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Traditional methods of taking attendance are often time-consuming and prone to errors, leading to inefficiencies in academic administration. The Classroom Skipper Recognition System (this project) is an innovative solution designed to streamline attendance management in educational institutions. This project addresses these challenges by harnessing the power of facial recognition technology coupled with advanced algorithms. Initially, this project builds a comprehensive database of students' facial data, where each student's face is captured and stored. This data is then used to train a robust face recognition model, ensuring high accuracy in identifying individuals. During class sessions, instructors input the current time, triggering the system to initiate attendance tracking. Using real-time facial detection, this project analyzes the faces present in the classroom and matches them against the stored database. Students who are recognized as present are marked as such in the attendance records. Moreover, the system periodically reassesses attendance by comparing the current session's detected students with historical data. A key feature of this project is its ability to detect class skippers—students who were present in previous sessions but are absent without valid reasons in the current session.