Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

A novel multi-feature fusion deep neural network using HOG and VGG-Face for facial expression classification

  • Special Issue Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

Facial expressions are a prevalent way to recognize human emotions, and automatic facial expression recognition (FER) has been a significant task in cognitive science, artificial intelligence, and computer vision. The critical issue with the design of the FER model is the strong intra-class correlation of different emotions. The accuracy of the FER model is reduced due to other problems such as the variations in expressing the emotions, variations in lighting, and different ethnic biases. The latest convolutional neural network-based FER models have shown significant improvement in accuracy score but lack distinguishing the micro-expressions. This paper proposed a multi-input hybrid FER model that considers both hand-engineered and self-learnt features to classify facial expressions. The VGG-Face and the histogram of oriented gradients (HOG) features are derived from the faces to distinguish various facial expression patterns. The fusion of deep (VGG-Face) and hand-engineered (HOG) features has shown improved accuracy compared to the conventional CNN models. The results obtained showed that the proposed model’s accuracy scores outperformed the accuracy scores of the other popular FER models on three facial expression datasets. Extended Cohn–Kanade (CK\(+\)), Yale-Face, and Karolinska directed emotional faces (KDEF) datasets are used to determine the model’s classification efficiency. The proposed model scored 98.12%, 95.26%, and 96.36% accuracy using a fivefold cross-validation process on the CK\(+\), Yale-Face and KDEF datasets.

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
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Mehrabian, A.: Nonverbal Communication. Routledge, London (2017)

    Book  Google Scholar 

  2. 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: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops. IEEE, pp. 94–101 (2010)

  3. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)

    Article  Google Scholar 

  4. Lundqvist, D., Flykt, A., Öhman, A.: Karolinska directed emotional faces. Cogn, Emot (1998)

    Google Scholar 

  5. Tang, Y., Zhang, X.M., Wang, H.: Geometric-convolutional feature fusion based on learning propagation for facial expression recognition. IEEE Access 6, 42532–42540 (2018)

    Article  Google Scholar 

  6. Wang, Y., Li, M., Zhang, C., Chen, H., Lu, Y.: Weighted-fusion feature of MB-LBPUH and HOG for facial expression recognition. Soft. Comput. 24(8), 5859–5875 (2020)

    Article  Google Scholar 

  7. Wang, X., Jin, C., Liu, W., Hu, M., Xu, L., Ren, F.: Feature fusion of HOG and WLD for facial expression recognition. In: Proceedings of the 2013 IEEE/SICE International Symposium on System Integration, pp. 227–232. IEEE (2013)

  8. Xie, X., Lam, K.M.: Facial expression recognition based on shape and texture. Pattern Recogn. 42(5), 1003–1011 (2009)

    Article  Google Scholar 

  9. Lin, D.T., Pan, D.C.: Integrating a mixed-feature model and multiclass support vector machine for facial expression recognition. Integr. Comput. Aid. Eng. 16(1), 61–74 (2009)

    Article  Google Scholar 

  10. Reddy, G.V., Savarni, C.D., Mukherjee, S.: Facial expression recognition in the wild, by fusion of deep learnt and hand-crafted features. Cogn. Syst. Res. 62, 23–34 (2020)

    Article  Google Scholar 

  11. Pan, X.: Fusing HOG and convolutional neural network spatial-temporal features for video-based facial expression recognition. IET Image Proc. 14(1), 176–182 (2020)

    Article  Google Scholar 

  12. Breuer, R., Kimmel, R.: A deep learning perspective on the origin of facial expressions. arXiv preprint, arXiv:1705.01842

  13. Jung, H., Lee, S., Yim, J., Park, S., Kim, J.: Joint fine-tuning in deep neural networks for facial expression recognition. In: Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 2983–2991

  14. Zhao, K., Chu, W.-S., Zhang, H.: Deep region and multi-label learning for facial action unit detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3391–3399 (2016)

  15. Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)

  16. Kahou, S.E., Pal, C., Bouthillier, X., Froumenty, P., Gülçehre, Ç., Memisevic, R., Vincent, P., Courville, A., Bengio, Y., Ferrari, R.C., Mirza, M.: Combining modality specific deep neural networks for emotion recognition in video. In: Proceedings of the 15th ACM on International Conference on Multimodal Interaction, pp. 543–550 (2013)

