Grey Wolf Optimization for Facial Emotion Recognition: Survey
DOI:
https://doi.org/10.29304/jqcm.2023.15.2.1229Keywords:
machine learning, deep learning, optimization algorithm, gray wolf optimization, neural networksAbstract
Human face expression Recognition is one of the most effective forms of social communication. Generally, facial expressions are a simple and obvious way for people to express their feelings and intentions. Typically, the goal of facial expression recognition is to categorize facial expressions into specific classes of expression labels. This paper presents a survey of facial emotion expression classification based on different machine learning and deep learning mechanisms and optimization algorithms. In order to evaluate the basic emotion of a person's face, a technology called facial expression recognition employs a computer as a helper with specific algorithms. Seven basic emotions were represented by facial expressions, including a smile, sadness, anger, disgust, surprise, fear, and a natural expression. In this paper, the focus is on using the Grey Wolf algorithm for selection of the optimal features from feature extraction from input image faces to recognize human facial emotions. In most studies, the FER system was applied to popular datasets such as the JAFEE database and the Cohn-Kanade database.
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References
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[19] C. Fu, Y. Hu, X. Wu, G. Wang, … Q. Z.-I. T. on, and undefined 2021, “High-fidelity face manipulation with extreme poses and expressions,” ieeexplore.ieee.org, Accessed: Dec. 02, 2022. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9316964/
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[21] T. Cao, C. Liu, J. Chen, and L. Gao, “Nonfrontal and Asymmetrical Facial Expression Recognition through Half-Face Frontalization and Pyramid Fourier Frequency Conversion,” IEEE Access, vol. 9, pp. 17127–17138, 2021, doi: 10.1109/ACCESS.2021.3052500.
[22] X. Zhang, F. Zhang, and C. Xu, “Joint Expression Synthesis and Representation Learning for Facial Expression Recognition,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 3, pp. 1681–1695, Mar. 2022, doi: 10.1109/TCSVT.2021.3056098.
[23] H. Joshi and S. Arora, “Enhanced grey Wolf optimization algorithm for global optimization,” Fundam Inform, vol. 153, no. 3, pp. 235–264, 2017, doi: 10.3233/FI-2017-1539.
[24] Z. M. Gao and J. Zhao, “An improved grey Wolf optimization algorithm with variable weights,” Comput Intell Neurosci, vol. 2019, 2019, doi: 10.1155/2019/2981282.
[25] E. Emary, H. M. Zawbaa, and C. Grosan, “Experienced Gray Wolf Optimization Through Reinforcement Learning and Neural Networks,” IEEE Trans Neural Netw Learn Syst, vol. 29, no. 3, pp. 681–694, Mar. 2018, doi: 10.1109/TNNLS.2016.2634548.
[26] Q. Al-Tashi, S. J. Abdul Kadir, H. M. Rais, S. Mirjalili, and H. Alhussian, “Binary Optimization Using Hybrid Grey Wolf Optimization for Feature Selection,” IEEE Access, vol. 7, pp. 39496–39508, 2019, doi: 10.1109/ACCESS.2019.2906757.
[27 W. Liu, M. Hu, Z. Ye, Y. Tang, … Z. W.-2019 10th I., and undefined 2019, “Feedforward Neural Network Based on Improved Gray Wolf Optimizer,” ieeexplore.ieee.org, Accessed: Dec. 03, 2022. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8924414/
[28 B. K. Kihel and S. Chouraqui, “A Novel Genetic Grey Wolf optimizer for Global optimization and Feature Selection,” in 2020 2nd International Conference on Embedded and Distributed Systems, EDiS 2020, Nov. 2020, pp. 82–86. doi: 10.1109/EDiS49545.2020.9296449.
[29] Y. Zhang, D. Liu, J. Liu, Y. Xian, and X. Wang, “Improved Deep Neural Network for OFDM Signal Recognition Using Hybrid Grey Wolf Optimization,” IEEE Access, vol. 8, pp. 133622–133632, 2020, doi: 10.1109/ACCESS.2020.3010589.
