Today, with the growth of information technology, face recognition is a challenging issue in the image and vision analysis of computers, and for this reason, in many years, many attention has been considered for many applications in different domains. There are many met
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Today, with the growth of information technology, face recognition is a challenging issue in the image and vision analysis of computers, and for this reason, in many years, many attention has been considered for many applications in different domains. There are many methods for implementing this technology, but the general method based on the comparison of certain characteristics of the faces of individuals with a database or pre-stored information set (which can be sampled from the sampling Be the faces of people). Biometric-based technologies have been recognized in recent years as the most promising option for identifying individuals. Different methods are used in order to implement facial diagnosis. In this paper, a review of some of the well-known image processing methods is performed and the advantages and disadvantages of the designs listed in it have been investigated. Also, the implementation of facial diagnostic systems is introduced. Then, face diagnostic algorithms are categorized and introduced based on biometric characteristics. In addition, while introducing hierarchical and X model algorithms, binary and x hierarchical model, the concept of deep face recognition structure has been addressed and some of the latest algorithms produced for this purpose. In the end, some of the most important applications of facial diagnostic systems have been studied. The purpose of this article is to introduce and express deep learning algorithms in face recognition and expression of existing challenges.
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