  17. Koc, M., Ergin, S., Gülmezoğlu, M.B., Edizkan, R., Barkana, A.: Use of gradient and normal vectors for face recognition. IET Image Proc. 14(10), 2121–2129 (2020)

    Article  Google Scholar 

  18. Liu, P., Han, S., Meng, Z., Tong, Y.: Facial expression recognition via a boosted deep belief network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1805–1812 (2014)

  19. Liu, P., Han, S., Meng, Z., Tong, Y.: Facial expression recognition via deep learning. In: IEEE International Conference on Smart Computing, pp. 303–308 (2014)

  20. Mollahosseini, A., Chan, D., Mahoor, M.H.: Going deeper in facial expression recognition using deep neural networks. In: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1–10 (2016)

  21. Khorrami, P., Paine, T.L., Huang, T.S.: Do deep neural networks learn facial action units when doing expression recognition? In: 2015 IEEE International Conference on Computer Vision Workshop (ICCVW), pp. 19–27 (2015)

  22. Zhang, K., Huang, Y., Du, Y., Wang, L.: Facial expression recognition based on deep evolutional spatial-temporal networks. IEEE Trans. Image Process. 26, 4193–4203 (2017)

    Article  MathSciNet  Google Scholar 

  23. Kurup, A.R., Ajith, M., Ramón, M.M.: Semi-supervised facial expression recognition using reduced spatial features and deep belief networks. Neurocomputing 367, 188–197 (2019)

    Article  Google Scholar 

  24. Datta, S., Sen, D., Balasubramanian, R.: Integrating geometric and textural features for facial emotion classification using SVM frameworks. In: Proceedings of International Conference on Computer Vision and Image Processing, pp. 619–628 (2017)

  25. Cai, J., Meng, Z., Khan, A.S., Li, Z., O’Reilly, J., Tong, Y.: Island loss for learning discriminative features in facial expression recognition 13th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 302–309 (2018)

  26. Kim, B. K., Dong, S. Y., Roh, J., Kim, G., Lee, S.-Y.: Fusing aligned and non-aligned face information for automatic affect recognition in the wild: a deep learning approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 48–57 (2016)

  27. Zia, M.S., Hussain, M., Jaffar, M.A.: A novel spontaneous facial expression recognition using dynamically weighted majority voting based ensemble classifier. Multimed. Tools Appl. 77, 25537–25567 (2018)

    Article  Google Scholar 

  28. Cotter, S.F.: Weighted voting of sparse representation classifiers for facial expression recognition. In: IEEE 18th European Signal Processing Conference, pp. 1164–1168 (2010)

  29. Dalal, N., Triggs, B., Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893. IEEE (2005)

  30. Carcagnì, P., Del Coco, M., Leo, M., Distante, C.: Facial expression recognition and histograms of oriented gradients: a comprehensive study. SpringerPlus 4(1), 1–25 (2015)

    Article  Google Scholar 

  31. Da, B., Sang, N.: Local binary pattern based face recognition by estimation of facial distinctive information distribution. Opt. Eng. 48(11), 117203 (2009)

    Article  Google Scholar 

  32. Chen, J., Shan, S., He, C., Zhao, G., Pietikäinen, M., Chen, X., Gao, W.: Wld: a robust local image descriptor. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1705–1720 (2009)

    Article  Google Scholar 

  33. Ullah, I., Hussain, M., Muhammad, G., Aboalsamh, H., Bebis, G., Mirza, A.M.: Gender recognition from face images with local WLD descriptor. In: 19th International Conference on Systems, Signals and Image Processing (IWSSIP), pp. 417–420. IEEE (2012)

  34. Ahmed, F., Hossain, E., Bari, A.H., Shihavuddin, A.S.M.: Compound local binary pattern (CLBP) for robust facial expression recognition. In: 2011 IEEE 12th International Symposium on Computational Intelligence and Informatics (CINTI), pp. 391–395 (2011)

  35. Chen, J., Chen, Z., Chi, Z., Fu, H., et al.: Facial expression recognition based on facial components detection and hog features. In: International Workshops on Electrical and Computer Engineering Subfields, pp. 884–888 (2014)

  36. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25, 1097–1105 (2012)

    Google Scholar 

  37. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  38. Parkhi, O.M., Vedaldi, A., Zisserman, A.: Visual Geometry Group, University of Oxford. https://www.robots.ox.ac.uk/~vgg/software/vgg_face/ (2015)

