An Effective Combination of Textures and Wavelet Features for Facial Expression Recognition

Authors

  • S. M. Hassan Department of AI and Mathematical Sciences, SMI University, Pakistan
  • A. Alghamdi College of Computer Science and Information Systems, Najran University, Saudi Arabia
  • A. Hafeez Department of Software Engineering, SMI University, Pakistan
  • M. Hamdi College of Computer Science and Information Systems, Najran University, Saudi Arabia
  • I. Hussain Department of AI and Mathematical Sciences, SMI University, Pakistan
  • M. Alrizq College of Computer Science and Information Systems, Najran University, Saudi Arabia

Abstract

In order to explore the accompanying examination goals for facial expression recognition, a proper combination of classification and adequate feature extraction is necessary. If inadequate features are used, even the best classifier could fail to achieve accurate recognition. In this paper, a new fusion technique for human facial expression recognition is used to accurately recognize human facial expressions. A combination of Discrete Wavelet Features (DWT), Local Binary Pattern (LBP), and Histogram of Gradients (HoG) feature extraction techniques was used to investigate six human emotions. K-Nearest Neighbors (KNN), Decision Tree (DT), Multi-Layer Perceptron (MLP), and Random Forest (RF) were chosen for classification. These algorithms were implemented and tested on the Static Facial Expression in Wild (SWEW) dataset which consists of facial expressions of high accuracy. The proposed algorithm exhibited 87% accuracy which is higher than the accuracy of the individual algorithms.

Keywords:

ANN, FER, DWT, LBP, HOG, K-Nearest Neighbors

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References

W.-L. Chao, J.-J. Ding, and J.-Z. Liu, "Facial expression recognition based on improved local binary pattern and class-regularized locality preserving projection," Signal Processing, vol. 117, pp. 1–10, Dec. 2015. DOI: https://doi.org/10.1016/j.sigpro.2015.04.007

F. Long and M. S. Bartlett, "Video-based facial expression recognition using learned spatiotemporal pyramid sparse coding features," Neurocomputing, vol. 173, pp. 2049–2054, Jan. 2016. DOI: https://doi.org/10.1016/j.neucom.2015.09.049

H. Fang et al., "Facial expression recognition in dynamic sequences: An integrated approach," Pattern Recognition, vol. 47, no. 3, pp. 1271–1281, Mar. 2014. DOI: https://doi.org/10.1016/j.patcog.2013.09.023

J. Hussain Shah, M. Sharif, M. Raza, M. Murtaza, and S. Ur-Rehman, "Robust Face Recognition Technique under Varying Illumination," Journal of applied research and technology, vol. 13, no. 1, pp. 97–105, 2015. DOI: https://doi.org/10.1016/S1665-6423(15)30008-0

S. Arya, N. Pratap, and K. Bhatia, "Future of Face Recognition: A Review," Procedia Computer Science, vol. 58, pp. 578–585, Jan. 2015. DOI: https://doi.org/10.1016/j.procs.2015.08.076

M.-Y. Chen and C.-C. Chen, "The contribution of the upper and lower face in happy and sad facial expression classification," Vision Research, vol. 50, no. 18, pp. 1814–1823, Aug. 2010. DOI: https://doi.org/10.1016/j.visres.2010.06.002

A. Fernandez, O. Ghita, E. Gonzalez, F. Bianconi, and P. F. Whelan, "Evaluation of robustness against rotation of LBP, CCR and ILBP features in granite texture classification," Machine Vision and Applications, vol. 22, no. 6, pp. 913–926, Nov. 2011. DOI: https://doi.org/10.1007/s00138-010-0253-4

S. Shankar and V. R. Udupi, "Recognition of Faces – An Optimized Algorithmic Chain," Procedia Computer Science, vol. 89, pp. 597–606, Jan. 2016. DOI: https://doi.org/10.1016/j.procs.2016.06.020

R. K. Nagar, R. Manazhy, and P. Sankaran, "Sparse Manifold Discriminant Embedding for Face Recognition," Procedia Computer Science, vol. 89, pp. 743–748, Jan. 2016. DOI: https://doi.org/10.1016/j.procs.2016.06.050

T. Gao, X. L. Feng, H. Lu, and J. H. Zhai, "A novel face feature descriptor using adaptively weighted extended LBP pyramid," Optik, vol. 124, no. 23, pp. 6286–6291, Dec. 2013. DOI: https://doi.org/10.1016/j.ijleo.2013.05.007

