Computer Science ›› 2022, Vol. 49 ›› Issue (5): 71-77.doi: 10.11896/jsjkx.210300222

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Face Recognition Method Based on Edge-Cloud Collaboration

WEI Qin, LI Ying-jiao, LOU Ping, YAN Jun-wei, HU Ji-wei   

  1. School of Information Engineering,Wuhan University of Technology,Wuhan 430070,China
  • Received:2021-03-22 Revised:2021-11-17 Online:2022-05-15 Published:2022-05-06
  • About author:WEI Qin,born in 1980,Ph.D,associate professor,postgraduate supervisor.Her main research interests include signal processing and fault diagnosis.
  • Supported by:
    National Natural Science Foundation of China(52075404) and Application Foundation Frontier Special Project of Wuhan Science and Technology Bureau(2020010601012176).

Abstract: Face recognition is widely used in daily life such as shopping,security check,travel,payment and work attendance.Face recognition systems need strong computing power and large storage space,so face images that need to be recognized are usually transmitted to the cloud platform through the network.Due to the problems of network coverage,congestion and delay,face re-cognition systems are difficult to meet the needs of actual application,and the user experience is poor.Aiming at the problems in face recognition,a face recognition method based on edge-cloud collaboration is proposed.This method combines the processing ability of cloud computing and the real-time performance of edge computing,so that face recognition systems are not constrained by the network status,and its application is more extensive and the user experience is better.In the cloud,the LResNet feature extraction method is proposed to improve the ResNet34 network structure,and the ArcFace face loss function is used to supervise the training process,so that the network can learn more face angle features.At the edge,due to the limited computing resources and storage resources,a SResNet feature extraction method is proposed.Deep separable convolution is used to lighten the LResNet network structure,and it has greatly reduced network parameters and computation.The face recognition experiment on edge-cloud collaboration shows that the system can recognize faces in real time with a high accuracy rate under any network status.

Key words: Edge-Cloud collaboration, Face recognition, ResNet, ArcFace, Deep separable convolution

CLC Number: 

  • TP391.41
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