Skip to main content
SpringerLink
Log in

Individual-free representation-based classification for facial expression recognition

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Facial expression recognition (FER) plays a significant role in human–computer interaction. However, in FER applications, the samples are usually corrupted by individual differences, which affect the classification result to some extent. This paper proposes an individual-free representation-based classification, which utilizes the variation training set (VTS) and the virtual variation training set (VVTS) to remit the side-effect caused by individual differences. The VTS and VVTS are both generated from the original training set and show possible variation of the expression. The new approach performs low-rank decomposition-based singular value decomposition for both VTS and VVTS, and then integrates them to determine the label of the query sample. This promising performance is mainly attributed to the fact that VTS and VVTS used in the proposed method can exploit limited original training set to produce a large possible expression variation. Experimental results show that the proposed method can achieve better performance than most of the competitive FER methods, e.g., SVM, SRC, CRC, LRC and the method in Lee et al.

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. Li, X., Ruan, Q., An, G., Jin, Y., Zhao, R.: Multiple strategies to enhance automatic 3D facial expression recognition. Neurocomputing 161, 89–98 (2015)

    Article  Google Scholar 

  2. Happy, S.L., Routray, A.: Automatic facial expression recognition using features of salient facial patches. IEEE Trans. Affect. Comput. 6, 1–12 (2015)

    Article  Google Scholar 

  3. Shao, J., Gori, l, Wan, S., Aggarwal, J.K.: 3D dynamic facial expression recognition using low-resolution videos. Pattern Recognit. Lett. 65, 157–162 (2015)

    Article  Google Scholar 

  4. Tariq, U., Thomas, J., Huang, T.S.: Supervised super-vector encoding for facial expression recognition. Pattern Recognit. Lett. 46, 89–95 (2014)

    Article  Google Scholar 

  5. Maronidis, A., Bolis, D., Tefas, A., Pitas, I.: Improving subspace learning for facial expression recognition using person dependent and geometrically enriched training sets. Neural Netw. 24, 814–823 (2011)

    Article  Google Scholar 

  6. Yurtkan, K., Demirel, H.: Feature selection for improved 3D facial expression recognition. Pattern Recognit. Lett. 38, 26–33 (2014)

    Article  Google Scholar 

  7. Fukushima, K.: Increased Robustness Against Background Noise: Pattern Recognition by a Neocognitron, vol. 6444, pp. 574–581. Springer, Berlin (2010)

    Google Scholar 

  8. Luo, Y., Wu, C.M., Zhang, Y.: Facial expression recognition based on fusion feature of PCA and LBP with SVM. Optik 124, 2767–2770 (2013)

    Article  Google Scholar 

  9. Siddiqi, M.H., Khan, A.M., Park, Y., 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 

  10. Wang, Z., Ying, Z.: Facial expression recognition based on local phase quantization and sparse representation. In: IEEE International Conference on Computer Science and Automation Engineering, vol. 2, pp. 440–444 (2012)

  11. Yan, Q., Nong, S., Rui, H.: Robust automatic facial expression detection method based on sparse representation plus LBP map. Optik 124, 6827–6833 (2013)

    Article  Google Scholar 

  12. Ruan, J., Yin, J., Chen, Q., Chen, G.: Facial expression recognition based on Gabor wavelet transform and relevance vector machine. J. Inf. Comput. Sci. 11, 295–302 (2014)

    Article  Google Scholar 

  13. Wang, Q.W., Ying, Z.L.: Facial expression recognition algorithm based on Gabor texture features and Adaboost feature selection via sparse representation. Appl. Mech. Mater. Sens. Mechatron. Autom. 511, 433–436 (2014)

    Google Scholar 

  14. Liu, W., Song, C., Wang, Y., Jia, L.: Facial expression recognition based on gabor features and sparse representation. In: International Conference on International Conference on Control, vol. 43, pp. 1402–1406 (2012)

  15. Wang, X., Ruan, Q., Jin, Y., An, G.: Expression robust three-dimensional face recognition based on Gaussian filter and dual-tree complex wavelet transform. J. Intell. Fuzzy Syst. 26, 193–201 (2014)

