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
[1]TURK M,PENTLAND A.Eigenfaces for Recognition[J].Journal of Cognitive Neuroscience,1991,3(1):71-86.
[2]WANG Y,LI X.Face Recognition Based on LDP Feature and Bayesian Model[J].Computer Science,2017,44(12):283-286,291.
[3]TARIQ U,LIN K H,LI Z,et al.Recognizing emotions from an ensemble of features[J].IEEE Transactions on Systems,Man and Cybernetics,Part B(Cybernetics),2012,42(4):1017-1026.
[4]WU Q H,GAO X D.Face Recognition in Non-ideal Environment Based on Sparse Representation and Support Vector Machine[J].Computer Science,2020,47(6):121-125.
[5]DENG L,XU G L,LI M J,et al.Fast Face Recognition Based on Deep Learning and Multiple Hash Similarity Weighting[J].Computer Science,2020,47(9):163-168.
[6]SUN Y,WANG X G,TANG X,et al.Deep Learning Face Representation from Predicting 10 000 Classes[C]//27th IEEE Conference on Computer Vision and Pattern Recognition.Columbus:IEEE,2014:1891-1898.
[7]SUN Y,CHEN Y H,WANG X G,et al.Deep Learning FaceRepresentation by Joint Identification-Verification[EB/OL].(2014-06-18)[2020-12-20].
[8]SUN Y,WANG X G,TANG X,et al.Deeply learned face representations are sparse,selective,and robust[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition.Boston:IEEE,2015:2892-2900.
[9]KIM Y,PARK W,ROH M C,et al.GroupFace:Learning Latent Groups and Constructing Group-Based Representations for Face Recognition[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).IEEE,2020:5620-5629.
[10]DUONG C N,TRUONG T D,LUU K,et al.Vec2Face:Unveil Human Faces From Their Blackbox Features in Face Recognition[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).IEEE,2020:6131-6140.
[11]SCHROFF F,KALENICHENKO D,PHILBIN J.Facenet:Aunified embedding for face recognition and clustering[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Boston:IEEE,2015:815-823.
[12]WANG S S,CHEN Y.A joint lass function for deep face recognition[J].Multidimensional Systems and Signal Processing,2019,30(3):1517-1530.
[13]HE K M,ZHANG X,REN S,et al.Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas:IEEE,2016:770-778.
[14]HE K M,ZHANG X,REN S,et al.Identity Mappings in Deep Residual Networks[C]//14th European Conference on Compu-ter.Amsterdam:IEEE,2016:630-645.
[15]LIU W,WEN Y,YU Z,et al.SphereFace:Deep Hypersphere Embedding for Face Recognition[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition.Honolulu:IEEE,2017:6738-6746.
[16]DENG J,GUO J,XUE N,et al.ArcFace:Additive Angular Margin Loss for Deep Face Recognition[C]//2019 IEEE Conference on Computer Vision and Pattern Recognition.Long Beach:IEEE,2019:4685-4694.
[17]HOWARD A G,ZHU M,CHEN B,et al.MobileNets:Efficient Convolutional Neural Networks for Mobile Vision Applications[EB/OL].(2017-04-17)[2020-12-20].
[18]SANDLER M,HOWARD A,ZHU M,et al.MobileNetV2:Inverted Residuals and Linear Bottlenecks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE,2018:4510-4520.
[19]DONG Y,LEI Z,LIAO S C,et al.Learning face representation from scratch[EB/OL].(2014-11-28)[2020-12-20].
[20]HUANG Y,WANG Y,TAI Y,et al.CurricularFace:Adaptive Curriculum Learning Loss for Deep Face Recognition[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).IEEE,2020:5900-5909.
[1] GAO Xin-yue, TIAN Han-min. Droplet Segmentation Method Based on Improved U-Net Network [J]. Computer Science, 2022, 49(4): 227-232.
[2] ZHANG Hong-min, LI Ping-ping, FANG Xiao-bing, LIU Hong. Human Abnormal Behavior Detection Method Based on Improved YOLOv3 Network Model [J]. Computer Science, 2022, 49(4): 233-238.
[3] HE Jia-yu, HUANG Hong-bo, ZHANG Hong-yan, SUN Mu-ye, LIU Ya-hui, ZHOU Zhe-hai. Review of 3D Face Reconstruction Based on Single Image [J]. Computer Science, 2022, 49(2): 40-50.
[4] CHEN Chang-wei, ZHOU Xiao-feng. Fast Local Collaborative Representation Based Classifier and Its Applications in Face Recognition [J]. Computer Science, 2021, 48(9): 208-215.