  39. Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database forstudying face recognition in unconstrained environments. In: Workshop on Faces in ‘Real-Life’ images: detection, alignment, and recognition (2008)

  40. Wolf, L., Hassner, T., Maoz, I.: Face recognition in unconstrained videos with matched background similarity. In: CVPR 2011, pp. 529–534. IEEE (2011)

  41. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)

  42. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

  43. Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., ... Adam, H.: Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)

  44. Zavarez, M.V., Berriel, R.F., Oliveira-Santos, T.: Cross-database facial expression recognition based on fine-tuned deep convolutional network. In: 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 405–412 (2017)

  45. Pantic, M., Valstar, M., Rademaker, R., Maat, L.: Web-based database for facial expression analysis. In: 2005 IEEE International Conference on Multimedia and Expo, p. 5 pp. IEEE (2005)

  46. Langner, O., Dotsch, R., Bijlstra, G., Wigboldus, D.H., Hawk, S.T., Van Knippenberg, A.D.: Presentation and validation of the Radboud faces database. Cogn. Emot. 24(8), 1377–1388 (2010)

    Article  Google Scholar 

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

  48. Martinez, A., Benavente, R.: The AR face database, CVC. Copyright of Informatica (03505596) (1998)

  49. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, vol. 1, pp. I-I. IEEE (2001)

  50. Serengil, S.I.: https://sefiks.com/2019/07/15/how-to-convert-matlab-models-to-keras/

  51. Ekman, P., Friesen, W., Hager, J.: Facial Action Coding System: Research Nexus. Network Research Information, Salt Lake City (2002)

    Google Scholar 

  52. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  53. Tieleman, T., Hinton, G.: Lecture 6.5-rmsprop, coursera: neural networks for machine learning. University of Toronto, Technical Report (2012)

  54. Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12(7), 2121–2159 (2011)

    MathSciNet  MATH  Google Scholar 

  55. Platt, J.C., Cristianini, N., Shawe-Taylor, J.: Large margin DAGs for multiclass classification. NIPS 12, 547–553 (1999)

    Google Scholar 

  56. Shan, C., Gong, S., McOwan, P.W.:Robust facial expression recognition using local binary patterns. In: IEEE International Conference on Image Processing 2005, vol. 2, pp. II-370. IEEE (2005)

  57. Friedman, J.H.: Another approach to polychotomous classification. Technical Report, Statistics Department, Stanford University (1996)

  58. Xie, S., Hu, H.: Facial expression recognition using hierarchical features with deep comprehensive multipatches aggregation convolutional neural networks. IEEE Trans. Multimed. 21(1), 211–220 (2018)

    Article  MathSciNet  Google Scholar 

  59. Nwosu, L., Wang, H., Lu, J., Unwala, I., Yang, X., Zhang, T., Deep convolutional neural network for facial expression recognition using facial parts. In: IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, 15th International Conference on Pervasive Intelligence and Computing, 3rd International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), pp. 1318–1321. IEEE (2017)

  60. Ravi, R., Yadhukrishna, S.V., Prithviraj, R.: A face expression recognition using CNN and LBP. In: Proceedings 4th International Conference on Computing Methodologies and Communication (ICCMC), pp. 684–689 (2020)

  61. Alshamsi, H., Kepuska, V., Meng, H.: Real time automated facial expression recognition app development on smart phones. In: 8th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 384–392 (2017)

  62. Koujan, M.R., Alharbawee, L., Giannakakis, G., Pugeault, N., Roussos: Real-time facial expression recognition in the wild by disentangling 3D expression from identity. arXiv preprint arXiv:2005.05509 (2020)

  63. Melaugh, R., Siddique, N., Coleman, S., Yogarajah, P.: Facial expression recognition on partial facial sections. In: 11th International Symposium on Image and Signal Processing and Analysis, pp. 193–197 (2019)

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication, National Institute of Technology, Warangal, Telangana, India

    Alagesan Bhuvaneswari Ahadit & Ravi Kumar Jatoth

Authors
  1. Alagesan Bhuvaneswari Ahadit
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ravi Kumar Jatoth
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alagesan Bhuvaneswari Ahadit.

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

Ahadit, A.B., Jatoth, R.K. A novel multi-feature fusion deep neural network using HOG and VGG-Face for facial expression classification. Machine Vision and Applications 33, 55 (2022). https://doi.org/10.1007/s00138-022-01304-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00138-022-01304-y

Keywords

Search

Navigation