Adaptive Resonance Theory-Based Approach for Robust and Efficient Face Recognition

Hewa Zangana (IT Dept., Duhok Technical College, Duhok Polytechnic University, Duhok, Iraq)
Ayaz Khalid Mohammed (Computer System Department, Ararat Technical Private Institute, Kurdistan Region – Iraq)
Marwan Omar (Illinois Institute of Technology ,USA)
Firas Mahmood Mustafa (Chemical Engineering Dept., Technical College of Engineering, Duhok Polytechnic University, Duhok, Iraq)
Anik Vega Vitianingsih (Informatics Department, Universitas Dr. Soetomo, Surabaya, Indonesia)

Vokasi UNESA Bulletin of Engineering, Technology and Applied Science
Vol. 2 No. 3 (2025)

Face recognition systems play a crucial role in security, surveillance, and authentication applications. However, traditional deep learning-based models, particularly Convolutional Neural Networks (CNNs), often struggle with issues such as varying lighting conditions, occlusions, and high computational costs. This paper proposes an Adaptive Resonance Theory (ART)-based face recognition framework that enhances recognition robustness and computational efficiency. Unlike CNNs, ART enables incremental learning without requiring retraining, making it suitable for realtime applications. The study evaluated the proposed system on threebenchmark datasets: LFW, Yale, and ORL. Experimental results indicate that the ART-based model achieved an average accuracy of 96.2%, outperforming CNN-based models (93.5%) while reducing recognition time by 25%. Additionally, ART demonstrated superior adaptability, maintaining recognition accuracy above 94% even under occlusion and low-light conditions. These findings confirm the effectiveness of ART-based face recognition for security, access control, and innovative surveillance applications. Future research will focus on integrating ART with deep learning techniques for enhanced performance in large-scale datasets.