Abstract
<p>In an era where face masks are commonplace, facial recognition faces new challenges and opportunities. This study introduces DualFaceNet (DFN), a cutting-edge neural network that efficiently combines facial landmark detection with mask classification. Benefiting from multi-task learning (MTL) and enhanced with a unique consistency loss, DFN outperforms traditional single-task models. Tests using the reputable 300W dataset and a face mask dataset showcase DFN’s strengths: a significant reduction in landmark error to 5.42 and an increase in mask classification accuracy to 92.59%. These results highlight the potential of integrating MTL and custom loss functions in facial recognition. As face masks continue to be globally essential, DFN’s integrated approach offers a fresh perspective in facial recognition studies. Furthermore, DFN paves the way for adaptive facial recognition systems, emphasizing the adaptability needed in our current era.</p>
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