Facial Expression Recognition through Optimal Filter Design Using a Metaheuristic Kidney Algorithm

Ghasemi Bideskan, E.; Razavi, S.M.; Mohamadzadeh, S.; Taghippour, M.

doi:10.22061/jecei.2024.10512.717

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	Facial Expression Recognition through Optimal Filter Design Using a Metaheuristic Kidney Algorithm
Journal of Electrical and Computer Engineering Innovations (JECEI)
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 11 اردیبهشت 1403
نوع مقاله: Original Research Paper
شناسه دیجیتال (DOI): 10.22061/jecei.2024.10512.717
نویسندگان
E. Ghasemi Bideskan¹؛ S.M. Razavi²؛ S. Mohamadzadeh^* ¹؛ M. Taghippour³
¹Department of Electrical and Computer Engineering, University of Birjand, Birjand, Iran.
²Department of Electrical Engineering, Faculty of Engineering, University of Birjand, Birjand, Iran.
³School of Engineering, London South Bank University, London, UK.
تاریخ دریافت: 16 دی 1402، تاریخ بازنگری: 22 فروردین 1403، تاریخ پذیرش: 11 اردیبهشت 1403
چکیده
Background and Objectives: The recognition of facial expressions using metaheuristic algorithms is a research topic in the field of computer vision. This article presents an approach to identify facial expressions using an optimized filter developed by metaheuristic algorithms. Methods: The entire process of feature extraction hinges on using a filter optimally configured by metaheuristic algorithms. Essentially, the purpose of utilizing this metaheuristic algorithm is to determine the optimal weights for feature extraction filters. Once the optimal weights for the filter have been determined by the metaheuristic algorithm, optimal filter sizes have also been determined. As an initial step, the k-nearest neighbor classifier is employed due to its simplicity and high accuracy. Following the initial stage, a final model is presented, which integrates results from both filterbank and Multilayer Perceptron neural networks. Results: An analysis of the existing instances in the FER2013 database has been conducted using the method proposed in this article. This model achieved a recognition rate of 78%, which is superior to other algorithms and methods while requiring less training time than other algorithms and methods.In addition, the JAFFE database, a Japanese women's database, was utilized for validation. On this dataset, the proposed approach achieved a 94.88% accuracy rate, outperforming other competitors. Conclusion: The purpose of this article is to propose a method for improving facial expression recognition by using an optimized filter, which is implemented through a metaheuristic algorithm based on the KA. In this approach, optimized filters were extracted using the metaheuristic algorithms kidney, k-nearest neighbor, and multilayer perceptron. Additionally, by employing this approach, the optimal size and number of filters for facial state recognition were determined in order to achieve the highest level of accuracy in the extraction process.
کلیدواژه‌ها
Optimal Filter؛ Kidney Algorithm؛ Nearest Neighbor Classification؛ Neural Network؛ Facial Expression recognition

مراجع
[1] S. Minaee, M. Minaei, A. Abdolrashidi, “Deep-emotion: Facial expression recognition using attentional convolutional network,” Sensors, 21(9): 3046, 2021. [2] S. Kaur, N. Kulkarni, “Emotion recognition-a review,” Int. J. Appl. Eng. Res., 16(2): 103-110, 2021. [3] E. Palagi, A. Celeghin, M. Tamietto, P. Winkielman, I. Norscia, “The neuroethology of spontaneous mimicry and emotional contagion in human and non-human animals,” Neurosci. Biobehav. Rev., 111: 149-165, 2020. [4] P. Singh, R. K. Mishra, S. Urolagin, V. Sharma, “Enhancing security by identifying facial check-in using deep convolutional neural network,” in Proc. 