In Institutions and schools, attendance management is a crucial task for faculty to monitor class strength. Traditional methods such as manual entry, biometrics, and RFID-based systems are commonly used, but they are time-consuming and, in the case of biometrics, potentially unhygienic. This paper presents an automated face recognition-based attendance system that utilizes preinstalled CCTV cameras to monitor student presence in real-time. The system employs RetinaFace for face detection and the face_recognition library for face encoding and matching. Known face images are preprocessed to generate face encodings, which are then compared with detected faces in each frame to determine attendance. The proposed system offers accuracy, efficiency, automation, and contactless operation while seamlessly integrating with existing infrastructure. A web interface allows users to start and stop attendance tracking, remove duplicate records, and download attendance logs in CSV format. The system demonstrates its applicability in educational environments by providing a scalable, non-intrusive, and secure solution for automated attendance management.