DESIGN OF ENERGY SYSTEMS AND MACHINE LEARNING BASED ON STATISTICAL LOCAL FEATURES AND DYNAMIC RANKING
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1Assistant Professor/Information Technology, Government College of Technology, Coimbatore, Tamil Nadu, India, 2Associate Professor/Department of Mechanical Engineering, PARK College of Engineering and Technology, Coimbatore, Tamil Nadu, India
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Abstract
This paper presents theoretical analyses of face recognition system and Energy systems based on increased kernel representation, Markov classifier and dynamic ranking. The main aim of this work is to minimize the error rate and complexity of recognition techniques in Face Recognition. To achieve this, robust classification techniques can be formulated and evaluated in this research so that to facilitate the recognition of faces in an effective way.This work acts as platform to study the improvement in accuracy and time complexity, when optimization techniques and neural networks are used in Face Recognition. Theoretical analysis of face recognition system with robust kernel representation, Markov network based classifier and dynamic ranking are implemented. Numerical values for recognition accuracy, error rate, system complexity and efficiency are found.
Keywords
Face recognition system Statistical local feature Markov classifier Multi partition Max pooling dynamic ranking
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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 27 2026
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Mary, M. Q. & Stanley, S. (2026). DESIGN OF ENERGY SYSTEMS AND MACHINE LEARNING BASED ON STATISTICAL LOCAL FEATURES AND DYNAMIC RANKING. International Journal of Science, Strategic Management and Technology, 02(8), 1-8. https://doi.org/10.55041/ijsmt.v2i8.089
Mary, M.Blessy, and S.G.Sam Stanley. "DESIGN OF ENERGY SYSTEMS AND MACHINE LEARNING BASED ON STATISTICAL LOCAL FEATURES AND DYNAMIC RANKING." International Journal of Science, Strategic Management and Technology, vol. 02, no. 8, 2026, pp. 1-8. doi:https://doi.org/10.55041/ijsmt.v2i8.089.
Mary, M.Blessy, and S.G.Sam Stanley. "DESIGN OF ENERGY SYSTEMS AND MACHINE LEARNING BASED ON STATISTICAL LOCAL FEATURES AND DYNAMIC RANKING." International Journal of Science, Strategic Management and Technology 02, no. 8 (2026): 1-8. https://doi.org/https://doi.org/10.55041/ijsmt.v2i8.089.
References
[1] Shang-Hung, L, Sun-Yuan, K & Long-Ji, L 1997, 'Face recognition/detection by probabilistic decision-based neural network', IEEE Transactions on Neural Networks, vol. 8, no. 1, pp. 114-132.
[2] Swets, DL &Weng, JJ 1996, 'Using discriminant eigen features for image retrieval', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 8, pp. 831-836.
[3] Yang, M, Zhang, L, Shiu, SCK & Zhang, D 2013, 'Robust kernel representation with statistical local features for face recognition', IEEE Transactions on Neural Networks and Learning Systems, vol. 24, no. 6, pp. 900-912.
[4] Yu, H & Yang, H 2001, 'A direct LDA algorithm for high-dimensional data - with application to face recognition', Pattern Recognition, vol. 34, no. 10, pp. 2067-2070.
[5] Zhao, H & Yuen, PC 2008, 'Incremental Linear Discriminant Analysis for Face Recognition', IEEE Transactions on Systems, Man & Cybernetics: Part B, vol. 38, no. 1, pp. 210-221.
[6] Hwang, W & Kim, J 2015, 'Markov Network-Based Unified Classifier for Face Recognition', IEEE Transactions on Image Processing, vol. 24, no. 11, pp. 4263-4275.
[7] Li, H &Suen, CY 2016, 'Robust face recognition based on dynamic rank representation', Pattern Recognition, vol. 60, pp. 13-24.
[8] Martínez, AM &Kak, AC 2001, 'PCA versus LDA', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 228-233.
[9] Moghaddam, B, Jebara, T &Pentland, A 2000, 'Bayesian face recognition', Pattern Recognition, vol. 33, no. 11, pp. 1771-1782.
[10] Modarresi, K 2015, 'Unsupervised feature extraction using singular value decomposition', Procedia Computer Science, vol. 51, no. 1, pp. 2417-2425.
[2] Swets, DL &Weng, JJ 1996, 'Using discriminant eigen features for image retrieval', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 8, pp. 831-836.
[3] Yang, M, Zhang, L, Shiu, SCK & Zhang, D 2013, 'Robust kernel representation with statistical local features for face recognition', IEEE Transactions on Neural Networks and Learning Systems, vol. 24, no. 6, pp. 900-912.
[4] Yu, H & Yang, H 2001, 'A direct LDA algorithm for high-dimensional data - with application to face recognition', Pattern Recognition, vol. 34, no. 10, pp. 2067-2070.
[5] Zhao, H & Yuen, PC 2008, 'Incremental Linear Discriminant Analysis for Face Recognition', IEEE Transactions on Systems, Man & Cybernetics: Part B, vol. 38, no. 1, pp. 210-221.
[6] Hwang, W & Kim, J 2015, 'Markov Network-Based Unified Classifier for Face Recognition', IEEE Transactions on Image Processing, vol. 24, no. 11, pp. 4263-4275.
[7] Li, H &Suen, CY 2016, 'Robust face recognition based on dynamic rank representation', Pattern Recognition, vol. 60, pp. 13-24.
[8] Martínez, AM &Kak, AC 2001, 'PCA versus LDA', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 228-233.
[9] Moghaddam, B, Jebara, T &Pentland, A 2000, 'Bayesian face recognition', Pattern Recognition, vol. 33, no. 11, pp. 1771-1782.
[10] Modarresi, K 2015, 'Unsupervised feature extraction using singular value decomposition', Procedia Computer Science, vol. 51, no. 1, pp. 2417-2425.
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