Skip to main content
Springer Nature Link
Log in

Multi-Featured and Fuzzy-Filtered Machine Learning Model for Face Expression Classification

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Face expression is the appearance-based descriptive information, which is used for recognizing the emotion, behavior, and intention of an individual. In this paper, the content and textural information are utilized for the identification of facial-expression. For exploring the content features, the Gabor filter is applied to normalized face-image. The face and Gabor-face images are divided into smaller blocks. For each block, content, structure, and texture-sensitive quantitative features are extracted. This stage transformed the image into the wider content-adaptive quantitative featureset. The fuzzy-based composite filter is applied to this larger featureset for the identification of the most relevant featureset. SVM classifier with different kernels is applied to this reduced-featureset for accurate recognition of expression. The experimental validation is conducted on JAFFE and CK+ datasets. Analytical observations are collected using accuracy, sensitivity, specificity, FNR, and FPR parameters. The experimentation results show that the proposed model outperformed the state-or-art methods and achieved a significant recognition rate.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others

References

  1. Michael Revina, I., & Sam Emmanuel, W. R. (2018). A survey on human face expression recognition techniques. Journal of King Saud University—Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2018.09.002.

    Article  Google Scholar 

  2. Zhang, H., et al. (2016). Face-selective regions differ in their ability to classify facial expressions. Neuroimage, 130, 77–90.

    Google Scholar 

  3. Martinez, A. M. (2003). Matching expression variant faces. Vision Research, 43(9), 1047–1060.

    Google Scholar 

  4. Khan, S. A., Hussain, A., & Usman, M. (2016). Facial expression recognition on real world face images using intelligent techniques: A survey. Optik, 127(15), 6195–6203.

    Google Scholar 

  5. Alugupally, N., Samal, A., Marx, D., & Bhatia, S. (2011). Analysis of landmarks in recognition of face expressions. Pattern Recognition and Image Analysis, 21(4), 681–693.

    Google Scholar 

  6. Somia Saeed, M., Mahmood, K., & Khan, Y. D. (2018). An exposition of facial expression recognition techniques. Neural Computing and Applications, 29(9), 425–443.

    Google Scholar 

  7. Lajevardi, S. M., & Hussain, Z. M. (2012). Automatic facial expression recognition: Feature extraction and selection. Signal, Image and Video Processing, 6(1), 159–169.

    Google Scholar 

  8. Juneja, K. (2017). MPMFFT based DCA-DBT integrated probabilistic model for face expression classification. Journal of King Saud University—Computer and Information Sciences, 32(5), 618–633.

    Google Scholar 

  9. Li, K., Jin, Y., Akram, M. W., Han, R., & Chen, J. (2019). Facial expression recognition with convolutional neural networks via a new face cropping and rotation strategy. The Visual Computer, 36, 391–404.

    Google Scholar 

  10. Kar, N. B., Babu, K. S., Sangaiah, A. K., & Bakshi, S. (2017). Face expression recognition system based on ripplet transform type II and least square SVM. Multimedia Tools and Applications, 78, 4789–4812.

    Google Scholar 

  11. Michael Revina, I., & Sam Emmanuel, W. R. (2018). Face expression recognition using LDN and dominant gradient local ternary pattern descriptors. Journal of King Saud University—Computer and Information Sciences.. https://doi.org/10.1016/j.jksuci.2018.03.015.

    Article  Google Scholar 

  12. Hsieh, C.-C., Hsih, M.-H., Jiang, M.-K., Cheng, Y.-M., & Liang, E.-H. (2016). Effective semantic features for facial expressions recognition using SVM. Multimedia Tools and Applications, 75(11), 6663–6682.

    Google Scholar 

  13. Taleb, I., Mammar, M. O., & Ouamri, A. (2018). New face expression recognition using polar angular radial transform and principal component analysis. International Journal of Biometrics, 10(2), 176–194.

    Google Scholar 

  14. De, A., Saha, A., & Pal, M. C. (2015). A human facial expression recognition model based on eigen face approach. Procedia Computer Science, 45, 282–289.

    Google Scholar 

  15. Ghimire, D., Jeong, S., Lee, J., & Park, S. H. (2017). Facial expression recognition based on local region specific features and support vector machines. Multimedia Tools and Applications, 76(6), 7803–7821.

    Google Scholar 

  16. Khan, S. A., Hussain, A., & Usman, M. (2018). Reliable facial expression recognition for multi-scale images using weber local binary image based cosine transform features. Multimedia Tools and Applications, 77(1), 1133–1165.

