@article{ART003076571},
author={Tan Heyi and Min, Byung-Won},
title={Improvement of Face Recognition Algorithm for Residential Area Surveillance System Based on Graph Convolution Network},
journal={Journal of Internet of Things and Convergence},
issn={2466-0078},
year={2024},
volume={10},
number={2},
pages={1-15}
TY - JOUR
AU - Tan Heyi
AU - Min, Byung-Won
TI - Improvement of Face Recognition Algorithm for Residential Area Surveillance System Based on Graph Convolution Network
JO - Journal of Internet of Things and Convergence
PY - 2024
VL - 10
IS - 2
PB - The Korea Internet of Things Society
SP - 1
EP - 15
SN - 2466-0078
AB - The construction of smart communities is a new method and important measure to ensure the security of residential areas. In order to solve the problem of low accuracy in face recognition caused by distorting facial features due to monitoring camera angles and other external factors, this paper proposes the following optimization strategies in designing a face recognition network: firstly, a global graph convolution module is designed to encode facial features as graph nodes, and a multi-scale feature enhancement residual module is designed to extract facial keypoint features in conjunction with the global graph convolution module. Secondly, after obtaining facial keypoints, they are constructed as a directed graph structure, and graph attention mechanisms are used to enhance the representation power of graph features. Finally, tensor computations are performed on the graph features of two faces, and the aggregated features are extracted and discriminated by a fully connected layer to determine whether the individuals' identities are the same. Through various experimental tests, the network designed in this paper achieves an AUC index of 85.65% for facial keypoint localization on the 300W public dataset and 88.92% on a self-built dataset. In terms of face recognition accuracy, the proposed network achieves an accuracy of 83.41% on the IBUG public dataset and 96.74% on a self-built dataset. Experimental results demonstrate that the network designed in this paper exhibits high detection and recognition accuracy for faces in surveillance videos.
KW - Key Words : Face Recognition;Vision Transformer;Graph Convolution;Residential Area;Surveillance System
DO -
UR -
ER -
Tan Heyi and Min, Byung-Won. (2024). Improvement of Face Recognition Algorithm for Residential Area Surveillance System Based on Graph Convolution Network. Journal of Internet of Things and Convergence, 10(2), 1-15.
Tan Heyi and Min, Byung-Won. 2024, "Improvement of Face Recognition Algorithm for Residential Area Surveillance System Based on Graph Convolution Network", Journal of Internet of Things and Convergence, vol.10, no.2 pp.1-15.
Tan Heyi, Min, Byung-Won "Improvement of Face Recognition Algorithm for Residential Area Surveillance System Based on Graph Convolution Network" Journal of Internet of Things and Convergence 10.2 pp.1-15 (2024) : 1.
Tan Heyi, Min, Byung-Won. Improvement of Face Recognition Algorithm for Residential Area Surveillance System Based on Graph Convolution Network. 2024; 10(2), 1-15.
Tan Heyi and Min, Byung-Won. "Improvement of Face Recognition Algorithm for Residential Area Surveillance System Based on Graph Convolution Network" Journal of Internet of Things and Convergence 10, no.2 (2024) : 1-15.
Tan Heyi; Min, Byung-Won. Improvement of Face Recognition Algorithm for Residential Area Surveillance System Based on Graph Convolution Network. Journal of Internet of Things and Convergence, 10(2), 1-15.
Tan Heyi; Min, Byung-Won. Improvement of Face Recognition Algorithm for Residential Area Surveillance System Based on Graph Convolution Network. Journal of Internet of Things and Convergence. 2024; 10(2) 1-15.
Tan Heyi, Min, Byung-Won. Improvement of Face Recognition Algorithm for Residential Area Surveillance System Based on Graph Convolution Network. 2024; 10(2), 1-15.
Tan Heyi and Min, Byung-Won. "Improvement of Face Recognition Algorithm for Residential Area Surveillance System Based on Graph Convolution Network" Journal of Internet of Things and Convergence 10, no.2 (2024) : 1-15.
