Face Recognition from Partial Face Data

During the spread of the Corona epidemic, everyone started wearing masks as protection in public places. Therefore, this causes a major challenge in authentication and safety systems, such as face recognition systems in railway stations, airports, and payment systems based on facial recognition technologies. Face recognition systems are safer than touch-based biometric systems. However, the face recognition systems are ineffective in the presence of a face with a mask. Therefore, we developed an efficient algorithm using the MTCNN and VGGF model to improve the efficacy of face recognition systems in partially occluded face images. The proposed approach produced 90% accuracy in the top half of the facial images.