    Google Scholar 

  17. Cruz, E. A. S., Jung, C. R., & Franco, C. H. E. (2018). Facial expression recognition using temporal POEM features. Pattern Recognition Letters, 114, 13–21.

    Google Scholar 

  18. Barman, A., & Dutta, P. (2019). Facial expression recognition using distance and texture signature relevant features. Applied Soft Computing, 77, 88–105.

    Google Scholar 

  19. Farkhod Makhmudkhujaev, M., Abdullah-Al-Wadud, Iqbal, M. T. B., Ryu, B., & Chae, O. (2019). Facial expression recognition with local prominent directional pattern. Signal Processing: Image Communication, 74, 1–12.

    Google Scholar 

  20. Nigam, S., Singh, R., & Misra, A. K. (2018). Efficient facial expression recognition using histogram of oriented gradients in wavelet domain. Multimedia Tools and Applications, 77(21), 28725–28747.

    Google Scholar 

  21. Abdallah, T. B., Guermazi, R., & Hammami, M. (2018). Facial-expression recognition based on a low-dimensional temporal feature space. Multimedia Tools and Applications, 77(15), 19455–19479.

    Google Scholar 

  22. Agarwal, S., & Mukherjee, D. P. (2017). Facial expression recognition through adaptive learning of local motion descriptor. Multimedia Tools and Applications, 76(1), 1073–1099.

    Google Scholar 

  23. Agarwal, S., Santra, B., & Mukherjee, D. P. (2018). Anubhav: Recognizing emotions through facial expression. The Visual Computer, 34(2), 177–191.

    Google Scholar 

  24. Wang, S., Shan, W., Gao, Z., & Ji, Q. (2016). Facial expression recognition through modeling age-related spatial patterns. Multimedia Tools and Applications, 75(7), 3937–3954.

    Google Scholar 

  25. Huang, Y., Yan, Y., Chen, S., & Wang, H. (2018). Expression-targeted feature learning for effective facial expression recognition. Journal of Visual Communication and Image Representation, 55, 677–687.

    Google Scholar 

  26. Zhang, F., Yongbin, Yu., Mao, Q., Gou, J., & Zhan, Y. (2016). Pose-robust feature learning for facial expression recognition. Frontiers of Computer Science, 10(5), 832–844.

    Google Scholar 

  27. Kumano, S., Otsuka, K., Yamato, J., Maeda, E., & Sato, Y. (2009). Pose-invariant facial expression recognition using variable-intensity templates. International Journal of Computer Vision, 83(2), 178–194.

    Google Scholar 

  28. Saha, P., Bhowmik, M. K., Bhattacharjee, D., De Kumar, B., & Nasipuri, M. (2016). Expressions recognition of North-East Indian (NEI) faces. Multimedia Tools and Applications, 75(24), 16781–16807.

    Google Scholar 

  29. Uddin, M. Z. (2014). An efficient local feature-based facial expression recognition system. Arabian Journal for Science and Engineering, 39(11), 7885–7893.

    Google Scholar 

  30. Chen, J., & Liu, R. X. (2018). Deep peak-neutral difference feature for facial expression recognition. Multimedia Tools and Applications, 77(22), 29871–29887.

    Google Scholar 

  31. Ji, Y., Hu, Y., Yang, Y., Shen, F., & Shen, H. T. (2019). Cross-domain facial expression recognition via an intra-category common feature and inter-category distinction feature fusion network. Neurocomputing, 333, 231–239.

    Google Scholar 

  32. Siddiqi, M. H., et al. (2015). Facial expression recognition using active contour-based face detection, facial movement-based feature extraction, and nonlinear feature selection. Multimedia Systems, 21(6), 541–555.

    Google Scholar 

  33. Tsai, H.-H., & Chang, Y.-C. (2018). Facial expression recognition using a combination of multiple facial features and support vector machine. Soft Computing, 22(13), 4389–4405.

    Google Scholar 

  34. Kim, D. J. (2016). Facial expression recognition using ASM-based post-processing technique. Pattern Recognition and Image Analysis, 26(3), 576–581.

    Google Scholar 

  35. Sujono, & Gunawan, A. A. S. (2015). Face expression detection on kinect using active appearance model and fuzzy logic. Procedia Computer Science, 59, 268–274.

