Literature Survey on Face Recognition of Occluded Faces

In this paper, a study of the recent advancements in occluded face recognition is made. Recently, this has become very relevant due to the outburst of corona virus. Because of the seriousness of the spread of the disease, every person wore a mask, and it became challenging for automated systems to identify the person. So, it became the need of the hour to find new techniques for occluded face detection. In the years 2021 to 2022, many papers have been published in this regard. Researchers all over the world have contributed significantly towards occluded face recognition. Also, the advancement of deep learning has helped researchers a lot in implementing innovative techniques to deal with the challenges in face recognition, especially occluded faces, which is considered one of the most difficult challenges to overcome. This paper intends to push the limits further and encourage researchers to find more innovative techniques for occlusion-independent face recognition. In this paper, the recent publications in this field are studied. This paper gives an insight into different algorithms which use Convolutional Neural Networks, Deep Learning, Attention mechanism, Dictionary representation, Simultaneous segmentation, Joint segmentation and identification, etc., for occluded face detection and recognition. Incorporating these strategies in face recognition has substantially improved the recognition rate, specifically for occluded face recognition. Additionally, the databases for training occluded face recognition algorithms lack adequate data. This paper also gives an insight into the recent databases available for training occluded face recognition algorithms. Also, different methods for obtaining synthesised occluded faces are discussed.