TY - GEN
T1 - Deep Face Recognition for Intelligent Video Surveillance at Electrical Substations
AU - Dai, Bo
AU - Jiang, Jinxia
AU - Shen, Guizhu
AU - Wang, Xue
AU - Wang, Qing
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Robust face recognition (FR) in real-world surveillance videos is a challenging but important issue due to the need of practical applications such as security monitoring at electrical substations. While the performance of current FR systems has been significantly boosted by deep learning technology due to its high capacity in learning discriminative features, they still tend to suffer from variations in pose, illumination, occlusion, scale, blur or low image quality in real-world surveillance videos. In this paper, we propose a novel framework which integrates face detection and recognition with tracking. Extensive experiments validate the effectiveness of the proposed framework. Our method outperforms previous SOTAs on three public datasets, i.e., LFW, CFP and AgeDB. Moreover, on the challenging testing datasets collected from the electrical substation surveillance system, the proposed method achieves an average accuracy of 91.4%.
AB - Robust face recognition (FR) in real-world surveillance videos is a challenging but important issue due to the need of practical applications such as security monitoring at electrical substations. While the performance of current FR systems has been significantly boosted by deep learning technology due to its high capacity in learning discriminative features, they still tend to suffer from variations in pose, illumination, occlusion, scale, blur or low image quality in real-world surveillance videos. In this paper, we propose a novel framework which integrates face detection and recognition with tracking. Extensive experiments validate the effectiveness of the proposed framework. Our method outperforms previous SOTAs on three public datasets, i.e., LFW, CFP and AgeDB. Moreover, on the challenging testing datasets collected from the electrical substation surveillance system, the proposed method achieves an average accuracy of 91.4%.
KW - Deep neural network
KW - Electrical substation
KW - Face recognition
KW - Surveillance video
UR - http://www.scopus.com/inward/record.url?scp=85129132602&partnerID=8YFLogxK
U2 - 10.1109/CCIS53392.2021.9754622
DO - 10.1109/CCIS53392.2021.9754622
M3 - 会议稿件
AN - SCOPUS:85129132602
T3 - Proceedings of 2021 7th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2021
SP - 514
EP - 518
BT - Proceedings of 2021 7th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2021
A2 - Li, Deyi
A2 - Zhou, Mengqi
A2 - Wang, Weining
A2 - Zou, Yaru
A2 - Luo, Meng
A2 - Zhang, Qian
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2021
Y2 - 7 November 2021 through 8 November 2021
ER -