A Comparative Study of CFs, LBP, HOG, SIFT, SURF, and BRIEF Techniques for Face Recognition

In recent years, developing Surveillance Systems (SS) for security has been one of the most active research fields in most applications. These systems used to adjust, enhance, and improve the security. One of these systems, face recognition system plays an efficient and very important tool for several applications despite the existence of different surveillance systems, like hand geometry, iris scan, as well as fingerprints. This is because it is natural, non-intrusive, and inexpensive. For the past two decades, various face recognition methods have been proposed to reduce the amount of calculation and improve the recognition rate. These proposed methods could be categorized into three significant categories: Local feature approaches, Subspace learning approaches, and Correlation filters approaches. In this paper, we discuss and compare some common face recognition algorithms. Our objective of this work is to demonstrate the effectiveness and feasibility of the best methods for face recognition in terms of design, implementation, and application.