Version 1
: Received: 19 August 2024 / Approved: 20 August 2024 / Online: 20 August 2024 (12:51:22 CEST)
How to cite:
JAGLI, D. S.; Nakirekanti, L.; Dhanikonda, S. I.; Nalla, S.; Theakadayi, L. M. Advancements in Facial Recognition for Automated Attendance
Management: A Comprehensive OpenCV-based Approach. Preprints2024, 2024081472. https://doi.org/10.20944/preprints202408.1472.v1
JAGLI, D. S.; Nakirekanti, L.; Dhanikonda, S. I.; Nalla, S.; Theakadayi, L. M. Advancements in Facial Recognition for Automated Attendance
Management: A Comprehensive OpenCV-based Approach. Preprints 2024, 2024081472. https://doi.org/10.20944/preprints202408.1472.v1
JAGLI, D. S.; Nakirekanti, L.; Dhanikonda, S. I.; Nalla, S.; Theakadayi, L. M. Advancements in Facial Recognition for Automated Attendance
Management: A Comprehensive OpenCV-based Approach. Preprints2024, 2024081472. https://doi.org/10.20944/preprints202408.1472.v1
APA Style
JAGLI, D. S., Nakirekanti, L., Dhanikonda, S. I., Nalla, S., & Theakadayi, L. M. (2024). Advancements in Facial Recognition for Automated Attendance
Management: A Comprehensive OpenCV-based Approach. Preprints. https://doi.org/10.20944/preprints202408.1472.v1
Chicago/Turabian Style
JAGLI, D. S., Subhashchandra Nalla and Leon Mathew Theakadayi. 2024 "Advancements in Facial Recognition for Automated Attendance
Management: A Comprehensive OpenCV-based Approach" Preprints. https://doi.org/10.20944/preprints202408.1472.v1
Abstract
Abstract: Abstract: In the present era, there is no longer a necessity to rely on outdated and
laborious methods for attendance management in the field of education. Managing attendance
for large groups of students in a classroom can be a challenging and time-consuming task when
it comes to recording data. Fortunately, modern technologies offer us the opportunity to
streamline attendance management and make it more efficient. One effective approach is
Real-Time Face Recognition, which simplifies the process of tracking attendance for numerous
students daily. We employ the Haar cascade classifier to identify positive and negative facial
attributes, along with the LBPH algorithm for accurate face recognition. These techniques are
implemented using Python programming language and the OpenCV library while
incorporating a user-friendly Tkinter GUI (Graphical User Interface) for seamless usability. By
embracing these advancements, we can revolutionize how attendance is managed in
educational environments.
Keywords
Facial Recognition; Facial Detection; Local Binary Pattern Histogram (LBPH); Graphical User Interface (GUI); Student Attendance
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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