A Novel Based Approach For Attendance Using Face Recognize System

• Author(s):

  Kaviyadharshini R | Kalaiyarasan K

• Keywords:

• Abstract:

  In Many Of The Educational Institutions, Managing Attendance Of Students/candidates Is Tedious, As There Would Be Large Number Of Students In The Class And Keeping Track Of All Is Onerous. There Are Situations Where Student Act As Proxies For Their Friends Even Though They Are Not Present. The Advancement In The History Of Computer Vision Utilizing Deep Learning Approaches Especially Convolutional Neural Networks Have Accomplished To Solve Difficult Problems In Face Recognition Field. Face Recognition-based Approach Is One Amongst The Important Identification Methods Which Can Be Used As A Possible Substitution For Conventional System Of Marking Attendance Manually, Especially If A Huge Classroom Of Students Is Addressed For An Hour Session. Our Solutions Integrate AI Capabilities With Smart Analytics Features To Facilitate Transparency In Classrooms And College Campus.This Project Develops An Automatic Attendance System Using Faster R-CNN Deep Learning Based Algorithm. In This System, A Database Containing The Trained Student’s Face. A Camera Installed In The College Campus Captures The Face Of All The Student In The Classroom And Other Places Too. This Face Image Is Processed Using FRCNN Algorithms To Detect Faces And To Mark The Attendance Automatically In An Excel Sheet. The System Records The Entire Class Session And Identifies When The Students Pay Attention In The Classroom, And Then Reports To The Facilities And Also This System Can Record Violations Of Classroom, That Is Absence, Roaming Around The College Campus During The Class Hours And Send Alert Message To The H.O.D.This Dynamic Attendance System Uses Face Recognition As An Important Aspect Of Taking Attendance Which Saves Time And Proxy Attendance And Is Avoided. The System Identifies Faces Very Fast Needing Only 100 Milliseconds To One Frame And Obtaining A High Accuracy. Our Face Recognition Model Has An Accuracy Rate Of 99%.

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Other Details

• Paper id:

  IJSARTV12I1104512

• Published in:

  Volume: 12 Issue: 1 January 2026

• Publication Date:

  2026-01-19

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