Adaptive Resonance Theory-Based Approach for Robust and Efficient Face Recognition
DOI:
https://doi.org/10.26740/vubeta.v2i3.38709Keywords:
Adaptive Resonance Theory, Computational Efficiency, Face Recognition , Real-Time Applications, RobustnessAbstract
In recent years, face recognition systems have gained significant traction due to their applications in security, surveillance, and user authentication. Despite the advances in deep learning techniques, challenges such as varying lighting conditions, occlusions, and facial expressions continue to affect the robustness and efficiency of these systems. This paper proposes a novel approach to face recognition based on Adaptive Resonance Theory (ART). ART's ability to adaptively learn and recognize patterns in a stable and incremental manner makes it particularly suitable for handling the dynamic variations encountered in face recognition tasks. Our proposed ART-based face recognition framework is evaluated on multiple benchmark datasets, demonstrating superior performance in terms of accuracy, robustness to noise, and computational efficiency compared to traditional methods. The experimental results highlight the potential of ART to enhance the reliability of face recognition systems in real-world applications.
Author Biographies
Hewa Majeed Zangana, IT Dept., Duhok Technical College, Duhok Polytechnic University, Duhok, Iraq
Hewa Majeed Zangana is an Assistant Professor at Duhok Polytechnic University (DPU) in Iraq, currently pursuing a PhD in ITM at DPU. He has previously served as an Assistant Professor at Ararat Private Technical Institute and a Lecturer at Amedi Technical Institute and Nawroz University. His administrative roles include Curriculum Division Director at DPU and Acting Dean of the College of Computer and IT at Nawroz University. His research interests cover network systems, information security, and intelligent systems. He has published in peer-reviewed journals such as IEEE and serves on various editorial boards and scientific committees, also published multiple books indexed in Scopus with IGI Global.
Ayaz Khalid Mohammed , Computer System Department, Ararat Technical Private Institute, Kurdistan Region – Iraq
Ayaz Khalid Mohammed is an Assistant Lecturer with a Master's in Computer Information Systems from Near East University and a Bachelor's in Computer Science from Nawroz University. He currently serves as the Head of the Computer Systems Department at Ararat Private Technical Institute, bringing his expertise and dedication to the field of computer science and information systems.
Marwan Omar , Illinois Institute of Technology ,USA
Marwan Omar is an Associate Professor of Cybersecurity and Digital Forensics at the Illinois Institute of Technology. He holds a Doctorate in Computer Science specializing in Digital Systems Security from Colorado Technical University and a Post-Doctoral Degree in Cybersecurity from the University of Fernando Pessoa, Portugal. Dr. Omar's work focuses on cybersecurity, data analytics, machine learning, and AI in digital forensics. His extensive research portfolio includes numerous publications and over 598 citations. Known for his industry experience and dedication to teaching, he actively contributes to curriculum development, preparing future cybersecurity experts for emerging challenges.
Firas Mahmood Mustafa, Chemical Engineering Dept., Technical College of Engineering, Duhok Polytechnic University, Duhok, Iraq
Firas Mahmood Mustafa holds a Ph.D. in Computer Engineering from Mosul University, Iraq. His academic journey began with a B.Sc. in Electrical Engineering (Electronics and Communication), graduating in the top quarter of his class. He earned an M.Sc. in Computer Engineering from Mosul University in 2000. Mustafa joined the Computer Science Department at AlHadba University in 2003 and completed his Ph.D. in 2007. From 2013 to 2017, he was with DPU University, and from 2017 to 2020, he chaired the CCE Department at Nawroz University. An active participant in Erasmus+ and IREX programs, he now teaches at DPU University, shaping future computer engineering professionals.
