Face Recognition Based Real Time Attendance System Using Machine Learning

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Authors : Prof. Ansar Sheikh , Nivedita Sanyal ,Payal Ninawe,Sanjana Waghe,Shruti Waghmare

DOI : 10.46335/IJIES.2024.9.8.19

Abstract— In contemporary educational and workplaces are changing quickly these days, so it's important to have an easier way to track who shows up. This study explores a new system that uses special face-  scanning technology to automatically take attendance.  This technology focus on the implementation of the Haar cascade method, to revolutionize and optimize attendance management protocols. By harnessing the power of computer vision and machine learning, our system can accurately detect and recognize faces in real- time. The Haar-cascade method , known for its robustness and speed, serves as the backbone of our facial recognition algorithm, enabling rapid and accurate identification of individuals within a given dataset. Our system boasts a user-friendly interface, making it accessible and intuitive for both administrators and end-users. Through seamless integration into existing infrastructures, such as educational institutions or corporate environments, our solution offers a hassle-free approach to attendance recording. Furthermore, by automating the identification process, our system significantly reduces the likelihood of manual errors associated with traditional attendance tracking methods. This not only saves valuable time but also enhances overall accuracy and reliability. our attendance system utilizing face recognition technology and the Haar cascade method represents a cutting-edge solution for modern attendance management needs. With its efficiency, reliability, and user-friendly interface, our system promises to revolutionize the way attendance is tracked and managed across various sectors.