K. Yu, Z. Wang, L. Zhuo, J. Wang, Z. Chi, and D. Feng, "Learning realistic facial expressions from web images," Pattern Recognition, vol. 46, no. 8, pp. 2144–2155, Aug. 2013. DOI: https://doi.org/10.1016/j.patcog.2013.01.032

R. A. Khan, A. Meyer, H. Konik, and S. Bouakaz, "Framework for reliable, real-time facial expression recognition for low resolution images," Pattern Recognition Letters, vol. 34, no. 10, pp. 1159–1168, Jul. 2013. DOI: https://doi.org/10.1016/j.patrec.2013.03.022

S. Ali Khan, A. Hussain, and M. Usman, "Facial expression recognition on real world face images using intelligent techniques: A survey," Optik, vol. 127, no. 15, pp. 6195–6203, Aug. 2016. DOI: https://doi.org/10.1016/j.ijleo.2016.04.015

S. Ali Khan, A. Hussain, A. Basit, and S. Akram, "Kruskal-Wallis-Based Computationally Efficient Feature Selection for Face Recognition," The Scientific World Journal, vol. 2014, May 2014, Art. no. e672630. DOI: https://doi.org/10.1155/2014/672630

C. Shan, S. Gong, and P. W. McOwan, "Facial expression recognition based on Local Binary Patterns: A comprehensive study," Image and Vision Computing, vol. 27, no. 6, pp. 803–816, May 2009. DOI: https://doi.org/10.1016/j.imavis.2008.08.005

W.-H. Chen, P.-C. Cho, P.-L. Fan, and Y.-W. Yang, "A framework for vision-based swimmer tracking," in International Conference on Uncertainty Reasoning and Knowledge Engineering, Bali, Indonesia, Aug. 2011, vol. 1, pp. 44–47. DOI: https://doi.org/10.1109/URKE.2011.6007835

D. Zecha, T. Greif, and R. Lienhart, "Swimmer detection and pose estimation for continuous stroke-rate determination," in Multimedia on Mobile Devices 2012; and Multimedia Content Access: Algorithms and Systems VI, California, United States, Jan. 2012, vol. 8304, Art. no. 830410.

S. S. Intille, J. W. Davis, and A. F. Bobick, "Real-time closed-world tracking," in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Juan, USA, Jun. 1997, pp. 697–703.

K.-A. Toh, W.-Y. Yau, and X. Jiang, "A reduced multivariate polynomial model for multimodal biometrics and classifiers fusion," IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 2, pp. 224–233, Feb. 2004. DOI: https://doi.org/10.1109/TCSVT.2003.821974

M. Kharrat, Y. Wakuda, N. Koshizuka, and K. Sakamura, "Automatic waist airbag drowning prevention system based on underwater time-lapse and motion information measured by smartphone’s pressure sensor and accelerometer," in IEEE International Conference on Consumer Electronics, Las Vegas, NE, USA, Jan. 2013, pp. 270–273. DOI: https://doi.org/10.1109/ICCE.2013.6486891

M. Kharrat, Y. Wakuda, S. Kobayashi, N. Koshizuka, and K. Sakamura, "Near drowning detection system based on swimmer’s physiological information analysis," presented at the World Conference on Drowning Prevention (WCDP), May 2011.

E. McAdams et al., "Wearable sensor systems: The challenges," in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA, Sep. 2011, pp. 3648–3651. DOI: https://doi.org/10.1109/IEMBS.2011.6090614

A. Ben-Hur, D. Horn, H. T. Siegelmann, and V. Vapnik, "Support Vector Clustering," Journal of Machine Learning Research, vol. 2, pp. 125–137, 2001.

Y. Said, M. Barr, and H. E. Ahmed, "Design of a Face Recognition System based on Convolutional Neural Network (CNN)," Engineering, Technology & Applied Science Research, vol. 10, no. 3, pp. 5608–5612, Jun. 2020. DOI: https://doi.org/10.48084/etasr.3490

D. Virmani, P. Girdhar, P. Jain, and P. Bamdev, "FDREnet: Face Detection and Recognition Pipeline," Engineering, Technology & Applied Science Research, vol. 9, no. 2, pp. 3933–3938, Apr. 2019. DOI: https://doi.org/10.48084/etasr.2492

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[1]
Hassan, S.M., Alghamdi, A., Hafeez, A., Hamdi, M., Hussain, I. and Alrizq, M. 2021. An Effective Combination of Textures and Wavelet Features for Facial Expression Recognition. Engineering, Technology & Applied Science Research. 11, 3 (Jun. 2021), 7172–7176. DOI:https://doi.org/10.48084/etasr.4080.

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