Anik Vega Vitianingsih , Informatics Department, Universitas Dr. Soetomo, Surabaya, Indonesia
Anik Vega Vitianingsih is a dedicated academic and professional in the field of Computer Engineering, currently employed at Universitas Dr. Soetomo in Surabaya, Indonesia, since March 2004. She holds a Master's degree in the same field from the same institution, enhancing her expertise in technology and systems development. Anik has contributed significantly to the academic community through various research articles, including her recent work on a recommendation system for determining the best banner supplier, utilizing profile matching and TOPSIS methods, published in the journal INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi. Additionally, she co-authored a study on a guidance information system for final projects using the Iconix process model, published in Jurnal Sistem Informasi Bisnis. Her contributions reflect a commitment to advancing knowledge and practical applications in computer engineering.
References
[1] J. Seiffertt, “Adaptive Resonance Theory in the Time Scales Calculus,” Neural Networks, vol. 120, pp. 32–39, 2019. https://doi.org/10.1016/j.neunet.2019.08.010
[2] G. Bartfai, “An Adaptive Resonance Theory-based Neural Network for Autonomous Learning via Iterative Knowledge Redescription,” International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1–8, 2021. https://doi.org/10.1109/IJCNN52387.2021.9533351
[3] H. Jahani Fariman, S. A. Ahmad, M. Hamiruce Marhaban, M. Alijan Ghasab, and P. H. Chappell, “Hand Movements Classification for Myoelectric Control System using Adaptive Resonance Theory,” Australasian Physical & Engineering Sciences in Medicine, vol. 39, pp. 85–102, 2016. https://doi.org/10.1007/s13246-015-0399-5
[4] F. H. Chiew, C. K. Ng, K. C. Chai, and K. M. Tay, “A Fuzzy Adaptive Resonance Theory‐Based Model for Mix Proportion Estimation of High‐Performance Concrete,” Computer‐Aided Civil and Infrastructure Engineering, vol. 32, no. 9, pp. 772–786, 2017. https://doi.org/10.1111/mice.12288
[5] L. Settipalli, G. R. Gangadharan, and U. Fiore, “Predictive and Adaptive Drift Analysis on Decomposed Healthcare claims using ART based Topological Clustering,” Information Processing & Management, vol. 59, no. 3, p. 102887, 2022. https://doi.org/10.1016/j.ipm.2022.102887
[6] N. Masuyama, Y. Nojima, C. K. Loo, and H. Ishibuchi, “Multi-label Classification Based on Adaptive Resonance Theory,” IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1913–1920, 2020. https://doi.org/10.1109/SSCI47803.2020.9308356
[7] X. Cao, J. Yang, Y. Gao, Q. Wang, and D. Shen, “Region-adaptive Deformable Registration of CT/MRI Pelvic Images via Learning-Based Image Synthesis,” IEEE Transactions on Image Processing, vol. 27, no. 7, pp. 3500–3512, 2018. https://doi.org/10.1109/TIP.2018.2820424
[8] H. M. Zangana and I. F. Al-Shaikhli, “A New Algorithm for Human Face Detection Using Skin Color Tone,” IOSR Journal Computer Engineering, vol. 11, no. 6, pp. 31–38, 2013. https://doi.org/10.9790/0661-1163138
[9] H. M. Zangana, “A New Skin Color Based Face Detection Algorithm by Combining Three Color Model Algorithms,” IOSR Journal Computer Engineering, vol. 17, pp. 6–125, 2015.
