An Efficient Attention-based Deep Learning Model for Masked and Unmasked Face Recognition

• Proposed the Custom Aug-CNN-5, a CNN-based architecture optimized for recognizing masked and partially occluded faces.
• Integrated attention mechanisms (CBAM) and cosine annealing to improve learning efficiency and enhance discriminative power.
• Conducted a comprehensive evaluation on the larger customized VGGFace2 dataset, demonstrating high accuracy, generalizability, and fairness across varied conditions.