Skip to main content
Log in

Facial expression recognition with dynamic cascaded classifier

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In this paper, a new approach for facial expression recognition has been proposed. The approach has imbedded a new feature extraction technique, new multiclass classification approach and a new kernel parameter optimization for support vector machines. The scheme of the approach begins with feature extraction from the input vectors, and the extracted features are transformed into a Gaussian space using compressive sensing techniques. This process ensures feature vector dimensionality reduction and matches the features vectors with radial basis function kernel used in support vector machines for classification. Prior to classification, an optimized parameter for support vector machines training is automatically determined based on an approach proposed which relies on the receiver operating characteristics of the support vector machine classifier. With the optimized kernel parameter, new proposed multiclass classification approach is used to finally classify any vector. In all the experiments conducted on the two facial expression databases with different cross-validation techniques, the proposed approach outperforms its counterparts under the same database and settings. The results further confirmed the validity and advantages of the proposed approach over other approaches currently used in the literature.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Fasel B, Juergen L (2003) Automatic facial expression analysis. J Pattern Recognit Soc 36:259–275

    MATH  Google Scholar 

  2. Minand T, Feng C (2013) Facial expression recognition and its application based on curvelet transform and PSO_SVM. Int J Light Electron Opt 124:5401–5406

    Google Scholar 

  3. Wenfei G, Cheng X, Venkatesh YV, Dong H, Hai L (2012) Facial expression recognition using radial encoding of local gabor features and classifier synthesis. J Pattern Recognit Soc 45:80–91

    Google Scholar 

  4. Shiqing Z, Lemin L, Zhijin Z (2012) Facial expression recognition based on gabor wavelets and sparse representation. In: Proceedings of ICSP, pp 816–819

  5. Michael JL, Shigeru A, Miyuki K, Jiro G (1998) Coding facial expressions with gabor wavelets. In: Proceedings of AFGR, pp 200–205

  6. Shishir B, Ganesh KV (2008) Recognition of facial expressions using Gabor wavelets and learning vector quantization. J Eng Appl Artif Intell 21:1056–1064

    Google Scholar 

  7. Baochang Z, Shiguang S (2007) Histogram of Gabor Phase Patterns (HGPP): a novel object representation approach for face recognition. In: Tran. IP, pp 57–68

  8. Gentile C, Li S, Kar P, Karatzoglou A, Zappella G, Etrue E (2017) On context-dependent clustering of bandits. In: Proceedings of international conference on machine learning, PMLR, pp 1253–1262

  9. Li S., Karatzoglou A, Gentile C (2016) Collaborative filtering bandits. In: Proceedings of international conference on research and development in information retrieval, SIGIR, pp 539–548

  10. Korda N, Szörényi B, Li S (2016) Distributed clustering of linear bandits in peer topeer networks. In: Proceedings of international conference on machine learning, ICML, pp 1301–1309

  11. Yimo G, Zhengguang X (2008) Local Gabor phase difference pattern for face recognition. In: Proceedings of ICPR, pp 1–4

  12. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3:71–86

    Google Scholar 

  13. Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Proceedings of COLT’92, pp 144–152

  14. Chapelle O, Vapnik V, Bousquet O, Mukherjee S (2002) Choosing multiple parameters for support vector machines. J Mach Learn 46:131–159

    MATH  Google Scholar 

  15. Vapnik V (1991) Principles of risk minimization for learning theory. In: Proceedings of NIPS, pp 832–838

  16. Vapnik V (1998) Statistical learning theory. Wiley, New York

    MATH  Google Scholar 

  17. Hsu C, Lin C (2002) A comparison of methods for multi-class support vector machines. IEEE Trans Neural Netw 13:415–425

    Google Scholar 

  18. Wang Z, Xu W, Hu J, Guo J (2010) A multiclass SVM method via probabilistic error-correcting output codes. In: Proceedings of ITA, pp 1–4

  19. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. In: Tran TPAMI, pp 711–720

  20. Lowe D (1989) Adaptive radial basis function nonlinearities, and the problem of generalisation. In: Proceedings of ANN, pp 171–175

  21. Knerr S, Personnaz L, Dreyfus G (1990) Single-layer learning revisited: a stepwise procedure for building and training a neural network. In: Fogelman-Soulie F, Herault J (eds) Neurocomputing: algorithms, architectures and applications, NATO ASI. Springer, Berlin

    Google Scholar 

  22. Friedman JH (1996) Another approach to polychotomous classification. Technical report, Stanford Department of Statistics. https://ci.nii.ac.jp/naid/10017594776/

  23. Platt JC, Cristianini N, Shawe-Taylor J (1999) Large margin DAGs for multiclass classification. http://papers.nips.cc/paper/1773-large-margin-dags-for-multiclass-classification.pdf

  24. Dietterich TG, Bakiri G (1995) Solving multiclass learning problems via error-correcting output codes. J Artif Intell Res 2:263–286

    MATH  Google Scholar 

  25. Broomhead DS, Lowe D (1988) Radial basis functions, multi-variable functional interpolation and adaptive networks. Royal Signals And Radar Establishment Malvern (United Kingdom)

  26. Shannon CE (1949) Communication in the presence of noise. In: Proceedings of IRE, vol 37, pp 10–21

  27. Shannon CE (1948) A Mathematical theory of communication. Bell Syst Tech J 27:379–423

    MathSciNet  MATH  Google Scholar 

  28. Emamnuel CJ (2006) Compressive sampling. In: Proceedings of ICM, vol 3, pp 1433–1452

  29. Baraniuk RG (2007) Compressed Sensing [Lecture Notes]. In: Proceedings of SPM, vol 24, pp. 118–124

  30. Eleyan A, Kose K, Cetin E (2013) Image feature extraction using compressive sensing. In: Proceedings of AISC, pp 177–184

