In the wake of the global health disaster brought on by the globally circulating COVID-19 coronavirus. It is currently a research topic in many fields, especially those interesting such as artificial intelligence and new information technologies. Many regulatory agencies now require wearing face masks, particularly in crowded areas involving regular and large-scale human interaction, like inside overcrowded transit facilities, where everyone must wear masks. It is challenging to identify the identity of a person using conventional facial recognition techniques, so it needs developed technology with high accuracy. The paper presents a new system by utilizing the advanced MobileNetV2 network to recognize the person's identity without the need to take off the face mask. The proposed system has trained by using different eight classes of regular people's faces (without wearing face masks) under diverse environmental conditions. The performance of the proposed system demonstrated high efficiency in identifying the identity of the person accurately up to 100%. The recognition process was achieved using Keras with TensorFlow in terms of accuracy and detection speed.

Face identification, MobileNetV2, Transfer learning (TL) , ImageNet , Confusion Matrix .