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In this paper, we proposed a facial recognition-based attendance system that improves the attendance system's accuracy and efficient hospitality in educational institutes. Conventional methods such as roll call and card swipe require a long time, creating a boycott and fraud opportunity. Face Detection and Face Recognition Our system detects faces using a Single Shot Detector (SSD) with MobileNet for real-time face detection, and we use FaceNet to recognize faces and make a 128-dimensional face mapping for each user. It includes Google Sheets integration for maintaining live attendance records and automated alerts to parents via the Twilio API to improve communication. We’re using Flask and React to create a straightforward, web-based interface that allows administrators to monitor attendance easily. In tests with 40 students, the system achieved a face detection accuracy of 96.2% and a recognition accuracy of 95.7%. Automating attendance management relieves administrators of time-consuming tasks, allowing educators to focus more on teaching and boosting student engagement. Additionally, it ensures that data is managed both reliably and securely.

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Advanced Student Attendance System with Deep Learning-Based Face Recognition

  • 会议论文

  • 发表日期：

    2025年

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  • Lokaiah Pullagura,
  • Biswajit Brahma,
  • U. Janardhan Reddy,
  • Azhar Hussain Mozumder

摘要

In this paper, we proposed a facial recognition-based attendance system that improves the attendance system's accuracy and efficient hospitality in educational institutes. Conventional methods such as roll call and card swipe require a long time, creating a boycott and fraud opportunity. Face Detection and Face Recognition Our system detects faces using a Single Shot Detector (SSD) with MobileNet for real-time face detection, and we use FaceNet to recognize faces and make a 128-dimensional face mapping for each user. It includes Google Sheets integration for maintaining live attendance records and automated alerts to parents via the Twilio API to improve communication. We’re using Flask and React to create a straightforward, web-based interface that allows administrators to monitor attendance easily. In tests with 40 students, the system achieved a face detection accuracy of 96.2% and a recognition accuracy of 95.7%. Automating attendance management relieves administrators of time-consuming tasks, allowing educators to focus more on teaching and boosting student engagement. Additionally, it ensures that data is managed both reliably and securely.