Abstract

This paper outlines an open source LabVIEW based Remote Laboratory System for Automated Attendance Monitoring through Facial Recognition to assist with both User Authentication and Multi-User Scalability within Online Practical Courses. The proposed RLS has been designed utilizing a Pipeline-Parallel Architecture where multiple Webcam Streams can be processed in parallel at different stages, namely Capture, Detection, Recognition and Logging. Evaluation of the proposed RLS involved testing with six Users simultaneously over a controlled environment. Results indicate Real-Time Operation with an average Per-User Latency Time of ~0.2 seconds. Average Throughput is estimated to be ~8 frames/sec/user (~45-50 frames/sec aggregate). Accuracy of Recognitions is reported as being ~96-99% under typical Lighting Conditions. A PCA/Eigenfaces Path was utilized for Fast Continuous Verification, and Periodic FaceNet-Based Checks were employed to Improve Robustness with moderate Overhead. Resource Profiling results estimate ~85% CPU Utilization from a Commodity Multi-Core Host (8-Cores / 16-Threading), 300 MB Memory Demand, and 3 Mbps Network Demand. These estimates suggest Straightforward Scaling via Additional Cores, GPU/FPGA Acceleration or Frame-Rate Throttling. The Proposed Design also Integrates Embedded/IoT Endpoints, which aligns with Computational Thinking and Education 4.0 Priorities that enable Authentic Participation, Learning Management System Integration, and Data-Driven Pedagogy. Wide-Area Conditions were Characterized Using a Laboratory WAN Emulator (tc/netem) that Simulated Controlled Levels of Latency, Jitter and Loss Representative of Campus, 4G and 5G Profiles. Due to the Complexity of Deploying an Internet-Scale Multi-Site Version of the Proposed RLS, such a Deployment is identified as Future Work.

Keywords

Remote Laboratories, LabVIEW, Attendance Monitoring, Face Recognition, Pipeline Parallelism, Machine Learning, IoT and 5G, Online Learning Platforms,

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References

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