Edge AI-Based Multimodal Biometric Smart Reader Using YOLOv8 for Integrated Academic Attendance Systems
Keywords:
Edge AI, YOLOv8, multimodal biometrics, smart attendance, face recognitionAbstract
Attendance systems in vocational education institutions face challenges related to accuracy, security, and susceptibility to manipulation due to the use of single-modality authentication methods. RFID-based systems are vulnerable to card sharing, fingerprint systems suffer from latency during peak usage, and face recognition systems are sensitive to illumination and pose variations. This study proposes an Edge AI-based multimodal biometric smart reader integrating RFID, fingerprint, and YOLOv8-based face recognition for an academic attendance system at SMK Negeri 1 Dumai. The system is implemented on NVIDIA Jetson Nano as an edge computing device and integrated with an academic information system through an IoT-based architecture for real-time attendance monitoring. A decision-level fusion approach using majority voting is applied, where authentication is accepted if at least two of three modalities match. The system is evaluated using accuracy, False Acceptance Rate (FAR), False Rejection Rate (FRR), and response time. Experimental results show that the proposed multimodal system achieves an accuracy of 98.72%, outperforming RFID (89.34%), fingerprint (92.15%), and YOLOv8 face recognition (95.63%). The system also reduces FAR to 0.82% and FRR to 0.91%, with an average response time of 1.47 seconds, making it suitable for real-time deployment. Overall, the proposed Edge AI-based multimodal biometric system demonstrates high accuracy, improved security, and efficient real-time performance, providing a scalable solution for intelligent attendance systems in vocational education environments.References
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