Replenishing the Facial Features Behind the Mask Using Unet

Replenishing the facial features behind the mask using U-Net is an innovative solution addressing the challenges posed by the widespread use of face masks. Leveraging state-of-the-art technologies, the system employs Haar cascade classifiers to identify faces, distinguishes masked and unmasked faces through a specialized convolutional neural network, and employs a U-Net model for real-time mask segmentation and completion. Our project aims to enhance personal connections and restore face recognition in an authentication scenario. It contributes a seamless and effective unmasking experience in real-world scenarios.