Design and Simulation of a Lightweight Edge–Cloud Face Recognition System for Smart Attendance Management
H Krupa1, G Sudha Gowd2, M Vidhyanand Reddy3, S Arshad Ali4, G Rajesh5, V Thanvi6
1PVKK Institute of Technology Anantapuramu, Andrapradesh
2PVKK Institute of Technology Anantapuramu, Andrapradesh
3PVKK Institute of Technology Anantapuramu, Andrapradesh
4PVKK Institute of Technology Anantapuramu, Andrapradesh
5PVKK Institute of Technology Anantapuramu, Andrapradesh
6PVKK Institute of Technology Anantapuramu, Andrapradesh
Abstract - Attendance management remains a significant challenge in educational institutions due to proxy entries, human errors, and the absence of real-time monitoring. Conventional roll-call methods are time-consuming and unreliable, particularly in large classrooms. This paper presents a Hybrid Edge-Cloud Face Recognition-Based Smart Attendance Monitoring System implemented as a prototype and evaluated in a simulated deployment environment. The proposed framework integrates lightweight Convolutional Neural Networks (CNNs) for face detection and recognition within a virtualized edge computing setup. Model optimization techniques, including structured pruning and INT8 quantization, are applied to reduce computational complexity and enable deployment on resource-constrained edge devices. To minimize bandwidth usage between edge and cloud components, facial embeddings are compressed from 512 to 128 dimensions. A hybrid loss function combining ArcFace and Center Loss is employed during training to enhance robustness against variations in lighting, pose, and multi-face classroom conditions. The architecture distributes processing between simulated edge nodes and a centralized cloud server to evaluate latency, bandwidth consumption, and scalability. An adaptive multi-frame attendance validation mechanism further reduces false positives. Experimental results demonstrate improved efficiency and reduced computational and network overhead without compromising recognition accuracy.
Key Words: Smart Attendance System, Face recognition, Convolutional Neural Network, Model Quantization, Edge cloud Architecture, Automated Attendance Management
