<![CDATA[Automated attendance systems have become a critical component of smart education environments. This study presents a bibliometric analysis of research on facial recognition-based attendance systems to identify research trends, collaboration patterns, and potential directions for future studies. Data were collected from the Scopus database for the period 2019–2024 using keywords related to “facial recognition,” “attendance system,” and “deep learning.” The bibliometric analysis was conducted using OpenRefine for data cleaning and Biblioshiny (R-Bibliometrix) for visualization and mapping of scientific networks, including co-authorship, keyword co-occurrence, and citation analysis. The results show a significant increase in research publications, dominated by contributions from India, Indonesia, and Malaysia, with deep learning and convolutional neural networks (CNN) as the most frequently studied techniques. International collaboration remains limited, indicating opportunities for broader cooperation in this field. This research contributes by providing a comprehensive overview of the global research landscape on facial recognition for attendance systems and offering strategic insights for developing more accurate, efficient, and scalable recognition technologies in educational environments.]]>
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