Improved CNN System for Face Mask Recognition

Abstract Deep learning, especially convolutional neural networks, has significantly improved performance in computer vision. Therefore, we designed and developed a modified deep convolutional neural network framework for detecting mask in facial images in a sizable synthesized and un-synthesized face mask dataset. The suggested method can be utilized to detect face masks in any image with a low-resolution, different alignments, complex, and noisy background by tuning the hyperparameters to accurately identify the existence of masks without generating overfitting. The experimentally obtained results demonstrate that the suggested model exhibits a significant efficiency level, achieving 97.39% accuracy, 97.34% precision, 97.41% recall, 97.37% F1-score, and 97.4% AUC. The empirical results have been documented after 35 iterations using optimized hyperparameter settings, and those predictive models were trained on 64,398 images with a 98% accuracy rate and 0.05 loss, proving the proposed work's reliability and robustness.