    Google Scholar 

  16. Lee, S.H., Plataniotis, K.N., Ro, Y.M.: Intra-class variation reduction using training expression images for sparse representation based facial expression recognition. IEEE Trans. Affect. Comput. 5, 340–351 (2014)

  17. Li, L., Ying, Z., Yang, T.: Facial expression recognition by fusion of gabor texture features and local phase quantization. In: International Conference on Signal Processing Proceedings, vol. 1, pp. 1781–1784 (2015)

  18. Zhong, S., Chen, Y., Liu, S.: Face expression using local feature selection and the extended nearest neighbor algorithm. In: International Symposium on Computational Intelligence and Design, vol. 1, pp. 328–331 (2014)

  19. Mery, D., Bowyer, K.: Recognition of Facial Attributes Using Adaptive Sparse Representation of Random Patches. Lecture Notes in Computer Science, vol. 8926, pp. 778–792 (2015)

  20. Ma, D., Li, M., Nian, F., Kong, C.: Facial expression recognition based on characteristics of block LGBP and sparse representation. J. Comput. Methods Sci. Eng. 15, 537–547 (2015)

    Article  Google Scholar 

  21. Li, W., Du, Q., Xiong, M.: Kernel collaborative representation with tikhonov regularization for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 12, 48–52 (2015)

    Article  Google Scholar 

  22. Zhuo, L., Geng, Z., Zhang, J., Li, X.: ORB feature based web pornographic image recognition. Neurocomputing 173, 511–517 (2016)

    Article  Google Scholar 

  23. Koc, M., Barkana, A.: Application of linear regression classification to low-dimensional datasets. Neurocomputing 131, 331–335 (2014)

    Article  Google Scholar 

  24. Xu, Y., Zhang, Z., Lu, G., Yang, J.: Approximately symmetrical face images for image preprocessing in face recognition and sparse representation based classification. Pattern Recognit. 54, 68–82 (2016)

    Article  Google Scholar 

  25. Wang, S.Z.: Sparse matrix of image denoising method based on SVD. Int. J. Multimedia Ubiquitous Eng. 9, 227–236 (2014)

    Article  Google Scholar 

  26. Waqas, J., Zhang, Y., Lei, Z.: Collaborative neighbor representation based classification using l2-minimization approach. Pattern Recognit. Lett. 34, 201–208 (2013)

    Article  Google Scholar 

  27. Liu, W., Lu, L., Li, H., Wang, W., Zou, Y.: A novel kernel collaborative representation approach for image classification. In: IEEE International Conference on Image Processing, pp. 4241–4245 (2014)

  28. Lyons, M., Akamatsu, S., Kamachi, M., Gyoba, J.: Coding facial expressions with Gabor wavelets. In: IEEE International Conference on Automatic Face and Gesture Recognition, pp. 200–205 (1998)

  29. Goeleven, E., De, Raedt R., Leyman, L., Verschuere, B.: The Karolinska directed emotional faces: a validation study. Cognit. Emot. 22, 1094–1118 (2008)

    Article  Google Scholar 

  30. Tian, Y.: Evaluation of face resolution for expression analysis. In: Conference on Computer Vision and Pattern Recognition Workshop, pp. 82–82 (2004)

Download references

Acknowledgments

This work is supported by National Natural Science Foundation of China under Grant No. 61071199, Natural Science Foundation of Hebei Province of China under Grant No. F2016203422, Postgraduate Innovation Project of Hebei (China) under Grant No. 00302-6370011.

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Yanshan University, Qinhuangdao, China

    Zhe Sun, Zheng-ping Hu, Meng Wang & Shu-huan Zhao

  2. School of Physics and Electronic Engineering, Taishan University, Tai’an, China

    Meng Wang

Authors
  1. Zhe Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zheng-ping Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Meng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shu-huan Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zheng-ping Hu.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this paper.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, Z., Hu, Zp., Wang, M. et al. Individual-free representation-based classification for facial expression recognition. SIViP 11, 597–604 (2017). https://doi.org/10.1007/s11760-016-0999-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-016-0999-x

Keywords

Search

Navigation