A Review of Methodologies in Machine Learning For Face Shape Analysis

Abstract

Face finding and face analysis have been employed as the basis of many researchers in recent years. The outline of an object gives the structure and look of an object. In applications like face recognition, a form of the face acts as a significant role. This paper reviews different methods adopted in different works to frame the shape of the human face that would be useful for many other future works. A list of existing machine learning and deep learning algorithms for face shape analysis is discussed and reviewed. It also discusses the result and accuracy of different methods that act as intermediate steps in achieving the shape of a human face and also a number of suggestions for achieving optimization in the routine of the work. A review of various performance measures such as time taken, error rate, classification rate, and memory are discussed.