基于图卷积神经网络的完全图人脸聚类

doi:10.11896/jsjkx.201200102

摘要/Abstract

摘要： 人脸聚类是根据不同身份对人脸图像进行分组的方法,主要用于人脸标注和图像管理等领域。针对现有方法中存在大量冗余数据的问题,文中使用一种基于完全图约束和上下文关系进行链接预测的方法。该聚类算法基于图卷积神经网络进行链接预测,结合完全图约束筛选数据,同时在预测的过程中对链接关系进行不断的更新。实验结果显示,结合完全图约束的人脸聚类方法能够在减少冗余数据、加快运行速度的同时,提升聚类的准确率,从而提高聚类的整体效果。

关键词: 链接预测, 人脸聚类, 图卷积神经网络, 完全图约束

Abstract: Face clustering is a method of grouping face images according to different identities,which is mainly used in the fields of face annotation,image management.etc.There is massive redundant data in existing methods.To handle this issue,this paper uses a link prediction method based on complete graph constraint and context relationship.The clustering algorithm is based on graph convolution network for link prediction,combined with complete graph constraints to filter data,and the link relationship is constantly updated in the process of prediction.Experimental results show that the face clustering method combined with complete graph constraint can reduce redundant data,speed up the operation,and improve the accuracy of clustering.Thus it improves the overall performance of clustering.

Key words: Complete graph constraint, Face clustering, Graph convolution network, Link prediction

中图分类号:

TP391.4

王文博, 罗恒利. 基于图卷积神经网络的完全图人脸聚类[J]. 计算机科学, 2021, 48(11A): 275-277. https://doi.org/10.11896/jsjkx.201200102

WANG Wen-bo, LUO Heng-li. Complete Graph Face Clustering Based on Graph Convolution Network[J]. Computer Science, 2021, 48(11A): 275-277. https://doi.org/10.11896/jsjkx.201200102

参考文献

[1]ZHANG Z,LUO P,CHEN C L,et al.Joint face representation adaptation and clustering in videos[C]//European Conference on Computer Vision.Springer,2016:236-251.
[2]SUN Y,WANG X,TANG X.Deep Learning Face Representation by Joint Identification-Verification[C]//Advances in Neural Information Processing Systems.2014:1988-1996.
[3]SUN Y,WANG X,TANG X,et al.Deep Learning Face Representation from Predicting 10 000 Classes[C]//Processing of the IEEE Conference on Computer Vision and Pattern Recognition.2014:1891-1898.
[4]https://github.com/cmusatyalab/openface.
[5]DENG J,GUO J,XU E N,et al.ArcFace:Additive AngularMargin Loss for Deep Face Recognition[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).IEEE,2019.
[6]MACQUEEN J.Some Methods for Classification and Analysis of Multi-Variate Observations[C]//Proc of Berkeley Symposiumon Mathematica lStatistics & Probability.1965.
[7]BIRANT D,KUT A.ST-DBSCAN:An algorithm for clustering spatial-temporal data[J].Data & Knowledge Engineering,2007,60(1):208-221.
[8]SHI Y,OTTO C,JAIN A K.Face clustering:representation and pairwise constraints[J].IEEE Transactions on Information Forensics and Security,2018,13(7):1626-1640.
[9]LIN W,CHEN J,CASTILLO C D,et al.Deep Density Clust-ering of Unconstrained Faces[C]//IEEE/CVF Conference onComputer Vision and Pattern Recognition.2018:8128-8137.
[10]WANG Z D,ZHENG L,LI Y L,et al.Linkage Based Face Clustering via Graph Convolution Network[C]//Processing of the IEEE Conference on Computer Vision and Pattern Recognition.2019:1117-1125.
[11]YANG L,ZHAN X,CHEN D,et al.Learning to Cluster Faces on an Affinity Graph[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition.2019:2293-2301.
[12]KIPF T N,WELLING M.Semi-supervised classification withgraph convolutional networks[J/OL].Machine Learning;Statistics-Machine Learning.2016.https://arxiv.org/abs/1609.02907.
[13]GUO Y,ZHANG L,HU Y,et al.MS-Celeb-1M:A Dataset and Benchmark for Large-Scale Face Recognition[C]//BEuropean Conference on Computer Vision.2016:87-102.
[14]YI D,LEI Z,LIAO S,et al.Learning face representation fromscratch[C]//Processing of the IEEE Conference on Computer Vision and Pattern Recognition.2014.
[15]WHITELAM C,TABORSKY E,BLANTON A,et al.IARPA Janus Benchmark-B Face Dataset[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops(CVPRW).IEEE,2017.
[16]AMIG E,GONZALO J,ARTILES J,et al.A comparison of extrinsic clustering evaluation metrics based on formal constraints[J].Information Retrieval,2009,12(5):613.

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