An Improved Automatic Face Recognition Attendance System Using Machine Learning Algorithms

The automated attendance system presented in this study is intended to improve and expedite the manual attendance procedure. The suggested solution makes use of cutting-edge machine learning and computer vision techniques to precisely detect and document people's presence in a specified space, like a workplace or classroom. The technology reduces human error and saves time by doing away with manual attendance taking through the use of real-time video analysis and facial recognition algorithms. The fundamental operation of the system is to record live video streams from well positioned cameras, identify and follow faces in the frames, and compare them to an existing database of enrolled people. The technology automatically logs the identified person's attendance after a match is verified, producing an extensive attendance report. For organizations and educational institutions to maintain responsibility and efficiency, effective attendance management is essential. The design and implementation of an automatic attendance system that makes use of cutting-edge technology like biometric authentication, RFID (Radio Frequency Identification), and facial recognition is presented in this study. Time inefficiency, errors, and the possibility of proxy attendance are some of the main issues with manual attendance methods that the system resolves.