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Automated Attendee Recognition System for Large-Scale Social Events or Conference Gathering

Dhruv Motwani, Ankush Tyagi, Vipul Dabhi, Harshadkumar Prajapati

TL;DR

The paper tackles the challenge of automating attendance at large-scale events using a cloud-based, camera-driven facial recognition system that operates in real time and tolerates non-frontal views. It proposes a hybrid cloud-local architecture leveraging local Java processing and AWS Rekognition, with G-Streamer for real-time output and security alerts. Empirical results show high accuracy (up to 97% overall) and low latency (around 5 seconds per frame) with prompt notifications, while identifying areas for improvement in handling occlusions and accessories. The proposed solution offers a scalable, non-intrusive approach to attendee validation that can enhance efficiency and security at conferences, weddings, and other large gatherings.

Abstract

Manual attendance tracking at large-scale events, such as marriage functions or conferences, is often inefficient and prone to human error. To address this challenge, we propose an automated, cloud-based attendance tracking system that uses cameras mounted at the entrance and exit gates. The mounted cameras continuously capture video and send the video data to cloud services to perform real-time face detection and recognition. Unlike existing solutions, our system accurately identifies attendees even when they are not looking directly at the camera, allowing natural movements, such as looking around or talking while walking. To the best of our knowledge, this is the first system to achieve high recognition rates under such dynamic conditions. Our system demonstrates overall 90% accuracy, with each video frame processed in 5 seconds, ensuring real time operation without frame loss. In addition, notifications are sent promptly to security personnel within the same latency. This system achieves 100% accuracy for individuals without facial obstructions and successfully recognizes all attendees appearing within the camera's field of view, providing a robust solution for attendee recognition in large-scale social events.

Automated Attendee Recognition System for Large-Scale Social Events or Conference Gathering

TL;DR

The paper tackles the challenge of automating attendance at large-scale events using a cloud-based, camera-driven facial recognition system that operates in real time and tolerates non-frontal views. It proposes a hybrid cloud-local architecture leveraging local Java processing and AWS Rekognition, with G-Streamer for real-time output and security alerts. Empirical results show high accuracy (up to 97% overall) and low latency (around 5 seconds per frame) with prompt notifications, while identifying areas for improvement in handling occlusions and accessories. The proposed solution offers a scalable, non-intrusive approach to attendee validation that can enhance efficiency and security at conferences, weddings, and other large gatherings.

Abstract

Manual attendance tracking at large-scale events, such as marriage functions or conferences, is often inefficient and prone to human error. To address this challenge, we propose an automated, cloud-based attendance tracking system that uses cameras mounted at the entrance and exit gates. The mounted cameras continuously capture video and send the video data to cloud services to perform real-time face detection and recognition. Unlike existing solutions, our system accurately identifies attendees even when they are not looking directly at the camera, allowing natural movements, such as looking around or talking while walking. To the best of our knowledge, this is the first system to achieve high recognition rates under such dynamic conditions. Our system demonstrates overall 90% accuracy, with each video frame processed in 5 seconds, ensuring real time operation without frame loss. In addition, notifications are sent promptly to security personnel within the same latency. This system achieves 100% accuracy for individuals without facial obstructions and successfully recognizes all attendees appearing within the camera's field of view, providing a robust solution for attendee recognition in large-scale social events.

Paper Structure

This paper contains 11 sections, 5 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background Theory and Related Work
  3. Proposed Work
  4. Proposed System
  5. System Setup
  6. Implementation of the Proposed System
  7. Experiments and Results
  8. Dataset Preparation
  9. Experiments Settings
  10. Results and Discussion
  11. Conclusion

Figures (5)

  • Figure 1: High-level Architecture of the Proposed System: Automated Attendee Recognition System
  • Figure 2: Implementation of the proposed system: AWS Cloud components and Lambda Functions for processing
  • Figure 3: Sample images of dataset preparation of available participants
  • Figure 4: A sample image of dataset preparation of unavailable participants
  • Figure 5: Testing of the proposed system: green bounding boxes indicate persons detected and recognized successfully (3 persons in this frame) and red bounding boxes indicate a face is detected, but the person is not recognized (1 person in this frame)