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.
