Computer vision and pattern recognition technologies have made a significant advancement in the recent past. A key application of this is face recognition. Its applications resonate in many fields. Various methodologies have been developed for this, and it is essential to compare, contrast and review them on the basis of various factors and aspects to find the optimal methodology. The journey of face recognition technology has progressed rapidly from traditional methods to deep learning applications. Yet, it is necessary to survey all these methods and critically and comprehensively evaluate them, as a majority of them are still limited to variation constraints such as lighting, expressions and orientation and other practical complexities. The objective of this paper is to examine, evaluate and illustrate the strengths and limitations of a range of papers by delving into various distinct yet comparable specifics of each methodology of implementing face recognition systems. The motive is to also culminate the essence of these methodologies to facilitate an insightful perspective and overview of the methodologies in a concise manner.