Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (6): 245-253.doi: 10.11896/jsjkx.210400023

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Compression Algorithm of Face Recognition Model Based on Unlabeled Knowledge Distillation

CHENG Xiang-ming, DENG Chun-hua   

  1. College of Computer Science and Technology,Wuhan University of Science and Technology,Wuhan 430065,China
    Institute of Big Data Science and Engineering,Wuhan University of Science and Technology,Wuhan 430065,China
    Hubei key Laboratory of Intelligent Information Processing and Real-time Industrial System,Wuhan University of Science and Technology,Wuhan 430065,China
  • Received:2021-03-31 Revised:2021-09-04 Online:2022-06-15 Published:2022-06-08
  • About author:CHENG Xiang-ming,born in 1996,postgraduate.His main research interests include computer vision and model compression.
    DENG Chun-hua,born in 1984,Ph.D,associate professor.His main research interests include computer vision and machine learning.
  • Supported by:
    National Natural Science Foundation of China(61806150).

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Abstract: When transplanting face recognition technology to mobile devices,it often needs to be processed by accelerated algorithms such as model compression.Knowledge distillation is a model compression method that has a wide range of practical applications and is easy to train.Existing knowledge distillation algorithms require a large amount of tagged face data,which may involve security issues such as identity privacy leakage.At the same time,the cost of large-scale collection of tagged face data is relatively high,while the massive amount of unlabeled face data that can be collected or generated cannot be used.In order to solve the above problems,this paper analyzes the characteristics of knowledge distillation in face recognition tasks,and proposes an indirect supervised training method of unlabeled knowledge distillation.This method can utilize massive amounts of unlabeled face data,thereby avoiding security risks such as privacy leakage.However,the data distribution of the unlabeled face data set is unpredictable,and there is the problem of uneven data distribution,which limits the performance of the indirect supervision algorithm.This research further proposes a data enhancement method for face content replacement,which balances the distribution of face data by replacing part of the content of the face,and at the same time enhances the diversity of face data.Sufficient experimental results show that when the face recognition model is greatly compressed,the performance of the algorithm in this research reaches an advanced level,and surpasses the large-scale network on the LFW data set.

Key words: Content replacement, Face recognition, Indirect supervision, Knowledge distillation, Model compression

CLC Number: 

  • TP391
[1] SUN C,SHRIVASTAVA A,SINGH S,et al.Revisiting unreasonable effectiveness of data in deep learning era[C]//Procee-dings of the IEEE International Conference on Computer Vision.2017:843-852.
[2] IANDOLA F N,HAN S,MOSKEWICZ M W,et al.Squeeze-Net:AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size[J].arXiv:1602.07360,2016.
[3] HOWARD A G,ZHU M,CHEN B,et al.Mobilenets:Efficient convolutional neural networks for mobile vision applications[J].arXiv:1704.04861,2017.
[4] ZHANG X,ZHOU X,LIN M,et al.Shufflenet:An extremelyefficient convolutional neural network for mobile devices[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:6848-6856.
[5] HINTON G,VINYALS O,DEAN J.Distilling the knowledge ina neural network[J].arXiv:1503.02531,2015.
[6] WANG R Z,GAO J,HUANG S H,et al.Malicious Code Family Detection Method Based on Knowledge Distillation[J].Compu-ter Science,2021,48(1):280-286.
[7] LECUN Y,BENGIO Y,HINTON G.Deep learning[J].Nature,2015,521(7553):436-444.
[8] GUO Y,ZHANG L,HU Y,et al.Ms-celeb-1m:A dataset and benchmark for large-scale face recognition[C]//European Conference on Computer Vision.2016:87-102.
[9] DATTAS,SHARMA G,JAWAHAR C V.Unsupervised lear-ning of face representations[C]//2018 13th IEEE International Conference on Automatic Face & Gesture Recognition(FG 2018).IEEE,2018:135-142.
[10] YU H,FAN Y,CHEN K,et al.Unknownidentity rejection loss:Utilizing unlabeled data for face recognition[C]//2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).IEEE Computer Society,2019:2662-2669.
[11] HUANG C,LOY C C,TANG X.Unsupervised learning of discriminative attributes and visual representations[C]//Procee-dings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:5175-5184.
[12] SCHMARJE L,SANTAROSSA M,SCHRÖDER S M,et al.A survey on semi-,self-and unsupervised techniques in image classification[J].arXiv:2002.08721,2020.
[13] MASI I,WU Y,HASSNER T,et al.Deep face recognition:Asurvey[C]//2018 31st SIBGRAPI Conference on Graphics,Patterns and Images (SIBGRAPI).IEEE,2018:471-478.
[14] JI C M,SONG T C.Sparse Representation-Based Classification Under Optimization Forms for Face Recognition[ J].Journal of Chongqing University of Technology(Natural Science),2020,34(2):120-126.
[15] KE P F,CAI M G,WU T.Face Recognition Algorithm Based on Improved Convolutional Neural Network and Ensemble Lear-ning[J].Computer Engineering,2020,46(2):262-267,273.
[16] TAO S F,LI Y F,HUANG Y F,et al.Face Detection Algo-rithm Based on Deep Residual Network and Attention Mechanism[J].Computer Engineering,2021,47(11):276-282.
[17] PARKHI O M,VEDALDI A,ZISSERMAN A.Deepface recognition[C]//Proceedings of the British Machine Vision Confe-rence.2015:41.1-41.12.
[18] NOROOZI M,VINJIMOOR A,FAVARO P,et al.Boostingself-supervised learning via knowledge transfer[C]//Procee-dings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:9359-9367.
[19] VONGKULBHISAL J,VINAYAVEKHIN P,VISENTINI-SCARZANELLA M.Unifying heterogeneous classifiers with distillation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2019:3175-3184.
[20] LUO P,ZHU Z,LIU Z,et al.Face model compression by distilling knowledge from neurons[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2016:3560-3566.
[21] HU J,SHEN L,SUN G.Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:7132-7141.
[22] DENG J,GUO J,XUE N,et al.Arcface:Additive angular margin loss for deep face recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2019:4690-4699.
[23] WANG H,WANG Y,ZHOU Z,et al.Cosface:Large margin cosine loss for deep face recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:5265-5274.
[24] DEVRIES T,TAYLOR G W.Improved regularization of convolutional neural networks with cutout[J].arXiv:1708.04552,2017.
[25] YUN S,HAN D,OH S J,et al.Cutmix:Regularization strategy to train strong classifiers with localizable features[C]//Procee-dings of the IEEE International Conference on Computer Vision.2019:6023-6032.
[26] ZHANG K,ZHANG Z,LI Z,et al.Joint face detection andalignment using multitask cascaded convolutional networks[J].IEEE Signal Processing Letters,2016,23(10):1499-1503.
[27] WANG F,XIANG X,CHENG J,et al.Normface:L2 hyper-sphere embedding for face verification[C]//Proceedings of the 25th ACM International Conference on Multimedia.2017:1041-1049.
[28] MIRZADEH S I,FARAJTABAR M,LI A,et al.Improvedknowledge distillation via teacher assistant[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2020:5191-5198.
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