[10] X. Jiang, J. Ma, G. Xiao, Z. Shao, and X. Guo, “A review of Multimodal Image Matching: Methods and Applications,” Information Fusion, vol. 73, pp. 22–71, 2021. https://doi.org/10.1016/j.inffus.2021.02.012
[11] X. Yan and Y. Wang, “Multi-objective Lung Image Detection Method Based on Self-Regulating Sparse Representation,” Cyber Security Intelligence and Analytics: International Conference on Cyber Security Intelligence and Analytics (CSIA2021), vol. 1, pp. 777–784, 2021. https://doi.org/10.1007/978-3-030-70042-3_110
[12] L. Li, V. Muneeswaran, S. Ramkumar, G. Emayavaramban, and G. R. Gonzalez, “Metaheuristic FIR Filter with Game Theory-Based Compression Technique-A Reliable Medical Image Compression Technique for Online Applications,” Pattern Recognition Letters, vol. 125, pp. 7–12, 2019. https://doi.org/10.1016/j.patrec.2019.03.023
[13] S. Mukherjee and A. Das, “Vague Set Theory Based Segmented Image Fusion Technique for Analysis Of Anatomical And Functional Images,” Expert Systems with Applications, vol. 159, p. 113592, 2020. https://doi.org/10.1016/j.eswa.2020.113592
[14] G. Yasmin, A. K. Das, J. Nayak, S. Vimal, and S. Dutta, “A Rough Set Theory and Deep Learning-Based Predictive System for Gender Recognition Using Audio Speech,” Soft computing, pp. 1–24, 2022. https://doi.org/10.21203/rs.3.rs-915201/v1
[15] N. Masuyama, Y. Nojima, C. K. Loo, and H. Ishibuchi, “Multi-Label Classification via Adaptive Resonance Theory-Based Clustering,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 7, pp. 8696–8712, 2022. https://doi.org/10.1109/TPAMI.2022.3230414
[16] Y. Zhan, Z. Peng, G. Hermosillo, and X. S. Zhou, “Robust Multi-Landmark Detection Based on Information Theoretic Scheduling,” Medical Image Recognition, Segmentation and Parsing, pp. 45–70, 2016. https://doi.org/10.1016/B978-0-12-802581-9.00003-2
[17] Y. Zhao, T. Li, X. Zhang, and C. Zhang, “Artificial intelligence-based Fault Detection and Diagnosis Methods for Building Energy Systems: Advantages, Challenges and The Future,” Renewable and Sustainable Energy Reviews, vol. 109, pp. 85–101, 2019. https://doi.org/10.1016/j.rser.2019.04.021
[18] C. Wang, H. Fan, and T. Wu, “Novel Rough Set Theory-Based Method for Epistemic Uncertainty Modeling, Analysis and Applications,” Applied Mathematical Modelling, vol. 113, pp. 456–474, 2023. https://doi.org/10.1016/j.apm.2022.09.002
[19] Q. Yao, S. Yang, Q. Shao, C. Bian, and M. Wu, “Topological Clustering Particle Swarm Optimizer Based on Adaptive Resonance Theory for Multimodal Multi-Objective Problems,” Information Sciences, p. 121106, 2024. https://doi.org/10.1016/j.ins.2024.121106
[20] N. Sevcenko, T. Appel, M. Ninaus, K. Moeller, and P. Gerjets, “Theory-based Approach for Assessing Cognitive Load During Time-Critical Resource-Managing Human–Computer Interactions: An Eye-Tracking Study,” Journal on Multimodal User Interfaces, vol. 17, no. 1, pp. 1–19, 2023. https://doi.org/10.1007/s12193-022-00398-y
[21] W. Zhu, X. Ma, X.-H. Zhu, K. Ugurbil, W. Chen, and X. Wu, “Denoise Functional Magnetic Resonance Imaging with Random Matrix Theory Based Principal Component Analysis,” IEEE Transactions on Biomedical Engineering, vol. 69, no. 11, pp. 3377–3388, 2022. https://doi.org/10.1109/TBME.2022.3168592
[22] P. Das, A. K. Das, J. Nayak, D. Pelusi, and W. Ding, “Group Incremental Adaptive Clustering Based on Neural Network and Rough Set Theory for Crime Report Categorization,” Neurocomputing, vol. 459, pp. 465–480, 2021. https://doi.org/10.1016/j.neucom.2019.10.109
[23] Z. Roozbehi, A. Narayanan, M. Mohaghegh, and S.-A. Saeedinia, “Dynamic-Structured Reservoir Spiking Neural Network in Sound Localization,” IEEE Access, 2024. https://doi.org/10.1109/ACCESS.2024.3360491
