Analysis of Face Recognition Methods (International Journal of Innovative Research in Computer Science & Technology, 2021)
Face recognition has emerged as a promising  field in computer-based applications in recent years, owing  to the wide range of applications that it has found in a variety  of fields. Due to the obvious wide variety of variances across  people's faces, face recognition utilizing database photos,  actual data, record images, and sensor photographs is a tough  task. Image segmentation, cognition, and data analysis, to  mention a few areas of research, all have ties to facial  identification. The development of new techniques  associated with face authentication technologies is an  ongoing process that contributes to the development of more  long-lasting face recognition algorithms. Many facial  recognition techniques are often divided into two categories:  feature-based approaches and holistic methods. At the  present, there is a relatively small number of research that  have been conducted that have linked both of these  techniques. Face recognition algorithms have been  developed in large numbers over the course of the past few  decades. The purpose of this article is to provide a systematic  evaluation of a wide variety of facial recognition methods  that are currently on the market. This includes neural  networks, fuzzy-based methodologies, and the eigenvalues  approach, among other things.
Keywords: Algorithms; Face Recognition Techniques; Deep Learning; Neural Networks; Pattern Recognition