[30] J. C. Bansal and S. Singh, “A better exploration strategy in Grey Wolf Optimizer,” J Ambient Intell Humaniz Comput, vol. 12, no. 1, pp. 1099–1118, Jan. 2021, doi: 10.1007/s12652-020-02153-1.
[31] P. M. Ashok Kumar, J. B. Maddala, and K. Martin Sagayam, “Enhanced Facial Emotion Recognition by Optimal Descriptor Selection with Neural Network,” IETE J Res, 2021, doi: 10.1080/03772063.2021.1902868.
[32] Minaee, S., Minaei, M., & Abdolrashidi, A. (2021). Deep-emotion: Facial expression recognition using attentional convolutional network. Sensors, 21(9), 3046.
[33] Eng, S. K., Ali, H., Cheah, A. Y., & Chong, Y. F. (2019, November). Facial expression recognition in JAFFE and KDEF Datasets using histogram of oriented gradients and support vector machine. In IOP Conference series: materials science and engineering (Vol. 705, No. 1, p. 012031). IOP
[34] Li, K., Jin, Y., Akram, M. W., Han, R., & Chen, J. (2020). Facial expression recognition with convolutional neural networks via a new face cropping and rotation strategy. The visual computer, 36, 391-404.
[35] Saurav, S., Saini, R., & Singh, S. (2021). Facial expression recognition using dynamic local ternary patterns with kernel extreme learning machine classifier. IEEE Access, 9, 120844-120868.
[36] Ghosh, M., Kundu, T., Ghosh, D., & Sarkar, R. (2019). Feature selection for facial emotion recognition using late hill-climbing based memetic algorithm. Multimedia Tools and Applications, 78, 25753-25779.
[37] Saha, S., Ghosh, M., Ghosh, S., Sen, S., Singh, P. K., Geem, Z. W., & Sarkar, R. (2020). Feature selection for facial emotion recognition using cosine similarity-based harmony search algorithm. Applied Sciences, 10(8), 2816.
[38] Paharia, N., Jadon, R. S., & Gupta, S. K. (2022). Feature selection using improved multiobjective and opposition-based competitive binary gray wolf optimizer for facial expression recognition. Journal of Electronic Imaging, 31(3), 033039-033039.
[39] Alenazy, W. M., & Alqahtani, A. S. (2021). Gravitational search algorithm based optimized deep learning model with diverse set of features for facial expression recognition. Journal of ambient intelligence and humanized computing, 12, 1631-1646.
[40] Shima, Y., & Omori, Y. (2018, August). Image augmentation for classifying facial expression images by using deep neural network pre-trained with object image database. In Proceedings of the 3rd International Conference on Robotics, Control and Automation (pp. 140-146).
[41] Kaviya, P., & Arumugaprakash, T. (2020, June). Group facial emotion analysis system using convolutional neural network. In 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) (pp. 643-647). IEEE.
[42] Mohan, K., Seal, A., Krejcar, O., & Yazidi, A. (2020). Facial expression recognition using local gravitational force descriptor-based deep convolution neural networks. IEEE Transactions on Instrumentation and Measurement, 70, 1-12.
[43] Tian, Chunwei, Lunke Fei, Wenxian Zheng, Yong Xu, Wangmeng Zuo, and Chia-Wen Lin. "Deep learning on image denoising: An overview." Neural Networks 131 (2020): 251-275.
[44] Altenberger, F., & Lenz, C. (2018). A non-technical survey on deep convolutional neural network architectures. arXiv preprint arXiv:1803.02129.
[45] Imani, M., & Montazer, G. A. (2019). A survey of emotion recognition methods with emphasis on E-Learning environments. Journal of Network and Computer Applications, 147, 102423.
