Analysis of Various Learning Approaches in Occluded Face Recognition

Face identification is a complex problem for robots, even though it is a simple human activity. There is a high growth of computer power aids in the rapid improvement of facial detection technologies. Several intelligent algorithms have been suggested for facial detection. So far, there's very less focus on thorough examining towards the existing techniques. This work aims to present a comprehensive analysis on face detection systems. First, an extensive review is made over the wide range of existing face recognition techniques, including their origin, working mechanism, advantages, limits, and applications in domains other than face identification. Next, the existing works for feature analysis and other techniques are made. Finally, this study presents an extensive evaluation amongst some of the algorithms exemplified to have an over all-encompassing perspective. Some previously published survey articles on face identification algorithms focused solely on technical specifics and commonly used techniques. However, our study thoroughly discusses face identification strategies and contemporary neural network techniques. This study provides the benefits and disadvantages of the state of art methods and provides a literature study that encompasses their application beyond face detection.