Authors :
Aluko Augustine Oli; Omoniyi Akintunde Ojo; Ojajuni Olowatoyin James; Aworetan Fayowole Ayanfe; Oloruntegbe Ayomitan Busola; Owa Victor Korede
Volume/Issue :
Volume 8 - 2023, Issue 8 - August
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/ydzypp8r
DOI :
https://doi.org/10.5281/zenodo.8344343
Abstract :
Staff attendance system is a means for
organizations to justify the time in and time out of her
workforce. It is used to determine the time measured in
hours contributed by individual worker for daily, weekly
or monthly wages processing. Biometrics refers to the
automatic identification of a person based on his or her
physiology or behavioral characteristics which include
face, fingerprint, iris, voice, retinal scan, hand geometry,
palm prints, ear, body odor, DNA. The existence of
Nobel Corona Virus code named Covid-19 has poses a
great danger on both health and lives of those using
traditional and computerized contact based staff
attendance management system, hence a contactless
based biometric system is needed to replace the existing
staff attendance system in compliance with social
distancing, less contact with public surfaces area among
others. A facial biometric technique was adopted for its
ability to identify, verify and authenticate staff for
attendance register system without physical contact with
the system interface. This project was designed to
manage staff attendance register using facial recognition
biometric technique. The system was able to capture
staff bio-information, facial attribute and cross matched
individual identity and authentication for attendance
management with conformity to Covid-19 preventive
protocols. To achieve the objective, LUXDAN facial
recognition SDK was adopted and modified to produce a
tailor based system for efficiency, MySQL relational
database for backend database, and application interface
was developed within the Microsoft Visual Studio Dot
Net framework. The was able to capture staff facial
coordinates, identify, verify and authenticate human
faces from significant distance of the camera coverage
and display attendance register marked on staff cell
phone using the Wi-Fi network connection available.
Keywords :
Algorithm, Facial Recognition, Covid-19, Biometric, Staff, Wi-Fi.
Staff attendance system is a means for
organizations to justify the time in and time out of her
workforce. It is used to determine the time measured in
hours contributed by individual worker for daily, weekly
or monthly wages processing. Biometrics refers to the
automatic identification of a person based on his or her
physiology or behavioral characteristics which include
face, fingerprint, iris, voice, retinal scan, hand geometry,
palm prints, ear, body odor, DNA. The existence of
Nobel Corona Virus code named Covid-19 has poses a
great danger on both health and lives of those using
traditional and computerized contact based staff
attendance management system, hence a contactless
based biometric system is needed to replace the existing
staff attendance system in compliance with social
distancing, less contact with public surfaces area among
others. A facial biometric technique was adopted for its
ability to identify, verify and authenticate staff for
attendance register system without physical contact with
the system interface. This project was designed to
manage staff attendance register using facial recognition
biometric technique. The system was able to capture
staff bio-information, facial attribute and cross matched
individual identity and authentication for attendance
management with conformity to Covid-19 preventive
protocols. To achieve the objective, LUXDAN facial
recognition SDK was adopted and modified to produce a
tailor based system for efficiency, MySQL relational
database for backend database, and application interface
was developed within the Microsoft Visual Studio Dot
Net framework. The was able to capture staff facial
coordinates, identify, verify and authenticate human
faces from significant distance of the camera coverage
and display attendance register marked on staff cell
phone using the Wi-Fi network connection available.
Keywords :
Algorithm, Facial Recognition, Covid-19, Biometric, Staff, Wi-Fi.