    Google Scholar 

  36. Shah, J. H., Sharif, M., Yasmin, M., & Fernandes, S. L. (2017). Facial expressions classification and false label reduction using LDA and threefold SVM. Pattern Recognition Letters. https://doi.org/10.1016/j.patrec.2017.06.021.

    Article  Google Scholar 

  37. Luo, Y., Zhang, L., Chen, Y., & Jiang, W. (2017). Facial expression recognition algorithm based on reverse co-salient regions (RCSR) features. In 4th international conference on information science and control engineering (ICISCE), Changsha (pp. 326–329).

  38. Juneja, K. (2017). Ring segmented and block analysis based multi-feature evaluation model for contrast balancing. In International conference on information, communication and computing technology (pp. 181–193).

  39. Juneja, K. (2017). Multiple feature descriptors based model for individual identification in group photos. Journal of King Saud University—Computer and Information Sciences, 31(2), 185–207.

    Google Scholar 

  40. Dinakaran, S., Ranjit, P., & Thangaiah, J. (2013). Role of attribute selection in classification algorithms. International Journal of Scientific & Engineering Research, 4(6), 67–71.

    Google Scholar 

  41. Praveena Priyadarsini, R., Valarmathi, M. L., & Sivakumari, S. (2011). Gain ratio based feature selection method for privacy preservation. ICTACT Journal of Soft Computing, 1(4), 201–205.

    MATH  Google Scholar 

  42. Cigdem, O., & Demirel, H. (2018). Performance analysis of different classification algorithms using different feature selection methods on Parkinson’s disease detection. Journal of Neuroscience Methods, 309, 81–90.

    Google Scholar 

  43. Cilia, N. D., De Stefano, C., Fontanella, F., & di Freca, A. S. (2018). A ranking-based feature selection approach for handwritten character recognition. Pattern Recognition Letters, 121, 77–86.

    Google Scholar 

  44. Lyons, M. J., Akamatsu, S., Kamachi, M., & Gyoba, J. (1998). Coding facial expressions with gabor wavelets. In Third IEEE international conference on automatic face and gesture recognition, Jara, Japan (pp. 200–205).

  45. Kanade, T., Cohn, J. F., & Tian, Y. (2000) Comprehensive database for facial expression analysis. In Fourth IEEE international conference on automatic face and gesture recognition, Grenoble (pp. 46–53).

  46. Lucey, P., et al. (2010). The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression. In: IEEE computer society conference on computer vision and pattern recognition—Workshops, San Francisco (pp. 94–101).

  47. Zhang, L., & Tjondronegoro, D. (2011). Facial expression recognition using facial movement features. IEEE Transactions on Affective Computing, 2(4), 219–229.

    Google Scholar 

  48. Mlakar, U., & Potocnik, B. (2015). Automated facial expression recognition based on histograms of oriented gradient feature vector differences. Signal, Image and Video Processing, 9, 245–253.

    Google Scholar 

  49. Kazmi, S. B., & Jaffar, M. A. (2012). Wavelets-based facial expression recognition using a bank of support vector machines. Soft Computing, 16(3), 369–379.

    Google Scholar 

  50. Guo, M., Hou, X., Ma, Y., & Wu, X. (2016). Facial expression recognition using ELBP based on covariance matrix transform in KLT. Multimedia Tools and Applications, 76, 2995–3010.

    Google Scholar 

  51. Shan, C., Gong, S., & Mcowan, P. W. (2009). Facial expression recognition based on local binary patterns: A comprehensive study. Image Visual Computing, 27(6), 803–816.

    Google Scholar 

  52. De la Torre, F., et al. (2015). Intraface. In IEEE international conference on automatic face and gesture recognition (pp. 1–8).

Download references

Author information

Author notes

  1. Kapil Juneja

    Present address: , 307, Sector 14, Rothak, 124001, Haryana, India

Authors and Affiliations

  1. Department of Computer Science and Engineering, University Institute of Engineering and Technology, Maharshi Dayanand University, Rohtak, Haryana, India

    Kapil Juneja & Chhavi Rana

Authors
  1. Kapil Juneja
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Chhavi Rana
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Kapil Juneja.

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

Juneja, K., Rana, C. Multi-Featured and Fuzzy-Filtered Machine Learning Model for Face Expression Classification. Wireless Pers Commun 115, 1227–1256 (2020). https://doi.org/10.1007/s11277-020-07620-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-020-07620-8

Keywords