Automated event attendance recording tool using facial recognition

Abstract

Monitoring and measuring participant attendance at events is crucial for several reasons. In addition to offering organizers and sponsors insights into an event's popularity, it is essential for assessing the economic, social, and environmental impacts of the event. Various methods for recording attendance are currently in use, including manual methods such as signing a register which are often inefficient and prone to errors. While electronic attendance systems, such as NFC cards and fingerprint readers, exist, NFC cards are vulnerable to impersonation, and fingerprint readers may raise health concerns due to contact with shared devices. In contrast, facial recognition technology offers a promising solution for improving the accuracy of attendance records at events while mitigating hygiene risks associated with fingerprint readers. However, facial recognition systems tend to be computationally intensive. This research developed an automated event attendance recording tool that integrates Hash-Based Indexing with facial recognition algorithms to enhance the accuracy of attendance records while reducing computational load through record indexing. The study employed agile methodology for developing the automated attendance recording tool. Testing and evaluation utilized publicly available image databases to train the machine learning image recognition model and assess its performance. The primary evaluation metrics included the accuracy of image identification and the duration of transaction processing. The model achieved 95% accuracy in face recognition, with its performance further analysed using the confusion matrix and classification report. The developed tool provided an interface for event organizers to create events and record attendance offering utilizing the full capabilities of CNN for image recognition. The developed tool offered an interface that allowed event organizers to create events and record attendance, fully utilizing the capabilities of CNN for image recognition and hash-based indexing for faster retrieval of records.

Keywords: Attendance, Agile methodology, Electronic attendance systems, Facial recognition, Fingerprint readers, Machine learning, Hash-Based Indexing, Record indexing