Authors :
Priyanka Manke; Mohammed Hamza Siddiqui; Himanshu Pednekar; Pawan Sakat; Qureshi Abdul Qadir
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/yc43kpne
Scribd :
https://tinyurl.com/44e78sd5
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR1482
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Facial recognition stands as one of the most
efficient applications in image processing, playing a
crucial role in the technical sphere. Identifying human
faces is a pressing concern, particularly in verifying
student attendance. Utilizing facial biostatistics, an
attendance system employing face recognition relies on
high-resolution monitoring and advanced computer
technologies. The objective of developing this system is to
digitize the traditional method of attendance-taking,
which involves verbal calls and manual record-
keeping.Current attendance procedures are laborious
and time-consuming, prone to manipulation through
manual recording. Both traditional attendance marking
and existing biometric systems are susceptible to
fraudulent proxies. This paper aims
toaddressthesechallenges. The proposed system
incorporates the Haar cascade algorithm, OpenCV, Dlib,
Pandas, and MySQL. Following facial recognition,
attendance reports are generated and saved in Excel
format. The system undergoes testing under different
conditions, such as variations in illumination, head
movements, and changes in camera-to-student distance.
Rigorous testing evaluates overall complexity and
accuracy. The proposed system proves to be an efficient
and robust solution for classroom attendance
management, eliminating manual labour and time
consumption. Additionally, the system's development is
cost-effective and requires minimal installation.
Keywords :
OpenCV, Haar Cascade Algorithm, MySQL,Python.
Facial recognition stands as one of the most
efficient applications in image processing, playing a
crucial role in the technical sphere. Identifying human
faces is a pressing concern, particularly in verifying
student attendance. Utilizing facial biostatistics, an
attendance system employing face recognition relies on
high-resolution monitoring and advanced computer
technologies. The objective of developing this system is to
digitize the traditional method of attendance-taking,
which involves verbal calls and manual record-
keeping.Current attendance procedures are laborious
and time-consuming, prone to manipulation through
manual recording. Both traditional attendance marking
and existing biometric systems are susceptible to
fraudulent proxies. This paper aims
toaddressthesechallenges. The proposed system
incorporates the Haar cascade algorithm, OpenCV, Dlib,
Pandas, and MySQL. Following facial recognition,
attendance reports are generated and saved in Excel
format. The system undergoes testing under different
conditions, such as variations in illumination, head
movements, and changes in camera-to-student distance.
Rigorous testing evaluates overall complexity and
accuracy. The proposed system proves to be an efficient
and robust solution for classroom attendance
management, eliminating manual labour and time
consumption. Additionally, the system's development is
cost-effective and requires minimal installation.
Keywords :
OpenCV, Haar Cascade Algorithm, MySQL,Python.
