Y (2018) Facial Expression Recognition Using Weighted Mixture Deep Neural Network Based on Double-Channel Facial Images. IEEE Access 6:4630–4640

    Article  Google Scholar 

  16. Uddin ZIA, Hassan MM (2017) Facial Expression Recognition Utilizing Local Direction-Based Robust Features and Deep Belief Network. IEEE Access 5:4525–4536

    Article  Google Scholar 

  17. Mao Q, Rao Q, Yu Y, Dong M (2017) Hierarchical Bayesian Theme Models for Multi-pose Facial Expression Recognition. IEEE Trans Multimed 19:861–873. https://doi.org/10.1109/TMM.2016.2629282

    Article  Google Scholar 

  18. Zeng N, Zhang H, Song B et al (2017) Facial Expression Recognition via Learning Deep Sparse Autoencoders. Neurocomputing 273:643–649. https://doi.org/10.1016/j.neucom.2017.08.043

    Article  Google Scholar 

  19. Meena HK, Sharma KK, Joshi SD (2017) Improved Facial Expression Recognition Using Graph Signal Processing. Electron Lett 53:718–720. https://doi.org/10.1049/el.2017.0420

    Article  Google Scholar 

  20. Cruz EAS, Jung CR, Franco CHE (2017) Facial Expression Recognition using temporal POEM features. Pattern Recogn Lett 114:13–21. https://doi.org/10.1016/j.patrec.2017.08.008

    Article  Google Scholar 

  21. Ludwig O, Nunes U, Araujo R (2014) Eigenvalue Decay: a New Method for Neural Network Regularization. Neurocomputing 124:33–42

    Article  Google Scholar 

  22. Michael L, Miyuki K, Jiro G (1999) Facial Expression Database: Japanese Female Facial Expression Database. http://www.kasrl.org/jaffe.html. Accessed 11 Nov 2018

  23. (2000) Cohn-Kanade AU-Coded Expression Database. http://www.pitt.edu/~emotion/ck-spread.htm. Accessed 11 Nov 2018

  24. (2007) TFEID: Taiwanese Facial Expression Image Database. http://bml.ym.edu.tw/tfeid/. Accessed 5 Feb 2019

  25. Van Der SJ, Hawk ST, Fischer AH, Doosje B (2011) Moving Faces, Looking Places: Validation of the Amsterdam Dynamic Facial Expression Set (ADFES). 11:907–920. doi: https://doi.org/10.1037/a0023853

  26. Rivera AR, Castillo JR, Chae O (2013) Local Directional Number Pattern for Face Analysis : Face and Expression Recognition. IEEE Trans Image Process 22:1740–1752

    Article  MathSciNet  MATH  Google Scholar 

  27. Carcagnì P, Del Coco M, Leo M, Distante C (2015) Facial expression recognition and histograms of oriented gradients: a comprehensive study. Springerplus 4. https://doi.org/10.1186/s40064-015-1427-3

  28. Kumar S, Bhuyan MK, Chakraborty BK (2016) Extraction of Informative Regions of a Face for Facial Expression Recognition. IET Comput Vis 10:567–576. https://doi.org/10.1049/iet-cvi.2015.0273

    Article  Google Scholar 

  29. Khan SA, Hussain A, Usman M (2018) Reliable Facial Expression Recognition for Multi-scale Images using Weber Local Binary Image Based Cosine Transform Features. Multimed Tools Appl 77:1133–1165. https://doi.org/10.1007/s11042-016-4324-z

    Article  Google Scholar 

  30. Nazir M, Jan Z, Sajjad M (2017) Facial expression recognition using histogram of oriented gradients based transformed features. Clust Comput. https://doi.org/10.1007/s10586-017-0921-5

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Nesamony Memorial Christian College affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, Tamil Nadu, 627012, India

    I. Michael Revina & W. R. Sam Emmanuel

Authors
  1. I. Michael Revina
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. W. R. Sam Emmanuel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to I. Michael Revina.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Revina, I.M., Emmanuel, W.R.S. MDTP: a novel multi-directional triangles pattern for face expression recognition. Multimed Tools Appl 78, 26223–26238 (2019). https://doi.org/10.1007/s11042-019-7711-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-7711-4

Keywords

Search

Navigation