A Survey Paper on Automation of Attendance System through Facial Recognition

Abstract

Abstract—In recent times almost, everything around us is being automated in some way just to make life easier for us. With the use of machine learning and deep learning we can allow non- living objects to program themselves to self-learn from the available data and perfect itself in the process. The process of recording attendance in educational institutions and workplaces has traditionally relied on manual methods, which are time- consuming, error-prone, and lack the level of security and accuracy desired in today’s digital age. This research presents a technological solution to these challenges. This technology offers a range of compelling advantages. It eliminates the need for manual attendance tracking, reducing the potential for errors and administrative workload. Moreover, it enhances security by ensuring that only authorized individuals can mark their attendance. Real-time data is available for monitoring and generating attendance reports, offering valuable insights into punctuality and attendance patterns. This research provides a comprehensive overview of the system’s architecture, algorithms, and the underlying technology, along with a discus- sion of the ethical and privacy considerations associated with face recognition. By implementing this innovative technology, educa- tional institutions and organizations can streamline attendance management, increase operational efficiency, and contribute to a more secure and productive environment. Keywords: Face recognition, LBPH, Haar-cascade, face detection, Viola n Jones, openCV, Adaboost