Face Recognition: Recent Advancements and Research Challenges

A Review of Face Recognition Technology: In the previous few decades, face recognition has become a popular field in computer-based application development This is due to the fact that it is employed in so many different sectors. Face identification via database photographs, real data, captured images, and sensor images is also a difficult task due to the huge variety of faces. The fields of image processing, pattern recognition, and computer vision are all crucial to face recognition. Almost 20 papers from 2011 to 2021 were analyzed and it was observed that a framework called Deep Learning includes a number of significant algorithms. To get better results, you need to employ distinct network models for different applications (images, speech, and text). A review of major person recognition research is also included which is followed by a detailed description of the most accurate and latest datasets (like CKPlus, DeepFace, ImageNet, etc.) required in face recognition. Further, the properties of appropriate face authentication applications and architectures are discussed. This paper also includes the research challenges that might occur and some potential future works as well. We present a prospective analysis of facial recognition also in this manuscript.