  31. Ashir AM, Eleyan A (2017) Facial expression recognition based on image pyramid and single branch decision tree. In: Proceedings of SIViP, vol 9, no 1, pp 1–8

  32. Kanade T, Cohn JF, Tian Y (2000) Comprehensive database for facial expression analysis. In: Proceedings of FG2000-ICAFGR, Grenoble, France, pp 46–53

  33. Valstar M, Pantic M (2010) Induced disgust, happiness and surprise: an addition to the MMI facial expression database. In: Proceedings of international conference on language resources and evaluation, workshop on EMOTION, pp 65–70

  34. Lucey P, Cohn JF, Kanade T, Saragih J, Ambadar Z, Matthews I (2010) The Extended Cohn-Kanade dataset (CK+): a complete dataset for action unit and emotion-specified expression. In: Proceedings of IEEE computer society conference on computer vision and pattern recognition—workshops, pp 94–101

  35. Wei-Lu C, Jian-Jiun D, Jun-Zuo L (2015) Facial expression recognition based on improved local binary pattern and class-regularized locality preserving projection. Int J Signal Process 117:1–10

    Google Scholar 

  36. Ying Z, Fang X (2008) Combining LBP and Adaboost for facial expression recognition. In: Proceedings of ICSP, pp 1461–1464

  37. Guo G, Dyer R (2007) Facial expression recognition based on Gabor histogram feature and MVBoost. J Comput Res Dev 44:1089–1096

    Google Scholar 

  38. Huang MW, Wang ZW, Ying ZL (2010) A new method for facial expression recognition based on sparse representation plus LBP. In: Proceedings of ICISP, pp 1750–1754

  39. Cai L, Yin Z (2009) A new approach of facial expression recognition based on Contourlet transform. In: Proceedings of ICWAPR, pp 275–280

  40. Zavaschi T, Koerich A, Oliveira L (2011) Facial expression recognition using ensemble of classifiers. In: Proceedings of IC-ASSP, pp 1489–1492

  41. Shan C, Gong S (2011) Facial expression analysis across databases. In: Proceedings of MT, pp 317–320

  42. Zhang Z, Xu C, Wang JX, Chen XN (2012) Facial expression recognition based on MB-LGBP feature and multi-level classification. J Adv Intell Soft Comput 129:37–42

    Google Scholar 

  43. Moeini A, Faez K, Sadeghi H, Moeini H (2016) 2D facial expression recognition via 3D reconstruction and feature fusion. J Vis Commun Image R 35:1–14

    Google Scholar 

  44. Rivera AR, Castillo JR, Chae O (2013) Local directional number pattern for face analysis: face and expression recognition. IEEE Trans Image Process 22(5):1740–1752

    MathSciNet  MATH  Google Scholar 

  45. Liu S, Ruan Q, Wang C, An G (2012) Tensor rank one differential graph preserving analysis for facial expression recognition. Image Vis Comput 30:535–545

    Google Scholar 

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

    Google Scholar 

  47. Gu W, Xiang C, Venkatesh YV, Huang D, Lin H (2012) Facial expression recognition using radial encoding of local Gabor features and classifier synthesis. Pattern Recognit 45:80–91

    Google Scholar 

  48. Yeasin M, Bullot B, Sharma R (2004) From facial expression to level of interest: a spatio-temporal approach. In: Proceedings of CVPR, pp 922–927

  49. Aleksic PS, Katsaggelos AK (2006) Automatic facial expression recognition using facial animation parameters and multi-stream HMMS. In: Tran. IFS, pp 3–11

  50. Li ZS, Imai J, Kaneko M (2010) Facial expression recognition using facial-component-based bag of words and PHOG descriptors. In: Proceedings of IMT, pp 1003–1009

  51. Shan C, Gong S, McOwan PW (2005) Robust facial expression recognition using local binary patterns. In: Proceedings of ICIP, pp 370–373

  52. Zhao GY, Pietik M (2007) Dynamic texture recognition using local binary patterns with an application to facial expressions. In: Tran. PAMI, pp 915–928

  53. Bartlett MS, Littlewort G, Fasel I, Movellan R (2003) Real-time face detection and facial expression recognition: development and application to human computer interaction. In: Workshop on HCI-CVPR

  54. Littlewort G, Bartlett M, Fasel I, Susskind J, Movellan J (2004) Dynamics of facial expression extracted automatically from video. In: Workshop on face processing in video

  55. Tian Y (2004) Evaluation of face resolution for expression analysis. In: Workshop on face processing in video

  56. Rudovic O, Pavlovic V, Pantic M (2012) Multi-output Laplacian dynamic ordinal regression for facial expression recognition and intensity estimation. In: Proceedings of international conference on CVPR, pp 2634–2641

  57. Zhong L, Liu Q, Yang P, Liu B, Huang J, Metaxas DN (2012) Learning active facial patches for expression analysis. In: Proceedings of international conference on CVPR, pp 2634–2641

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Abubakar M. Ashir.

Ethics declarations

Conflict of interest

We wish to submit a new manuscript entitled “Facial Expression Recognition with Dynamic Cascaded Classifier” for consideration in the Neural Computing and Applications Journal. We confirm that this work is original and has not been published elsewhere nor is it currently under consideration for publication elsewhere and there is no conflict of interest whatsoever.

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

Ashir, A.M., Eleyan, A. & Akdemir, B. Facial expression recognition with dynamic cascaded classifier. Neural Comput & Applic 32, 6295–6309 (2020). https://doi.org/10.1007/s00521-019-04138-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-019-04138-4

Keywords

Navigation