Comparative Analysis Of Visitor Face Recognition Attendance Systems: Dashboard Themes Vs Bootstrap-Based Interfaces

Abstract

The integration of face recognition technology into visitor attendance systems has significantly enhanced security, monitoring, and operational efficiency across institutional settings. This paper presents a comparative study between two systems: the Visitor Face Recognition Attendance Database System Using Dashboard Themes (VFRA-DT) and the Visitor Face Recognition Attendance System with Bootstrap-Based Interface (VFRA-BI). The primary objective is to evaluate and compare the systems based on critical dimensions, interface architectures and design, recognition performance, database efficiency, and overall system reliability, to select the most suitable interface for creating a visitor face recognition system. Both systems were designed to automate the visitor check-in process, eliminate manual logging, and provide administrators with real-time data visualization.  VFRA-DT features a dashboard-centric design tailored for administrative control, while VFRA-BI focuses on modularity and responsive design using the Bootstrap framework.  The systems were deployed and tested under diverse environmental conditions—such as varying lighting, backgrounds, and distances—to evaluate their accuracy, response time, error rates, and usability. The results indicate that VFRA-BI achieves higher recognition accuracy (92% vs. 89%), faster attendance logging (1.8s vs. 2.1s), more responsive database querying, and reduced system downtime compared to the baseline. VFRA-BI’s use of a machine learning-driven approach and flexible interface enhances its adaptability in dynamic settings. Meanwhile, VFRA-DT remains effective in more controlled environments, offering a user-friendly interface with reliable recognition capabilities. Nonetheless, both systems face limitations in low-light and complex background scenarios, particularly with darker face images.  This study provides comparative insights to support the development and implementation of efficient, secure, and scalable face recognition systems for visitor management.