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Face recognition technology holds significant importance across various domains due to its practical applications, transforming sectors such as security, education, retail, and healthcare. This research focuses on optimizing and streamlines attendance tracking and recording during learning sessions by combining state-of-the-art technologies like cloud computing and machine learning. An innovative cross-platform solution is proposed, utilizing Convolutional Neural Networks (CNNs) to achieve an impressive accuracy of 96% in identifying individuals, making it suitable for diverse organizational settings, including schools and offices. By automating attendance tracking, the system reduces manual effort and minimizes errors associated with traditional methods. It features a user-friendly interface and facilitates real-time recognition, allowing seamless integration with existing databases. This enhances operational efficiency while ensuring secure and trustworthy attendance management, complete with robust protections for data privacy and user authentication. Our face recognition-based attendance system streamlines the attendance process and serves as a reliable alternative to manual tracking, transforming the way attendance management is approached.

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Improving Attendance Tracking with Face Recognition Using Convolutional Neural Networks

  • 会议论文

  • 发表日期：

    2026年

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  • S. V. Bhaskar,
  • Rachietaa Ramesh Rao,
  • S. Vaishnavi,
  • J. Karthiyayini,
  • Trupthi S. Anand,
  • K. Varsha

摘要

Face recognition technology holds significant importance across various domains due to its practical applications, transforming sectors such as security, education, retail, and healthcare. This research focuses on optimizing and streamlines attendance tracking and recording during learning sessions by combining state-of-the-art technologies like cloud computing and machine learning. An innovative cross-platform solution is proposed, utilizing Convolutional Neural Networks (CNNs) to achieve an impressive accuracy of 96% in identifying individuals, making it suitable for diverse organizational settings, including schools and offices. By automating attendance tracking, the system reduces manual effort and minimizes errors associated with traditional methods. It features a user-friendly interface and facilitates real-time recognition, allowing seamless integration with existing databases. This enhances operational efficiency while ensuring secure and trustworthy attendance management, complete with robust protections for data privacy and user authentication. Our face recognition-based attendance system streamlines the attendance process and serves as a reliable alternative to manual tracking, transforming the way attendance management is approached.