Computer vision to attendance recognition: a bibliometric analysis

João Paulo Folador; Jefferson Beethoven Martins; Fábio Henrique Monteiro Oliveira

doi:10.5753/jbcs.2026.6103

Authors

João Paulo Folador Federal University of Paraná https://orcid.org/0000-0001-7766-5930
Jefferson Beethoven Martins Federal Institute of Triângulo Mineiro https://orcid.org/0000-0001-6804-9802
Fábio Henrique Monteiro Oliveira Federal Institute of Brasília https://orcid.org/0000-0002-0344-5801

DOI:

https://doi.org/10.5753/jbcs.2026.6103

Keywords:

Computer Vision, Facial Recognition, Bibliometric Analysis, Attendance Monitoring, Monitored Academic Environment, Deep Learning, CNN

Abstract

As the number of scientific publications continues to grow, their content becomes more extensive and research increasingly fragmented. In this context, scientific mapping becomes an essential practice. Bibliometrics offers valuable indicators that can guide studies and help build a coherent set of relevant works. This study highlights the main bibliometric indicators in the field of computer vision, specifically related to the monitoring and recording of student attendance in academic environments. Additionally, it conceptualises the key aspects of bibliometrics and outlines the study scenario. The metadata were extracted from the Web of Science and Scopus databases, considering all publications up to 2024. The results indicate that India is among the most prolific countries in this domain, and the terms "face recognition" and "computer vision" are the most frequently associated. The paper also presents the most cited articles, prominent authors, and consolidates a list of significant works in the area. Furthermore, the search strategy revealed a limited number of studies addressing spoofing, presentation attack detection (PAD), or liveness detection, suggesting a promising direction for future research in strengthening the reliability of attendance validation systems.

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