<![CDATA[This research presents the development of a facial recognition and verification system that aims to address attendance record falsification in university lectures, a persistent challenge in non-biometric attendance management. The proposed framework integrates the efficiency of YOLOv8n for facial detection with the strong feature representation capability of VGG-Face. The system applies image augmentation to create varied facial embeddings, uses Cosine Similarity for identity verification, and evaluates its performance through accuracy, precision, recall, and F1-Score metrics. Experimental evaluations on student facial datasets captured under different lighting conditions, poses, and viewing angles show that the system achieves a stable accuracy of around 90 % without augmentation, increasing to 97 % with augmentation, which enhances overall stability and reliability. These results demonstrate that the integration of YOLOv8n and VGG-Face offers an effective and dependable solution for strengthening the security and credibility of facial recognition-based attendance systems in academic settings.]]>
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