[24] M. S. Alam, C. Yakopcic, G. Subramanyam, and T. M. Taha, “Memristor Based Neuromorphic Adaptive Resonance Theory for One-Shot Online Learning and Network Intrusion Detection,” International Conference on Neuromorphic Systems, pp. 1–8, 2020. https://doi.org/10.1145/3407197.3407608
[25] A. Brahma and S. Panigrahi, “Role-based Profiling using Fuzzy Adaptive Resonance Theory for Securing Database Systems,” International Journal of Applied Metaheuristic Computing (IJAMC), vol. 12, no. 2, pp. 36–48, 2021. https://doi.org/10.4018/IJAMC.2021040103
[26] A. Barton, E. Volna, and M. Kotyrba, “Control of Autonomous Robot Behavior Using Data Filtering Through Adaptive Resonance Theory,” Vietnam Journal of Computer Science, vol. 5, pp. 85–94, 2018. https://doi.org/10.1007/s40595-017-0103-7
[27] V. Bargsten and F. Kirchner, “Actuator-level Motion and Contact Episode Learning and Classification Using Adaptive Resonance Theory,” Intelligent Service Robotics, vol. 16, no. 5, pp. 537–548, 2023. https://doi.org/10.1007/s11370-023-00481-7
[28] F. Leconte, F. Ferland, and F. Michaud, “Fusion Adaptive Resonance Theory Networks Used as Episodic Memory for an Autonomous Robot,” Artificial General Intelligence: 7th International Conference, pp. 63–72, 2014. https://doi.org/10.1007/978-3-319-09274-4_7
[29] A. Tripathi, K. K. Singh, G. Srivastava, and P. K. Maurya, Analysis of Adaptive Resonance Theory for Diagnostic Understanding of Epilepsy. Book Rivers, 2022.
[30] H. Jahani Fariman, S. A. Ahmad, M. Hamiruce Marhaban, M. Alijan Ghasab, and P. H. Chappell, “Hand Movements Classification for Myoelectric Control System using Adaptive Resonance Theory,” Australas Phys Eng Sci Med, vol. 39, pp. 85–102, 2016. https://doi.org/10.1007/s13246-015-0399-5
[31] H. H. Jobaneh, “Fingerprint Recognition Using Markov Chain and Kernel Smoothing Technique with Generalized Regression Neural Network and Adaptive Resonance Theory with Mapping,” Machine Learning Research, vol. 4, no. 1, pp. 7–12, 2019. https://doi.org/10.11648/j.mlr.20190401.12
[32] L. Meng, A.-H. Tan, and D. Wunsch, Adaptive resonance theory in social media data clustering. Springer, 2019. https://doi.org/10.1007/978-3-030-02985-2
[33] I. Sucholutsky and M. Schonlau, “ConvART: Improving Adaptive Resonance Theory for Unsupervised Image Clustering,” Proceedings of CVIS, vol. 4, no. 1, 2018. https://doi.org/10.15353/jcvis.v4i1.326
[34] D. Buhanov, S. Chernikov, V. Polyakov, and M. Panchenko, “Detection of Video Image Modification Using a Classifier Based on Adaptive Resonance Theory,” International Scientific and Practical Conference on Information Technologies and Intelligent Decision-Making Systems, pp. 23–32, 2022. https://doi.org/10.1007/978-3-031-31353-0_3
[35] S. Zhang, F. Huang, B. Liu, H. Yu, and Y. Chen, “Infrared Dim Target Detection Method Based on the Fuzzy Accurate Updating Symmetric Adaptive Resonance Theory,” Journal of Visual Communication and Image Representation, vol. 60, pp. 180–191, 2019. https://doi.org/10.1016/j.jvcir.2019.02.018
[36] R. Dmitriy, “Developing a Modified Neural Network of Adaptive Resonance Theory for Solving Image Recognition Problems,” 2016.
[37] O. I. Al-Sanjary, A. A. Ahmed, A. A. Bin Jaharadak, M. A. M. Ali, and H. M. Zangana, “Detection Clone an Object Movement using an Optical Flow Approach,” IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), pp. 388–394, 2018. https://doi.org/10.1109/ISCAIE.2018.8405504
[38] S. Grossberg, “Adaptive Resonance Theory: How a Brain Learns to Consciously Attend, Learn, and Recognize A Changing World,” Neural networks, vol. 37, pp. 1–47, 2013. https://doi.org/10.1016/j.neunet.2012.09.017
[39] G. Carpenter and S. Grossberg, “Adaptive Resonance Theory,” Boston University Center for Adaptive Systems and Department of Cognitive, 1998.