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N): 1006-1010, 2021. [5] J. He, “Algorithm composition and emotion recognition based on machine learning,” Comput. Intell. Neurosci., 2022 . [6] K. Richardson et al., “Robot enhanced therapy for children with autism (DREAM): A social model of autism,” IEEE Technol. Soc. Mag., 37(1): 30-39, 2018. [7] K. Osaka, R. Tanioka, F. Betriana, T. Tanioka, Y. Kai, R. C. Locsin, “Robot therapy program for patients with dementia: Its framework and effectiveness,” Information Systems - Intelligent Information Processing, p. 87, 2021. [8] D. Cruz-Sandoval, A. Morales-Tellez, E. B. Sandoval, J. Favela, “A social robot as therapy facilitator in interventions to deal with dementia-related behavioral symptoms,” in Proc. ACM/IEEE International Conference on Human-Robot Interaction: 161-169, 2020. [9] B. Szymona et al., “Robot-assisted autism therapy (RAAT). Criteria and types of experiments using anthropomorphic and zoomorphic robots. Review of the research,” Sensors, 21(11): 3720, 2021. [10] Z. Wang et al., “Masked face recognition dataset and application,” arXiv:2003.09093v2, 2020. [11] S. MacHiraju, S. Urolagin, R. K. Mishra, V. Sharma, “Face mask detection using keras, opencv and tensorflow by implementing mobilenetv2,” in Proc. 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N): 1485-1489, 2021. [12] Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE, 86(11):2278-2324, 1998. [13] S. Fooladi, H. Farsi, S. Mohamadzadeh., “Detection and classification of skin cancer using deep learning, ” J. Birjand Univ. Med. Sci., 26(1): 44-53, 2019.‏ [14] S. Albawi, T. A. Mohammed, S. Al-Zawi, “Understanding of a convolutional neural network,” in Proc. International Conference on Engineering and Technology (ICET), 2017. [15] B. Han, J. Sim, H. Adam, “BranchOut: Regularization for online ensemble tracking with convolutional neural networks,” in Proc. 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR ), 2017. [16] K. Liu, M. Zhang, Z. Pan, “Facial expression recognition with CNN Ensemble,” in Proc. 2016 International Conference on Cyberworlds, CW 2016. [17] S. Minaee, A. Abdolrashidi, “Deep-emotion: facial expression recognition using attentional convolutional network,” arXiv:1902.01019v1, 2019. [18] R. T. Ionescu, M. Popescu, C. Grozea, “Local learning to improve bag of visual words model for facial expression recognition,” in Proc. ICML 2013 Workshop on Representation Learning, 2013. [19] M. I. Georgescu, R. T. Ionescu, M. Popescu, “Local learning with deep and handcrafted features for facial expression recognition,” IEEE Access, 7: 64827- 64836, 2019. [20] P. Giannopoulos, I. Perikos, I. Hatzilygeroudis, “Deep learning approaches for facial emotion recognition: A case study on FER-2013,” Smart Innovation, Systems and Technologies, 85: 1-16, 2018. [21] A. Mollahosseini, D. Chan, M. H. Mahoor, “Going deeper in facial expression recognition using deep neural networks,” in Proc. IEEE Winter Conference on Applications of Computer Vision (WACV), 2016. [22] Y.Tang, “Deep learning using linear support vector machines,” arXiv, 1306.0239, 2013. [23] J. Shi, S. Zhu, Z. Liang, “Learning to amend facial expression representation via de-albino and affinity,” arXiv Prepr. arXiv2103.10189, 2021. [24] C. Pramerdorfer, M. Kampel, “Facial expression recognition using convolutional neural networks: state of the art,” arXiv Prepr. arXiv1612.02903, 2016. [25] K. Zhang, Z. Zhang, Z. Li, Y. Qiao, “Joint face detection and alignment using multitask cascaded convolutional networks,” IEEE Signal Process. Lett., 23(10): 1499-1503, 2016. [26] T. Shanmugaraja, R. Dhivaya Devi, M. Supriya, K. Murugan, T. Venkatesh, K. Siddarthraju, “A critical review on applications of adaptive filters,” J. Crit. Rev., 7(6): 656-659, 2020. [27] S. R. Ashokkumar, G. MohanBabu, S. Anupallavi, “A novel two-band equilateral wavelet filter bank method for an automated detection of seizure from EEG signals,” Int. J. Imaging