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April 21, 2026Open Access

AttendNet: A Real-Time Deep Learning Framework for Multi-Face Recognition and Automated Attendance Monitoring

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Authors

KRK P Thrived Reddy

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Overview

Automated attendance monitoring system shows high accuracy in dynamic educational environments, indicating improved reliability.

Key Points

  • The aim is to develop a reliable real-time face recognition system for automated attendance monitoring in classrooms.
  • Proposed AttendNet AI, a temporal-sensitive recognition system using a distance-based similarity measure.
  • Implemented a multi-frame temporal validation mechanism for enhanced accuracy.
  • Deployed as a full-stack application with user interfaces for administration and attendance logging.
  • Achieved an accuracy of 96.3%, precision of 96.9%, recall of 95.0%, and F1-score of 95.6.
  • Demonstrated improved resilience against motion, occlusion, and transient detections in varied lighting scenarios.

Cite This Study

K P Thrived Reddy (2026) studied this question.

synapsesocial.com/papers/69e713decb99343efc98d490https://doi.org/10.5281/zenodo.19654074
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  1. 1INTELLIGENT CLASS MONITORING SYSTEM USING FACENET&OPEN CV2026
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