[40] L. E. B. da Silva, N. Rayapati, and D. C. Wunsch, “iCVI-ARTMAP: Using Incremental Cluster Validity Indices and Adaptive Resonance Theory Reset Mechanism to Accelerate Validation and Achieve Multiprototype Unsupervised Representations,” IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 12, pp. 9757-9770, 2022. https://doi.org/10.1109/TNNLS.2022.3160381
[41] F. Pourpanah, C. P. Lim, A. Etemad, and Q. M. J. Wu, “An Ensemble Semi-Supervised Adaptive Resonance Theory Model with Explanation Capability for Pattern Classification,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 8, no. 1, pp. 814-827, 2023. https://doi.org/10.1109/TETCI.2023.3285932
[42] P. Tiwari, P. Mishra, U. Singh, and R. Itare, “New Adaptive Resonance Theory Based Intrusion Detection System,” Second International Conference on Computer Networks and Communication Technologies: ICCNCT 2019, pp. 745–754 2020. https://doi.org/10.1007/978-3-030-37051-0_84
[43] S. Rizvi, T. Flock, T. Flock, and I. Williams, “Anomaly Detection to Protect Networks from Advanced Persistent Threats Using Adaptive Resonance AI Concepts,” International Conference on Software Security and Assurance (ICSSA), pp. 60–65, 2020. https://doi.org/10.1109/ICSSA51305.2020.00018
[44] W. Wu, Y. Hu, K. Xu, L. Qin, and Q. Yin, “Self-Organizing Memory Based on Adaptive Resonance Theory for Vision and Language Navigation,” Mathematics, vol. 11, no. 19, p. 4192, 2023. https://doi.org/10.3390/math11194192
[45] A. Mishchenko, “FIART: Adaptive Resonance Model of Feature Integration, Proto-Objects Formation and Coherence Theory of Visual Attention,” Biologically Inspired Cognitive Architectures, vol. 21, pp. 13–25, 2017. https://doi.org/10.1016/j.bica.2017.07.001
[46] C. Zhang, C. Jiang, and Q. Xu, “Pretrained Back Propagation Based Adaptive Resonance Theory Network for Adaptive Learning,” Journal of Algorithms & Computational Technology, vol. 17, p. 17483026231205008, 2023. https://doi.org/10.1177/17483026231205009
[47] A. Tripathi, G. Srivastava, K. K. Singh, and P. K. Maurya, “Review of Unsupervised Adaptive Resonance Theory,” International Conference on Cloud Computing, Data Science & Engineering (Confluence), pp. 409–415, 2019. https://doi.org/10.1109/CONFLUENCE.2019.8776941
[48] H. M. Zangana and F. M. Mustafa, “Hybrid Image Denoising Using Wavelet Transform and Deep Learning,” EAI Endorsed Transactions on AI and Robotics, EAI Endorsed Transactions on AI and Robotics, 2024. https://doi.org/10.4108/airo.7486
[49] H. Majeed, “A New Algorithm for Shape Detection,” Department of Computer Science/College, 2017.
[50] N. Masuyama, T. Takebayashi, Y. Nojima, C. K. Loo, H. Ishibuchi, and S. Wermter, “A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning,” arXiv preprint arXiv:2305.01507, 2023. https://doi.org/10.1109/ACCESS.2022.3186479
[51] L. Wang, H. Zhu, J. Meng, and W. He, “Incremental Local Distribution-Based Clustering Using Bayesian Adaptive Resonance Theory,” IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 11, pp. 3496–3504, 2019. https://doi.org/10.1109/TNNLS.2019.2919723
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Hewa Majeed Zangana
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Abstract views: 56
,
PDF Downloads: 41