[2] H. Lee, K. H.-2017 I. C. on, and undefined 2017, “A study on emotion recognition method and its application using face image,” ieeexplore.ieee.org, Accessed: Dec. 02, 2022. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8191005/
[3] N. P. N. Sreedharan, B. Ganesan, R. Raveendran, P. Sarala, B. Dennis, and R. Rajakumar Boothalingam, “Grey Wolf optimisation-based feature selection and classification for facial emotion recognition,” IET Biom, vol. 7, no. 5, pp. 490–499, Sep. 2018, doi: 10.1049/iet-bmt.2017.0160.
[4] J. Guo et al., “Dominant and Complementary Emotion Recognition from Still Images of Faces,” IEEE Access, vol. 6, pp. 26391–26403, Apr. 2018, doi: 10.1109/ACCESS.2018.2831927.
[5] T. Zhang, W. Zheng, Z. Cui, Y. Zong, and Y. Li, “Spatial-Temporal Recurrent Neural Network for Emotion Recognition,” IEEE Trans Cybern, vol. 49, no. 3, pp. 939–947, Mar. 2019, doi: 10.1109/TCYB.2017.2788081.
[6] M. Zadeh, M. Imani, B. M.-2019 5th C. on, and undefined 2019, “Fast facial emotion recognition using convolutional neural networks and Gabor filters,” ieeexplore.ieee.org, Accessed: Dec. 02, 2022. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8734943/
[7] S. Ma et al., “A face sequence recognition method based on deep convolutional neural network,” in Proceedings - 2019 18th International Symposium on Distributed Computing and Applications for Business Engineering and Science, DCABES 2019, Nov. 2019, pp. 104–107. doi: 10.1109/DCABES48411.2019.00033.
[8] H. Zhang, A. Jolfaei, and M. Alazab, “A Face Emotion Recognition Method Using Convolutional Neural Network and Image Edge Computing,” IEEE Access, vol. 7, pp. 159081–159089, 2019, doi: 10.1109/ACCESS.2019.2949741.
[9] A. N. Salman and C. Busso, “Dynamic versus Static Facial Expressions in the Presence of Speech,” in Proceedings - 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2020, Nov. 2020, pp. 436–443. doi: 10.1109/FG47880.2020.00119.
[10] H. Sikkandar and R. Thiyagarajan, “Soft biometrics-based face image retrieval using improved Grey Wolf optimisation,” IET Image Process, vol. 14, no. 3, pp. 451–461, Feb. 2020, doi: 10.1049/iet-ipr.2019.0271.
[11] J. H. Lee, H. J. Kim, and Y. G. Cheong, “A multi-modal approach for emotion recognition of TV drama characters using image and text,” in Proceedings - 2020 IEEE International Conference on Big Data and Smart Computing, BigComp 2020, Feb. 2020, pp. 420–424. doi: 10.1109/BigComp48618.2020.00-37.
[12] N. Ayari, H. Abdelkawy, A. Chibani, and Y. Amirat, “Hybrid Model-Based Emotion Contextual Recognition for Cognitive Assistance Services,” IEEE Trans Cybern, vol. 52, no. 5, pp. 3567–3576, May 2022, doi: 10.1109/TCYB.2020.3013112.
[13] G. Pons and D. Masip, “Multitask, Multilabel, and Multidomain Learning with Convolutional Networks for Emotion Recognition,” IEEE Trans Cybern, vol. 52, no. 6, pp. 4764–4771, Jun. 2022, doi: 10.1109/TCYB.2020.3036935.
[14] P. Kaviya, T. A.-2020 4th International, and undefined 2020, “Group facial emotion analysis system using convolutional neural network,” ieeexplore.ieee.org, Accessed: Dec. 02, 2022. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9143037/
[15] A. Vulpe-Grigorasi and O. Grigore, “Convolutional Neural Network Hyperparameters optimization for Facial Emotion Recognition,” in 12th International Symposium on Advanced Topics in Electrical Engineering, ATEE 2021, Mar. 2021. doi: 10.1109/ATEE52255.2021.9425073.
