Diagnosis-Aware Real-Time Video Face Recognition Model (DART- VFR)

Abstract

Facial recognition technologies have gained much progress along the years, however, over-the traditional methods, they still have problem when dealing with a dynamic environment and are not able to adapt contextual information. In this paper, we propose a new diagnosis-aware real-time video face recognition framework, termed DART-VFR to deal with the challenges mentioned above. The proposed pipeline signature includes additional knowledge-metadata -like demographic, behavior, and environmental data - by means of a Bayesian inference approach with adaptive threshold. DART-VFR uses deep learning-based feature extraction and contextual fusion for enhancing recognition performance and robustness. The model has accuracy of 98.7% with 65.3% reduction in false positives. Other metrics like precision 92%, recall 90% and average latency below 100ms imply that the model is fit for deployment on edge devices. Experimental results on hybrid datasets indicate that DART-VFR achieves better accuracy, flexibility, and efficiency than state-of-the-art methods. These results emphasize that the system has the potential to be used in real time, such as in healthcare, surveillance, and similar sensitive context, in which the context-aware, and ethically-aligned FR are important.

Author Biography

Hasan A. Abdulla, Northern Technical University

Hasan serves as an Assistant Lecturer in the Department of Applied Mechanical Engineering Technologies at Technical college of Mousl at Northern Technical University (NTU). He is also involved in administrative work as the Head of the Statistics Unit in the Department of Studies and Planning. Hasan is a member of the teaching staff at the College of Technical Engineering in Mosul.In addition, Hasan Abdulelah Abdullah alsarraf is a PhD student at University Sains Malaysia (USM), specializing in artificial intelligence and machine learning. His current research focuses on developing a hybrid AI model for drug-target interaction prediction, contributing to advancements in computational drug discovery.Hasan earned his Master’s degree in Computer Engineering from Northern Technical University, College of Technical Engineering, Mosul, in 2021. He also holds a Bachelor’s degree in Computer Engineering Technologies from Northern Technical University, College of Technical Engineering, Mosul, in 2007. His academic journey has equipped him with expertise in AI, deep learning, and security applications.An active researcher, Hasan has published 4 scientific papers in various fields within his area of expertise, contributing valuable insights to the scientific community. His work extends to recognition systems, machine learning, and quantum computing, with a focus on medical image analysis and AI-driven security solutions. Also participated in numerous conferences and has reviewed 124 scientific papers within prestigious global research fields. His involvement in academic review and research continues to make significant contributions to the advancement of AI and related technologies.

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Authors

  • Hasan A. Abdulla Northern Technical University
  • Luluwah A. Y. Al-Hbetiw
  • Mazin N. Farhan

DOI:

https://doi.org/10.31449/inf.v49i20.9734

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Published

12/14/2025

How to Cite

Abdulla, H. A., Al-Hbetiw, L. A. Y., & Farhan, M. N. (2025). Diagnosis-Aware Real-Time Video Face Recognition Model (DART- VFR). Informatica, 49(20). https://doi.org/10.31449/inf.v49i20.9734

Issue

Vol. 49 No. 20 (2025): Online-only issue

Section

Online-only

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