[5] HE Qing-fang, WANG Hui, CHENG Guang. Research on Classification of Breast Cancer Pathological Tissues with Adaptive Small Data Set [J]. Computer Science, 2021, 48(6A): 67-73.
[6] WEN He, LUO Pin-jie. Dynamic Face Recognition Based on Improved Pulse Coupled Neural Network [J]. Computer Science, 2021, 48(6A): 85-88.
[7] LIU Han-qing, KANG Xiao-dong, LI Bo, ZHANG Hua-li, FENG Ji-chao, HAN Jun-ling. Comparative Study on Classification and Recognition of Medical Images Using Deep Learning Network [J]. Computer Science, 2021, 48(6A): 89-94.
[8] LI Fan, YAN Xing, ZHANG Xiao-yu. Optimization of GPU-based Eigenface Algorithm [J]. Computer Science, 2021, 48(4): 197-204.
[9] BAI Zi-yi, MAO Yi-rong , WANG Rui-ping. Survey on Video-based Face Recognition [J]. Computer Science, 2021, 48(3): 50-59.
[10] GONG Hang, LIU Pei-shun. Detection Method of High Beam in Night Driving Vehicle [J]. Computer Science, 2021, 48(12): 256-263.
[11] LU Yao-yao, YUAN Jia-bin, HE Shan, WANG Tian-xing. Low-quality Video Face Recognition Method Based on Super-resolution Reconstruction [J]. Computer Science, 2021, 48(11A): 295-302.
[12] YANG Zhang-jing, WANG Wen-bo, HUANG Pu, ZHANG Fan-long, WANG Xin. Local Weighted Representation Based Linear Regression Classifier and Face Recognition [J]. Computer Science, 2021, 48(11A): 351-359.
[13] LUAN Xiao, LI Xiao-shuang. Face Anti-spoofing Algorithm Based on Multi-feature Fusion [J]. Computer Science, 2021, 48(11A): 409-415.
[14] YANG Yue-lin, BI Zong-ze. Network Anomaly Detection Based on Deep Learning [J]. Computer Science, 2021, 48(11A): 540-546.
[15] LI Hui, LI Xiu-hua, XIONG Qing-yu, WEN Jun-hao, CHENG Lu-xi, XING Bin. Edge Computing Enabling Industrial Internet:Architecture,Applications and Challenges [J]. Computer Science, 2021, 48(1): 1-10.
Full text



[1] WU Jian-xia, YANG Yong-li. Algorithm for Reducing PAPR of FBMC-OQAM System[J]. Computer Science, 2018, 45(6): 89 -95 .
[2] ZHANG Su-fang,ZHAI Jun-hai,WANG Ting-ting,HAO Pu,WANG Cong,ZHAO Chun-ling. Spark Based Condensed Nearest Neighbor Algorithm[J]. Computer Science, 2018, 45(6A): 406 -410 .
[3] TAO Bing-mo,LU Shu-xia. Adaptive Stochastic Gradient Descent for Imbalanced Data Classification[J]. Computer Science, 2018, 45(6A): 487 -492 .
[4] ZHENG Jian-bin, BAI Ya-xian, ZHAN En-qi and WANG Yang. Improved SIFT Matching Method for Milk Beverage Recognition in Grocery[J]. Computer Science, 2017, 44(9): 315 -319 .
[5] LI Ting and CHENG Hai-tao. Research of Approximate Keyword Query on Fuzzy XML Documents[J]. Computer Science, 2017, 44(9): 216 -221 .
[6] CHEN Jie-qiong, JIANG Shu-juan and ZHANG Zheng-guang. Approach for Test Case Generation Based on Data Flow Criterion[J]. Computer Science, 2017, 44(2): 107 -111 .
[7] LI Yan and WANG Li-na. Research of Spatio-temporal Interpolation Algorithm Based on Time Series[J]. Computer Science, 2014, 41(Z6): 414 -416 .
[8] ZHAO Wei-quan,YUAN Hua-qiang,LI Di and WEI Xiao-rui. Research on Delay in a Kind of Internet of Things Control System[J]. Computer Science, 2014, 41(Z6): 303 -305 .
[9] GUO Hua-ping,YUAN Jun-hong,ZHANG Fan,Wu Chang-an and FAN Ming. New Ensemble Learning Approach[J]. Computer Science, 2014, 41(7): 283 -289 .
[10] HUANG Jian, LI Ming-qi and GUO Wen-qiang. Parallel Fp-growth Algorithm in Search Engines[J]. Computer Science, 2015, 42(Z6): 459 -461 .