Tracking Unauthorized Access Using Machine Learning and PCA for Face Recognition Developments

In the last two decades there has been obtained tremendous improvements in the field of artificial intelligence (AI) especially in the sector of face/facial recognition (FR). Over the years, the world obtained remarkable progress in the technology that enhanced the face detection techniques use on common PCs and smartphones. Moreover, the steadily progress of programming languages, libraries, frameworks, and tools combined with the great passion of developers and researchers worldwide contribute substantially to open-source AI materials that produced machine learning (ML) algorithms available to any scholar with the will to build the software of tomorrow. The study aims to analyze the specialized literature starting from the first prototype delivered by Cambridge University until the most recent discoveries in FR. The purpose is to identify the most proficient algorithms, and the existing gap in the specialized literature. The research builds a FR application based on simplicity and efficiency of code that facilitates a person's face detection using a real time photo and validate the access by querying a given database. The paper brings contribution to the field throughout the literature review analysis as well as by the customized code in Phyton, using ML with Principal Component Analysis (PCA), AdaBoost and MySQL for a myriad of application's development in a variety of domains.