A Machine Learning-Based Attendance Management System

The primary goal of this project is to update and modernize the current system, as well as boost its efficacy and efficiency, by developing an attendance tracking system for educational institutions based on facial recognition technology. Recording and manually inputting attendance logs in a classroom is not the most efficient method. In the current age, some students may find it interesting or creative to skip classes or arrange for proxies for those who are absent. Manually recording attendance in logbooks becomes a challenging and readily manipulable activity. This technology uses facial recognition to automatically identify students in the classroom and record their attendance. In order to construct this system, real-time facial recognition is used. The reference faces in the dataset are compared with the detected faces, marking the participants' attendance The data is trained using the Fisher Face Recognizer algorithm, and faces are detected using the haar-cascade classifier. SMTP is utilized for email transmission and reception, and the Tkinter graphical user interface is employed for input capture. The technology in question is a facial recognition system. One of a person's natural features that can be used to identify them specifically is their face. Faces are used to trace identification since they are unlikely to deviate or be copied. The project comprises creating facial databases in order to enhance the recognizer algorithm's capabilities. During attendance sessions, a systematic comparison of faces against the established database will be conducted to accurately determine individual identities. Upon recognition, the presence of the individual will be automatically documented, and the pertinent information will be saved in an Excel spreadsheet. The excel document with every person's attendance detail is mailed to the relevant professors at the conclusion of the day.