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