[16] A. Jamshed, B. Mallick, and R. K. Bharti, “An Efficient Pattern Mining Convolution Neural Network (CNN) algorithm with Grey Wolf Optimization (GWO),” Apr. 2022, Accessed: Dec. 02, 2022. [Online]. Available: http://arxiv.org/abs/2204.04704
[17] Debnath, T., Reza, M. M., Rahman, A., Beheshti, A., Band, S. S., & Alinejad-Rokny, H. (2022). Four-layer ConvNet to facial emotion recognition with minimal epochs and the significance of data diversity. Scientific Reports, 12(1), 6991.
[18] F. Zhang, T. Zhang, Q. Mao, and C. Xu, “Geometry Guided Pose-Invariant Facial Expression Recognition,” IEEE Transactions on Image Processing, vol. 29, pp. 4445–4460, 2020, doi: 10.1109/TIP.2020.2972114.
[19] C. Fu, Y. Hu, X. Wu, G. Wang, … Q. Z.-I. T. on, and undefined 2021, “High-fidelity face manipulation with extreme poses and expressions,” ieeexplore.ieee.org, Accessed: Dec. 02, 2022. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9316964/
[20] T. R. Huang, S. M. Hsu, and L. C. Fu, “Data Augmentation via Face Morphing for Recognizing Intensities of Facial Emotions,” IEEE Trans Affect Comput, 2021, doi: 10.1109/TAFFC.2021.3096922.
[21] T. Cao, C. Liu, J. Chen, and L. Gao, “Nonfrontal and Asymmetrical Facial Expression Recognition through Half-Face Frontalization and Pyramid Fourier Frequency Conversion,” IEEE Access, vol. 9, pp. 17127–17138, 2021, doi: 10.1109/ACCESS.2021.3052500.
[22] X. Zhang, F. Zhang, and C. Xu, “Joint Expression Synthesis and Representation Learning for Facial Expression Recognition,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 3, pp. 1681–1695, Mar. 2022, doi: 10.1109/TCSVT.2021.3056098.
[23] H. Joshi and S. Arora, “Enhanced grey Wolf optimization algorithm for global optimization,” Fundam Inform, vol. 153, no. 3, pp. 235–264, 2017, doi: 10.3233/FI-2017-1539.
[24] Z. M. Gao and J. Zhao, “An improved grey Wolf optimization algorithm with variable weights,” Comput Intell Neurosci, vol. 2019, 2019, doi: 10.1155/2019/2981282.
[25] E. Emary, H. M. Zawbaa, and C. Grosan, “Experienced Gray Wolf Optimization Through Reinforcement Learning and Neural Networks,” IEEE Trans Neural Netw Learn Syst, vol. 29, no. 3, pp. 681–694, Mar. 2018, doi: 10.1109/TNNLS.2016.2634548.
[26] Q. Al-Tashi, S. J. Abdul Kadir, H. M. Rais, S. Mirjalili, and H. Alhussian, “Binary Optimization Using Hybrid Grey Wolf Optimization for Feature Selection,” IEEE Access, vol. 7, pp. 39496–39508, 2019, doi: 10.1109/ACCESS.2019.2906757.
[27 W. Liu, M. Hu, Z. Ye, Y. Tang, … Z. W.-2019 10th I., and undefined 2019, “Feedforward Neural Network Based on Improved Gray Wolf Optimizer,” ieeexplore.ieee.org, Accessed: Dec. 03, 2022. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/8924414/
[28 B. K. Kihel and S. Chouraqui, “A Novel Genetic Grey Wolf optimizer for Global optimization and Feature Selection,” in 2020 2nd International Conference on Embedded and Distributed Systems, EDiS 2020, Nov. 2020, pp. 82–86. doi: 10.1109/EDiS49545.2020.9296449.
[29] Y. Zhang, D. Liu, J. Liu, Y. Xian, and X. Wang, “Improved Deep Neural Network for OFDM Signal Recognition Using Hybrid Grey Wolf Optimization,” IEEE Access, vol. 8, pp. 133622–133632, 2020, doi: 10.1109/ACCESS.2020.3010589.
