Smart attendance recording application using deep facial recognition in group photos

Abstract: This project proposes a model to build an efficient attendance recording application that uses advanced deep learning techniques for face recognition. This project aims to reduce the time-consuming traditional attendance recording method and avoid errors such as manual errors and proxying. Although this attendance registration system may already exist, they operate in a way that uses individual student images. The program aims to use a group photo of students to identify each student and create an Excel file that professors can use to record attendance. Using advanced deep learning techniques and a native-react developed application, the application is trained to achieve maximum accuracy through a well-designed, easy-to-use graphical user interface application that would be deployed in the Play Store, allowing users to interact with it. The research involved studying and analyzing different works by different researchers, how they designed and created an application, and how we can improve it, simplifying the registration process of attendees and setting a new standard of accuracy and convenience in the attendance management system.

Keywords:  Face Detection, Deep learning technique, Deep Face, Face recognition, Android application, VGG.