Authors :
Atharva Moholkar; Pranav Rao
Volume/Issue :
Volume 8 - 2023, Issue 11 - November
Google Scholar :
https://tinyurl.com/46eknw8j
Scribd :
https://tinyurl.com/4mdxj82r
DOI :
https://doi.org/10.5281/zenodo.10224161
Abstract :
This article presents the creation of a system
that utilises face recognition technology for attendance
marking purposes in various settings.
While face recognition has the least accuracy in
comparison to the other biometric methods such as
fingerprint or iris identification, its non-invasive and
contactless approach makes it a popular choice. This
system aims to address the inefficiencies of traditional
manual attendance systems that are time-consuming and
prone to errors such as proxy attendance. It has four
phases: Creating the database, detecting the face,
recognition of it and bringing the attendance up to date.
Database is made using pictures of the people in the class.
detection of face and recognition are done by using the
Facial_Recognition python library which is very efficient
and effective. The available system looks and recognises
faces from video streaming live feeds of the classroom,
and records of attendance are automatically forwarded to
the available respective faculty members of the institute
at the end of each particular session via mail.
Keywords :
Face Recognition; Face Detection; Classifier; Attendance System; Recognition Libraries.
This article presents the creation of a system
that utilises face recognition technology for attendance
marking purposes in various settings.
While face recognition has the least accuracy in
comparison to the other biometric methods such as
fingerprint or iris identification, its non-invasive and
contactless approach makes it a popular choice. This
system aims to address the inefficiencies of traditional
manual attendance systems that are time-consuming and
prone to errors such as proxy attendance. It has four
phases: Creating the database, detecting the face,
recognition of it and bringing the attendance up to date.
Database is made using pictures of the people in the class.
detection of face and recognition are done by using the
Facial_Recognition python library which is very efficient
and effective. The available system looks and recognises
faces from video streaming live feeds of the classroom,
and records of attendance are automatically forwarded to
the available respective faculty members of the institute
at the end of each particular session via mail.
Keywords :
Face Recognition; Face Detection; Classifier; Attendance System; Recognition Libraries.
