Traditional attendance systems employed in educational institutions and workplaces suffer from inherent inefficiencies, including susceptibility to proxy attendance, high administrative overhead, and slow data processing. This paper presents the design and implementation of an automated Facial Recognition Attendance Monitoring System (FRAMS) developed using Java and the OpenCV computer vision library. The proposed system leverages the Haar Cascade Classifier for robust real-time face detection and the Local Binary Pattern Histogram (LBPH) algorithm for accurate face recognition. The architecture integrates a webcam-based image acquisition module, a preprocessing pipeline for noise reduction and face normalization, an LBPH-trained recognition engine, and a MySQL database for persistent attendance storage. Experimental results demonstrate a recognition accuracy of up to 97.4% under optimal lighting conditions, with an average frame processing time of 210 milliseconds. The system effectively eliminates proxy attendance, reduces administrative workload, and enables real-time monitoring without requiring specialized hardware. Evaluation across diverse environmental conditions confirms the system’s robustness, with performance metrics substantially outperforming conventional attendance modalities. This work contributes a practical, cost-effective, and scalable solution to institutional attendance management.