A MPSO-AIDED TECHNIQUE FOR ROBUST RECOGNITION USING DEEP CONVOLUTION NEURAL NETWORKS AND ITS APPLICATION IN SOLAR ENERGY SECTOR
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Government College of Technology, Coimbatore, Tamil Nadu, India
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Abstract
This article introduces a robust face recognition technique that leverages the micro-batch particle swarm optimization (MPSO) for optimal parameter selection and deep convolution neural networks which lead to considerable improvement in recognition accuracy and minimization of computation cost. The conventional approaches to recognize the pattern, faced the limitations in estimating the recognition accuracy as there is no knob to fine tune the parameters, hence an additional layer of information is provided to augment the deep convolution neural networks (CNN) with the optimized weights gained from micro-batch particle swarm optimization (MPSO) algorithm. The utilization of micro-batch particle swarm optimization aimed to optimize the end results on convolution neural networks to improve the recognition accuracy. The proposed method has been applied to YALE database and the experimental research can be carried out which gained maximum accuracy with minimal execution time and the final result was one step ahead than conventional methods. These methods can also be applied in Solar Energy sector.
Keywords
Face Recognition Particle Swarm Optimization Deep Convolution Neural Networks Principal Component Analysis
Article Information
PUBLICATION DETAILS
Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 8
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Aug 03 2026
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Mary, M. Q. & Stanley, S. (2026). A MPSO-Aided Technique for Robust Recognition Using Deep Convolution Neural Networks and Its Application in Solar Energy Sector. International Journal of Science, Strategic Management and Technology, 02(8), 1-9. https://doi.org/10.55041/ijsmt.v2i7.110
Mary, M.Blessy, and S.G.Sam Stanley. "A MPSO-Aided Technique for Robust Recognition Using Deep Convolution Neural Networks and Its Application in Solar Energy Sector." International Journal of Science, Strategic Management and Technology, vol. 02, no. 8, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijsmt.v2i7.110.
Mary, M.Blessy, and S.G.Sam Stanley. "A MPSO-Aided Technique for Robust Recognition Using Deep Convolution Neural Networks and Its Application in Solar Energy Sector." International Journal of Science, Strategic Management and Technology 02, no. 8 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijsmt.v2i7.110.
References
[1]Li, Lixiang, et al. "A review of face recognition technology." IEEE access 8 (2020): 139110-139120.
[2] Manjula, V. S., and L. D. S. S. Baboo. "Face detection identification and tracking by PRDIT algorithm using image database for crime investigation." Int. J. Comput. Appl 38.10 (2012): 40-46.
[3]Smith, Marcus, and Seumas Miller. "The ethical application of biometric facial recognition technology." Ai & Society 37.1 (2022): 167-175.
[4]Quinn, George W., Patrick J. Grother, and George W. Quinn. Performance of face recognition algorithms on compressed images. US Department of Commerce, National Institute of Standards and Technology, 2011.
[5]Zhi, Hui, and Sanyang Liu. "Face recognition based on genetic algorithm." Journal of Visual Communication and Image Representation 58 (2019): 495-502.
[6]Wang, Dongshu, Dapei Tan, and Lei Liu. "Particle swarm optimization algorithm: an overview." Soft computing 22.2 (2018): 387-408.
[7]Zhang, Yanhu, and Lijuan Yan. "Face recognition algorithm based on particle swarm optimization and image feature compensation." SoftwareX 22 (2023): 101305.
[8]Chalabi, Nour Elhouda, et al. "Particle swarm optimization based block feature selection in face recognition system." Multimedia Tools and Applications 80.24 (2021): 33257-33273.
[9]Li, YaoChong, et al. "A quantum deep convolutional neural network for image recognition." Quantum Science and Technology 5.4 (2020): 044003.
[10]Wang, Bin, et al. "Evolving deep convolutional neural networks by variable-length particle swarm optimization for image classification." 2018 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2018.
[11]Singh, Pratibha, Santanu Chaudhury, and Bijaya Ketan Panigrahi. "Hybrid MPSO-CNN: Multi-level particle swarm optimized hyperparameters of convolutional neural network." Swarm and Evolutionary Computation 63 (2021): 100863.
[2] Manjula, V. S., and L. D. S. S. Baboo. "Face detection identification and tracking by PRDIT algorithm using image database for crime investigation." Int. J. Comput. Appl 38.10 (2012): 40-46.
[3]Smith, Marcus, and Seumas Miller. "The ethical application of biometric facial recognition technology." Ai & Society 37.1 (2022): 167-175.
[4]Quinn, George W., Patrick J. Grother, and George W. Quinn. Performance of face recognition algorithms on compressed images. US Department of Commerce, National Institute of Standards and Technology, 2011.
[5]Zhi, Hui, and Sanyang Liu. "Face recognition based on genetic algorithm." Journal of Visual Communication and Image Representation 58 (2019): 495-502.
[6]Wang, Dongshu, Dapei Tan, and Lei Liu. "Particle swarm optimization algorithm: an overview." Soft computing 22.2 (2018): 387-408.
[7]Zhang, Yanhu, and Lijuan Yan. "Face recognition algorithm based on particle swarm optimization and image feature compensation." SoftwareX 22 (2023): 101305.
[8]Chalabi, Nour Elhouda, et al. "Particle swarm optimization based block feature selection in face recognition system." Multimedia Tools and Applications 80.24 (2021): 33257-33273.
[9]Li, YaoChong, et al. "A quantum deep convolutional neural network for image recognition." Quantum Science and Technology 5.4 (2020): 044003.
[10]Wang, Bin, et al. "Evolving deep convolutional neural networks by variable-length particle swarm optimization for image classification." 2018 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2018.
[11]Singh, Pratibha, Santanu Chaudhury, and Bijaya Ketan Panigrahi. "Hybrid MPSO-CNN: Multi-level particle swarm optimized hyperparameters of convolutional neural network." Swarm and Evolutionary Computation 63 (2021): 100863.
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