Syst. Technol., 30(4): 978-993, 2020. [28] N. S. Jaddi, J. Alvankarian, S. Abdullah, “Kidney-inspired algorithm for optimization problems,” Commun. Nonlinear Sci. Numer. Simul., 42: 358-369, 2017. [29] I. J. Goodfellow et al., “Challenges in representation learning: A report on three machine learning contests,” Neural Netw., 64: 59-63, 2015. [30] M. Taheri, M. Rastgarpour, A. Koochari, “A novel method for medical image segmentation based on convolutional neural networks with SGD optimization,” J. Electr. Comput. Eng. Innov., 9 (1): 37-46, 2020. [31] S. Fooladi, H. Farsi, S. Mohamadzadeh, “Segmenting the lesion area of brain tumor using convolutional neural networks and fuzzy k-means clustering,” Int. J. Eng., 36(8): 1556-1568, 2023. [32] H. Bayati, M. B. Dowlatshahi, M. Paniri, “Multi-label feature selection based on competitive swarm optimization,” J. Soft Comput. Inf. Technol., 9(4): 56-69, 2020. [33] I. J. Goodfellow et al., “Challenges in representation learning: A report on three machine learning contests,” Neural Netw., 64: 59-63, 2015. [34] S. Minaee, M. Minaei, A. Abdolrashidi, “Deep-emotion: Facial expression recognition using attentional convolutional network,” Sensors, 21(9): 3046, 2021. [35] A. P. Fard, M. H. Mahoor, “Ad-corre: Adaptive correlation-based loss for facial expression recognition in the wild,” IEEE Access, 10: 26756-26768, 2022. [36] A. Khanzada, C. Bai, F. T. Celepcikay, “Facial expression recognition with deep learning,” arXiv Prepr. arXiv2004.11823, 2020. [37] M. I. Georgescu, R. T. Ionescu, M. Popescu, “Local learning with deep and handcrafted features for facial expression recognition,” IEEE Access, 7: 64827-64836, 2019. [38] Y. Khaireddin, Z. Chen, “Facial emotion recognition: State of the art performance on FER2013,” arXiv Prepr. arXiv2105.03588, 2021. [39] R. Pecoraro, V. Basile, V. Bono, “Local multi-head channel self-attention for facial expression recognition,” Information, 13(9): 419, 2022. [40] S. Vignesh, M. Savithadevi, M. Sridevi, R. Sridhar, “A novel facial emotion recognition model using segmentation VGG-19 architecture,” Int. J. Inf. Technol., 15(4): 1777-1787, 2023. [41] L. Pham, T. H. Vu, T. A. Tran, “Facial expression recognition using residual masking network,” in Proc. 25Th International Conference on Pattern Recognition (ICPR): 4513-4519, 2020. [42] A. Sajjanhar, Z. Wu, Q. Wen, “Deep learning models for facial expression recognition,” in Proc. 2018 International Conference on Digital Image Computing: Techniques and Applications (DICTA): 1-6, 2018. [43] Y. K. Bhatti, A. Jamil, N. Nida, M. H. Yousaf, S. Viriri, S. A. Velastin, “Facial expression recognition of instructor using deep features and extreme learning machine,” Comput. Intell. Neurosci., 2021: 1-17, 2021. [44] S. Minaee, M. Minaei, A. Abdolrashidi, “Deep-emotion: Facial expression recognition using attentional convolutional network,” Sensors, 21(9): 3046, 2021. [45] M. N. Kartheek, M. V. N. K. Prasad, R. Bhukya, “Windmill graph based feature descriptors for facial expression recognition,” Optik (Stuttg)., 260: 169053, 2022. [46] Z. Sun, H. Zhang, S. Ma, Z. Hu, “Combining filtered dictionary representation based deep subspace filter learning with a discriminative classification criterion for facial expression recognition,” Artif. Intell. Rev., 55(8): 6547-6566, 2022. [47] G. K. Sahoo, S. K. Das, P. Singh, “Performance comparison of facial emotion recognition: A transfer learning-based driver assistance framework for in-vehicle applications,” Circuits, Syst. Signal Process.: 1–28, 2023. [48] M. J. Lyons, J. Budynek, S. Akamatsu, “Automatic classification of single facial images,” IEEE Trans. Pattern Anal. Mach. Intell., 21(12): 1357-1362, 1999.
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Facial Expression Recognition through Optimal Filter Design Using a Metaheuristic Kidney Algorithm