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An Approach Toward Deep Learning-Based Facial Expression Recognition in Wavelet Domain

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Soft Computing and Signal Processing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1340))

Abstract

Facial expression recognition (FER) provides an effective way to recognize human emotions. Since, the facial expression shows the internal feelings of a person; therefore, it can be used to understand human behavior from image sequences. Wavelets and deep learning have been effectively and extensively used separately in many computer vision applications. Being motivated from wavelets and deep learning, we introduce an integration of convolutional neural network (CNN) and discrete wavelet transform (DWT) for FER. The proposed FER framework consists of three key modules, namely, face processing, feature representation using DWT and expression recognition using CNN. We have used benchmark CK+ dataset for experiments and evaluated the performance of the proposed method in terms of recognition accuracy. The developed framework has been compared with existing FER methods and shows its effectiveness with an accuracy of 98.73%.

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References

  1. P. Ekman, Facial expression and emotion. Am. Psychol. 48(4), 384 (1993)

    Article  Google Scholar 

  2. I. Cohen, N. Sebe, A. Garg, L.S. Chen, T.S. Huang, Facial expression recognition from video sequences: temporal and static modeling. Comput. Vis. Image Understand. 91(1–2), 160–187 (2003)

    Article  Google Scholar 

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

    Article  Google Scholar 

  4. M. Tang, F. Chen, Facial expression recognition and its application based on curvelet transform and PSO-SVM. Optik 124(22), 5401–5406 (2013)

    Article  Google Scholar 

  5. X. Xu, C. Quan, F. Ren: Facial expression recognition based on gabor wavelet transform and histogram of oriented gradients, in 2015 IEEE International Conference on Mechatronics and Automation (ICMA) (IEEE, 2015), pp. 2117–2122

    Google Scholar 

  6. B. Zhang, G. Liu, G. Xie: Facial expression recognition using IBP and IPQ based on gabor wavelet transform, in 2016 2nd IEEE International Conference on Computer and Communications (ICCC) (IEEE, 2016), pp. 365–369

    Google Scholar 

  7. N. Jain, S. Kumar, A. Kumar, P. Shamsolmoali, M. Zareapoor, Hybrid deep neural networks for face emotion recognition. Pattern Recogn. Lett. 115, 101–106 (2018)

    Article  Google Scholar 

  8. J. Li, D. Zhang, J. Zhang, J. Zhang, T. Li, Y. Xia, Q. Yan, L. Xun, Facial expression recognition with faster R-CNN. Proc. Comput. Sci. 107, 135–140 (2017)

    Article  Google Scholar 

  9. M. Liu, S. Li, S. Shan, R. Wang, X. Chen: Deeply learning deformable facial action parts model for dynamic expression analysis, in Asian Conference on Computer Vision (Springer, 2014), pp. 143–157

    Google Scholar 

  10. H. Jung, S. Lee, S. Park, B. Kim, J. Kim, I. Lee, C. Ahn, Development of deep learning-based facial expression recognition system, in 2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV) (IEEE, 2015), pp. 1–4

    Google Scholar 

  11. B. Fasel, J. Luettin, Automatic facial expression analysis: a survey. Pattern Recogn. 36(1), 259–275 (2003)

    Article  Google Scholar 

  12. S. Li, W. Deng, Deep facial expression recognition: a survey. arXiv preprint arXiv:1804.08348 (2018)

  13. G. Sandbach, S. Zafeiriou, M. Pantic, L. Yin, Static and dynamic 3d facial expression recognition: a comprehensive survey. Image Vis. Comput. 30(10), 683–697 (2012)

    Article  Google Scholar 

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

    Article  Google Scholar 

  15. P. Carcagnì, M. Del Coco, M. Leo, C. Distante, Facial expression recognition and histograms of oriented gradients: a comprehensive study. SpringerPlus 4(1), 645 (2015)

    Article  Google Scholar 

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

    Google Scholar 

  17. S. Poria, E. Cambria, R. Bajpai, A. Hussain, A review of affective computing: From unimodal analysis to multimodal fusion. Inf. Fusion 37, 98–125 (2017)

    Article  Google Scholar 

  18. X. Zhao, S. Zhang, A review on facial expression recognition: feature extraction and classification. IETE Tech. Rev. 33(5), 505–517 (2016)

    Article  Google Scholar 

  19. Y. Tian, T. Kanade, J.F. Cohn, Facial expression recognition, in Handbook of Face Recognition (Springer, 2011), pp. 487–519

    Google Scholar 

  20. E. Friesen, P. Ekman, Facial action coding system: a technique for the measurement of facial movement. Palo Alto 3, (1978)

    Google Scholar 

  21. M.Z. Uddin, J. Lee, T.S. Kim, An enhanced independent component-based human facial expression recognition from video. IEEE Trans. Consum. Electron. 55(4), 2216–2224 (2009)

    Article  Google Scholar 

  22. L. Franco, A. Treves, A neural network facial expression recognition system using unsupervised local processing, in ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In Conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat.) (IEEE, 2001), pp. 628–632

    Google Scholar 

  23. Z. Zhang, M. Lyons, M. Schuster, S. Akamatsu, Comparison between geometry-based and gabor-wavelets-based facial expression recognition using multi-layer perceptron, in Proceedings Third IEEE International Conference on Automatic face and gesture recognition (IEEE, 1998), pp. 454–459

    Google Scholar 

  24. S. Dongcheng, J. Jieqing: The method of facial expression recognition based on DWT-PCA/IDA, in 2010 3rd International Congress on Image and Signal Processing, vol. 4 (IEEE, 2010), pp. 1970–1974

    Google Scholar 

  25. B.K. Kim, J. Roh, S.Y. Dong, S.Y. Lee, Hierarchical committee of deep convolutional neural networks for robust facial expression recognition. J. Multimodal User Interfaces 10(2), 173–189 (2016)

    Article  Google Scholar 

  26. H. Li, J. Sun, Z. Xu, L. Chen, Multimodal 2d+3d facial expression recognition with deep fusion convolutional neural network. IEEE Trans. Multimed. 19(12), 2816–2831 (2017)

    Article  Google Scholar 

  27. P.S. Addison, The Illustrated Wavelet Transform Handbook: Introductory Theory and Applications in Science, Engineering, Medicine and Finance (CRC Press, 2017)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Banasthali Vidyapith, Rajasthan, India

    Pooja Sharma & Rajiv Singh

Authors
  1. Pooja Sharma
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  2. Rajiv Singh
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Corresponding author

Correspondence to Rajiv Singh .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Malla Reddy College of Engineering and Technology, Secunderabad, India

    V. Sivakumar Reddy

  2. Department of Computer Science and Engineering, Jawaharlal Nehru Technological University Hyderabad (JNTUH), Hyderabad, India

    V. Kamakshi Prasad

  3. Department of Computer Science and Software Engineering, Monmouth University, West Long Branch, NJ, USA

    Jiacun Wang

  4. Department of Electronics and Communication Engineering, Sir Visvesvaraya Institute of Technology, Nashik, India

    K. T. V. Reddy

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Sharma, P., Singh, R. (2022). An Approach Toward Deep Learning-Based Facial Expression Recognition in Wavelet Domain. In: Reddy, V.S., Prasad, V.K., Wang, J., Reddy, K.T.V. (eds) Soft Computing and Signal Processing. Advances in Intelligent Systems and Computing, vol 1340. Springer, Singapore. https://doi.org/10.1007/978-981-16-1249-7_10

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