{"id":4347,"date":"2026-03-02T10:09:32","date_gmt":"2026-03-02T04:39:32","guid":{"rendered":"https:\/\/journal.ipem.edu.in\/computer_applications\/a-comprehensive-survey-on-cloud-computing-concepts-challenges-and-future-directions-2\/"},"modified":"2026-03-09T11:46:13","modified_gmt":"2026-03-09T06:16:13","slug":"bridging-accuracy-and-efficiency-in-facial-expression-recognition-an-adaptive-swarm-intelligence-framework-for-resource-constrained-environments","status":"publish","type":"page","link":"https:\/\/journal.ipem.edu.in\/computer_applications\/bridging-accuracy-and-efficiency-in-facial-expression-recognition-an-adaptive-swarm-intelligence-framework-for-resource-constrained-environments\/","title":{"rendered":"Bridging Accuracy and Efficiency in Facial Expression Recognition: An Adaptive Swarm Intelligence Framework for Resource-Constrained Environments"},"content":{"rendered":"\t\t
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Bridging Accuracy and Efficiency in Facial Expression Recognition: An Adaptive Swarm Intelligence Framework for Resource-Constrained Environments<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t Shweta Sharma<\/em><\/a>,\r\n Prof. Ashok Kumar<\/em><\/a>\r\n\r\n\r\n\r\n\r\n\r\n

Vol 10 , Issue 1 , December 2025<\/a> | <\/span>Pages: 13-19\r\n <\/p>\r\n \r\n

DOI: <\/a><\/p>\r\n\r\n

Published Online: December, 2025<\/span><\/p>\r\n

Download Article<\/a><\/p>\r\n \t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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  • \n \n \n \n Author Affiliations<\/span> <\/li>\n
  • \n \n \n \n Abstract<\/span> <\/li>\n
  • \n \n \n \n References<\/span> <\/li>\n
  • \n \n \n \n Citation<\/span> <\/li>\n <\/ul>\n <\/div>\n \n
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    Author Details<\/b><\/p>

    1. <\/b>Shweta Sharma, <\/strong>Research Scholar, College of Computing Science & Information Technology Teerthanker Mahaveer University, Moradabad-244001, Uttar Pradesh, India<\/div>
    2. <\/b>Prof. Ashok Kumar, <\/strong>Professor, College of Computing Science & Information Technology Teerthanker Mahaveer University, Moradabad-244001, Uttar Pradesh, India<\/div><\/div> <\/div>\n\t\t \n
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    Facial expression recognition (FER) systems confront major computational hurdles due to high-dimensional feature spaces, limiting their application on resource-constrained devices. This research offers a unique Particle Swarm Optimization with Adaptive Opposition-Based Learning (PSO-AOBL) framework for optimal feature selection in FER systems. The suggested adaptive technique prevents premature convergence while preserving quick optimization performance by dynamically initiating opposition-based learning based on swarm diversity. Three benchmark datasets\u2014CK+, FER2013, and JAFFE\u2014are used to assess our methodology. Experimental results show PSO-AOBL reduces features by 73% (from 3,918 to 1,050 features) and inference time by 75% while achieving 94.2% accuracy on the CK+ dataset. Compared to normal PSO, our technique offers 2.1% accuracy improvement and 43% faster convergence.<\/p>

    Keywords<\/p>

    Facial expression recognition, feature selection, particle swarm optimization, swarm intelligence, computational efficiency.<\/p> <\/div>\n\t\t \n

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    [1] S. Li and W. Deng, “Deep facial expression recognition: A survey,”\u00a0IEEE Trans. Affective Comput.<\/em>, vol. 13, no. 3, pp. 1195-1215, 2022.<\/p>

    [2] A. Mollahosseini et al., “AffectNet: A database for facial expression,”\u00a0IEEE Trans. Affective Comput.<\/em>, vol. 10, no. 1, pp. 18-31, 2019.<\/p>

    [3] Y. Zhang et al., “Relative uncertainty learning for facial expression recognition,”\u00a0Proc. NeurIPS<\/em>, pp. 17616-17627, 2021.<\/p>

    [4] M. Ghosh et al., “Feature selection for facial expression recognition: A review,”\u00a0Expert Syst. Appl.<\/em>, vol. 189, article 116077, 2022.<\/p>

    [5] I. Guyon and A. Elisseeff, “An introduction to variable and feature selection,”\u00a0J. Mach. Learn. Res.<\/em>, vol. 3, pp. 1157-1182, 2003.<\/p>

    [6] G. Chandrashekar and F. Sahin, “A survey on feature selection methods,”\u00a0Computers Elect. Eng.<\/em>, vol. 40, no. 1, pp. 16-28, 2014.<\/p>

    [7] L. Yu and H. Liu, “Efficient feature selection via analysis of relevance and redundancy,”\u00a0J. Mach. Learn. Res.<\/em>, vol. 5, pp. 1205-1224, 2004.<\/p>

    [8] M. Dash and H. Liu, “Feature selection for classification,”\u00a0Intell. Data Anal.<\/em>, vol. 1, no. 3, pp. 131-156, 1997.<\/p>

    [9] M. Dorigo et al., “Ant colony optimization,”\u00a0IEEE Comput. Intell. Mag.<\/em>, vol. 1, no. 4, pp. 28-39, 2006.<\/p>

    [10] R. Poli et al., “Particle swarm optimization: An overview,”\u00a0Swarm Intell.<\/em>, vol. 1, no. 1, pp. 33-57, 2007.<\/p>

