Deep learning and edge computing are the emerging technologies, which are used for efficient processing of huge amounts of data with distinct accuracy. In this world of advanced information systems, one of the major issues is authentication. Several techniques have been employed to solve this problem. Face recognition is considered as one of the most reliable solutions. Usually, for face recognition, scale-invariant feature transforms (SIFT) and speeded up robust features (SURF) have been used by the research community. The main purpose of this project is to build a face recognition-based attendance monitoring system for educational institutions to enhance and upgrade the current attendance system into more efficient and effective as compared to before. The current old system has a lot of ambiguity that causes inaccurate and inefficient attendance taking. Many problems arise when the authority is unable to enforce the regulations that exist in the old system. Thus, by means of technology, this project will resolve the flaws existing in the current system while bringing attendance to a whole new level by automating most of the tasks. Face recognition technology has a slight edge on other biometric systems like finger-print, palm-print and iris due to its non-contact process. Face recognition system is also able to recognize the person from a distance without touching or any interaction with the person. Moreover, the face recognition system also helps in crime deterrent purposes, because the captured image can be stored in a repository and later can be helpful in many ways like to identify a person. Currently, face recognition applications are deployed in social media websites like Facebook, in the entrance of Airports, Railways Stations, Bus Stop, highly secured areas, advertisement, and health care. The purpose of these applications is to minimize criminal activities, fake authentication, tracking addictive gamblers in casinos, whereas Facebook is using a face recognition system for automatic tagging purposes. For face recognition purposes, there is a need for large data sets and complex features to uniquely identify the different subjects by manipulating different obstacles like illumination, pose and aging.