Intelligent Attendance Monitoring System Using Deep Face Recognition With Residual Neural Network (resnet) Analysis

• Author(s):

  Thinesh T | Mr. S. Chandrasekar | Sivasakthi S | Sivanthamil M | Suman Raj R

• Keywords:

  OpenCV; Face Detection; Attendance System; Deep Learning; Facial Recognition; Neural Networks; Real-time Processing-of-entry For Any Given Scientific Paper Or Patent Application

• Abstract:

  An Innovative Attendance System Utilizing Face Detection Technology Is Presented, Aimed At Improving The Efficiency And Accuracy Of Attendance Tracking. This System Integrates Computer Vision With Advanced Deep Learning Techniques, Enabling Reliable Recognition Of Individuals And Real-time Attendance Logging. Convolutional Neural Networks (CNNs) Are Employed For Face Detection And Recognition, Establishing A Robust Alternative To Traditional Attendance Methods. With High Detection Accuracy, Rapid Processing Times, And Comprehensive Data Security Protocols, This System Is Well-suited For Implementation In Educational Institutions, Corporate Environments, And Secure Access Management. Experimental Results Indicate A Detection Accuracy Of 98.6% And An Average Verification Time Of Under 1.5 Seconds, Underscoring The Effectiveness Of Face Recognition Technology In Automated Attendance Systems.

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Other Details

• Paper id:

  IJSARTV11I5103458

• Published in:

  Volume: 11 Issue: 5 May 2025

• Publication Date:

  2025-05-04

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