    [11] J. Kennedy and R. Eberhart, “Particle swarm optimization,”\u00a0Proc. IEEE ICNN<\/em>, pp. 1942-1948, 1995.<\/p>

    [12] J. Kennedy and R. C. Eberhart, “A discrete binary version of PSO,”\u00a0Proc. IEEE SMC<\/em>, pp. 4104-4108, 1997.<\/p>

    [13] B. Xue et al., “PSO for feature selection: A multi-objective approach,”\u00a0IEEE Trans. Cybern.<\/em>, vol. 43, no. 6, pp. 1656-1671, 2013.<\/p>

    [14] X. Jiang et al., “DFEW: A large-scale database for dynamic facial expressions,”\u00a0Proc. ACM MM<\/em>, pp. 2881-2889, 2020.<\/p>

    [15] M. M. Kabir et al., “A new hybrid ant colony optimization algorithm,”\u00a0Expert Syst. Appl.<\/em>, vol. 39, no. 3, pp. 3747-3763, 2012.<\/p>

    [16] H. R. Tizhoosh, “Opposition-based learning: A new scheme,”\u00a0Proc. CIMCA<\/em>, pp. 695-701, 2005.<\/p>

    [17] P. Ekman and W. V. Friesen, “Facial action coding system,” Consulting Psychologists Press, 1978.<\/p>

    [18] T. Ojala et al., “Local binary patterns,”\u00a0IEEE Trans. PAMI<\/em>, vol. 24, no. 7, pp. 971-987, 2002.<\/p>

    [19] N. Dalal and B. Triggs, “Histograms of oriented gradients,”\u00a0Proc. IEEE CVPR<\/em>, pp. 886-893, 2005.<\/p>

    [20] K. Wang et al., “Suppressing uncertainties for large-scale FER,”\u00a0Proc. IEEE CVPR<\/em>, pp. 6897-6906, 2020.<\/p>

    [21] F. Zhang et al., “Joint pose and expression modeling,”\u00a0Proc. IEEE CVPR<\/em>, pp. 3359-3368, 2022.<\/p>

    [22] Y. Li et al., “Self-supervised representation learning,”\u00a0Proc. IEEE CVPR<\/em>, pp. 3633-3642, 2021.<\/p>

    [23] K. Kira and L. A. Rendell, “A practical approach to feature selection,”\u00a0Proc. ICML<\/em>, pp. 249-256, 1992.<\/p>

    [24] I. Guyon et al., “Gene selection using support vector machines,”\u00a0Mach. Learn.<\/em>, vol. 46, pp. 389-422, 2002.<\/p>

    [25] S. Ghosh et al., “Automatic group affect analysis,”\u00a0IEEE Trans. Affective Comput.<\/em>, vol. 14, no. 2, pp. 1169-1180, 2023.<\/p>

    [26] A. Kumar et al., “Feature selection using regularization methods,”\u00a0Pattern Recognit. Lett.<\/em>, vol. 158, pp. 123-130, 2023.<\/p>

    [27] M. Dorigo and L. M. Gambardella, “Ant colony system,”\u00a0IEEE Trans. Evol. Comput.<\/em>, vol. 1, no. 1, pp. 53-66, 1997.<\/p>

    [28] B. Xue et al., “A survey on evolutionary computation for feature selection,”\u00a0IEEE Trans. Evol. Comput.<\/em>, vol. 20, no. 4, pp. 606-626, 2016.<\/p>

    [29] J. Too et al., “Hybrid binary PSO differential evolution,”\u00a0IEEE Access<\/em>, vol. 8, pp. 140753-140769, 2020.<\/p>

    [30] M. Mafarja and S. Mirjalili, “Hybrid whale optimization algorithm,”\u00a0Neurocomputing<\/em>, vol. 260, pp. 302-312, 2017.<\/p>

    [31] S. Rahnamayan et al., “Opposition-based differential evolution,”\u00a0IEEE Trans. Evol. Comput.<\/em>, vol. 12, no. 1, pp. 64-79, 2008.<\/p>

    [32] H. Wang et al., “Enhancing PSO using generalized opposition-based learning,”\u00a0Inf. Sci.<\/em>, vol. 181, no. 20, pp. 4699-4714, 2011.<\/p>

    [33] Q. Xu et al., “A review of opposition-based learning,”\u00a0Eng. Appl. Artif. Intell.<\/em>, vol. 29, pp. 1-12, 2014.<\/p>

    [34] S. Ergezer et al., “Oppositional biogeography-based optimization,”\u00a0Proc. IEEE SMC<\/em>, pp. 1009-1014, 2009.<\/p>

    [35] D. Patel et al., “PSO for feature selection in FER,”\u00a0Proc. IEEE ICIP<\/em>, pp. 2145-2150, 2024.<\/p>

    [36] Z. Zeng et al., “A survey of affect recognition methods,”\u00a0IEEE Trans. PAMI<\/em>, vol. 31, no. 1, pp. 39-58, 2009.<\/p>

    [37] V. Kazemi and J. Sullivan, “One millisecond face alignment,”\u00a0Proc. IEEE CVPR<\/em>, pp.<\/p> <\/div>\n\t\t \n

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    Sharma and A. Kumar, \u201cBridging accuracy and efficiency in facial expression recognition: An adaptive swarm intelligence framework for resource-constrained environments,\u201d IPEM Journal of Computer Application & Research<\/em>, vol. 10, pp. 13\u201319, Dec. 2025, doi:<\/p> <\/div>\n\t\t <\/div>\n <\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

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