Innovative Hybrid Approach for Masked Face Recognition Using Pretrained Mask Detection and Segmentation, Robust PCA, and KNN Classifier

Face masks are widely used in various industries and jobs, such as healthcare, food service, construction, manufacturing, retail, hospitality, transportation, education, public safety. Masked face recognition is essential to accurately identify authenticate individuals wearing masks. has emerged a vital technology address this problem enable accurate identification authentication masked scenarios. In paper, we propose novel method that utilizes combination of deep-learning-based mask detection, landmark oval robust principal component analysis (RPCA) for recognition. Specifically, use pretrained ssd-MobileNetV2 detecting the presence location on employ detection key facial features. The proposed also RPCA separate occluded non-occluded components an image, making it more reliable identifying faces with To optimize performance our method, particle swarm optimization (PSO) both KNN features number k KNN. Experimental results demonstrate outperforms existing methods terms accuracy robustness occlusion. Our achieves rate 97%, which significantly higher than state-of-the-art methods. represents significant improvement over recognition, providing high