Facial recognition is essential for identification and identity verification, especially used in security and authentication. The COVID-19 pandemic posed a major challenge due to the widespread use of masks, compromising the effectiveness of facial recognition systems. This survey presents a thorough examination of the advancements in masked face recognition. We explore various techniques, including feature extraction from visible facial regions, and the reconstruction of occluded areas using methods such as generative models and transfer learning. This survey aims to provide valuable insights for overcoming occlusion challenges and advancing the development of effective biometric systems for recognizing masked faces.