Automated Attendance Management System(AMS) Using Local Binary Pattern

EasyChair Preprint 6008

Abstract

In the human body, the face is the most crucial factor in identifying each person as it contains many vital details. There are different prevailing methods to capture person's presence like biometrics to take attendance which is a time-consuming process. In this paper we develop a model to classify each character's face from a captured image using a collection of rules i.e., LBP algorithm to record the student attendance. LBP (Local Binary Pattern) is one among the methods and is popular as well as effective technique used for the image representation and classification and it was chosen for its robustness to pose and illumination shifts. The proposed ASAS (Automated Smart Attendance System) will capture the image and will be compared to the image stored in the database. The database is updated upon the enrolment of the student using an automation process that also includes name and rolls number. ASAS marks individual attendance, if the captured image matches the image in the database i.e., if both images are identical. The proposed algorithm reduces effort and captures day-to-day actions of managing each student and also makes it simple to mark the presence.

Keyphrases: Local Binary Pattern, face detection, face recognition, image processing

@booklet{EasyChair:6008,
  author    = {Preethi Kolipaka},
  title     = {Automated Attendance Management System(AMS) Using Local Binary Pattern},
  howpublished = {EasyChair Preprint 6008},
  year      = {EasyChair, 2021}}