Face recognition systems have gained widespread usage in numerous daily applications, including but not limited to unlocking smartphones, monitoring school attendance, facilitating secure online bank transactions, and enhancing border control measures. The field of face recognition is continuously advancing, with numerous research studies focused on developing innovative theories and approaches.
In recent times, the global COVID-19 pandemic has been rapidly spreading, causing significant repercussions on both public health and the economy. To prevent the spread of viruses, wearing masks in public settings has proven to be an effective measure. Nevertheless, recognizing faces covered by masks poses a formidable challenge due to the limited availability of facial feature information. To recognize masks with faces, a system using CNN extracts features from specific regions of the masked face, such as the eyes, forehead, and eyebrows, and integrates them with the features learned from RetinaFace, a robust face detector. This approach serves as a rapid and efficient solution for masked face recognition.