[30] J. C. Bansal and S. Singh, “A better exploration strategy in Grey Wolf Optimizer,” J Ambient Intell Humaniz Comput, vol. 12, no. 1, pp. 1099–1118, Jan. 2021, doi: 10.1007/s12652-020-02153-1.
[31] P. M. Ashok Kumar, J. B. Maddala, and K. Martin Sagayam, “Enhanced Facial Emotion Recognition by Optimal Descriptor Selection with Neural Network,” IETE J Res, 2021, doi: 10.1080/03772063.2021.1902868.
[32] Minaee, S., Minaei, M., & Abdolrashidi, A. (2021). Deep-emotion: Facial expression recognition using attentional convolutional network. Sensors, 21(9), 3046.
[33] Eng, S. K., Ali, H., Cheah, A. Y., & Chong, Y. F. (2019, November). Facial expression recognition in JAFFE and KDEF Datasets using histogram of oriented gradients and support vector machine. In IOP Conference series: materials science and engineering (Vol. 705, No. 1, p. 012031). IOP
[34] Li, K., Jin, Y., Akram, M. W., Han, R., & Chen, J. (2020). Facial expression recognition with convolutional neural networks via a new face cropping and rotation strategy. The visual computer, 36, 391-404.
[35] Saurav, S., Saini, R., & Singh, S. (2021). Facial expression recognition using dynamic local ternary patterns with kernel extreme learning machine classifier. IEEE Access, 9, 120844-120868.
[36] Ghosh, M., Kundu, T., Ghosh, D., & Sarkar, R. (2019). Feature selection for facial emotion recognition using late hill-climbing based memetic algorithm. Multimedia Tools and Applications, 78, 25753-25779.
[37] Saha, S., Ghosh, M., Ghosh, S., Sen, S., Singh, P. K., Geem, Z. W., & Sarkar, R. (2020). Feature selection for facial emotion recognition using cosine similarity-based harmony search algorithm. Applied Sciences, 10(8), 2816.
[38] Paharia, N., Jadon, R. S., & Gupta, S. K. (2022). Feature selection using improved multiobjective and opposition-based competitive binary gray wolf optimizer for facial expression recognition. Journal of Electronic Imaging, 31(3), 033039-033039.
[39] Alenazy, W. M., & Alqahtani, A. S. (2021). Gravitational search algorithm based optimized deep learning model with diverse set of features for facial expression recognition. Journal of ambient intelligence and humanized computing, 12, 1631-1646.
[40] Shima, Y., & Omori, Y. (2018, August). Image augmentation for classifying facial expression images by using deep neural network pre-trained with object image database. In Proceedings of the 3rd International Conference on Robotics, Control and Automation (pp. 140-146).
[41] Kaviya, P., & Arumugaprakash, T. (2020, June). Group facial emotion analysis system using convolutional neural network. In 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) (pp. 643-647). IEEE.
[42] Mohan, K., Seal, A., Krejcar, O., & Yazidi, A. (2020). Facial expression recognition using local gravitational force descriptor-based deep convolution neural networks. IEEE Transactions on Instrumentation and Measurement, 70, 1-12.
[43] Tian, Chunwei, Lunke Fei, Wenxian Zheng, Yong Xu, Wangmeng Zuo, and Chia-Wen Lin. "Deep learning on image denoising: An overview." Neural Networks 131 (2020): 251-275.
[44] Altenberger, F., & Lenz, C. (2018). A non-technical survey on deep convolutional neural network architectures. arXiv preprint arXiv:1803.02129.
[45] Imani, M., & Montazer, G. A. (2019). A survey of emotion recognition methods with emphasis on E-Learning environments. Journal of Network and Computer Applications, 147, 102423.
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Published
2023-09-24
How to Cite
Marzouk, H. F., & Mosa, Q. O. (2023). Grey Wolf Optimization for Facial Emotion Recognition: Survey. Journal of Al-Qadisiyah for Computer Science and Mathematics, 15(2), Comp Page 11–26. https://doi.org/10.29304/jqcm.2023.15.2.1229
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Computer Articles
