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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Kaewchada, Sopee
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Computer Science & IT Engineering

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DualFaceNet: augmentation consistency for optimal facial landmark detection and face mask classification Songsri-in, Kritaphat; Rattaphun, Munlika; Kaewchada, Sopee; Ruang-on, Somporn
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3228-3239

Abstract

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.
Co-Authors Rattaphun, Munlika Ruang-On, Somporn Songsri-in, Kritaphat