跨模态异构行人再识别的研究进展

doi:10.16451/j.cnki.issn1003-6059.202012002

Abstract
Figure/Table
References (98)
Related Citation (15)

Download: PDF (952 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract

Homogeneous person re-identification(ReID) based on RGB images is well researched.With the development of public security and pedestrian retrieval in complex real-world situations,the field of pedestrian matching based on multi-modal heterogeneous data sources,called cross-modality heterogeneous ReID,is explored.In this paper,heterogeneous ReID and the differences from homogeneous ReID are firstly summarized.Then,heterogeneous ReID is described under six types of scenarios,including text-to-image,images-to-video,cross-resolution,infrared-visible,RGB-depth and sketch-photo.The relevant popular datasets,representative algorithms and excellent performance of these algorithms on datasets are summarized and reorganized.Finally,the current research progress and the future research trends of cross-modality heterogeneous person re-identification are discussed.

Key words： Person Re-identification(ReID) Heterogeneous Person Re-identification(Hetero-ReID) Cross-Modality Metric Learning Feature Representation Deep Learning

Received: 13 October 2020

Fund:

General Project of National Natural Science Foundation of China(No.61471154,61876057),Key Research and Development Plan of Anhui Province(No.202004D07020012)

Corresponding Authors: SUN Rui,Ph.D.,professor.His research interests include computer vision and machine learning.

About author:: ZHAO Zhenghui,master student.Her research interests include image information processing and computer vision;YANG Zi,master student.Her research interests include image information processing and computer vision;GAO Jun,Ph.D.,professor.His research interests include intelligent information processing and machine vision.

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	SUN Rui
	ZHAO Zhenghui
	YANG Zi
	GAO Jun

Cite this article:

SUN Rui,ZHAO Zhenghui,YANG Zi等. A Survey on Cross-Modality Heterogeneous Person Re-identification[J]. , 2020, 33(12): 1066-1082.

URL:

http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202012002 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2020/V33/I12/1066

[1] GHEISSARI N,SEBASTIAN T B,HARTLEY R.Person Re-identification Using Spatiotemporal Appearance//Proc of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Washington,USA:IEEE,2006:1528-1535.
[2] LUO H,JIANG W,GU Y Z,et al.A Strong Baseline and Batch Normalization Neck for Deep Person Re-identification.IEEE Transactions on Multimedia,2020,22(10):2597-2609.
[3] ZHENG L,HUANG Y J,LU H C,et al.Pose-Invariant Embedding for Deep Person Re-identification.IEEE Transactions on Image Processing,2019,28(9):4500-4509
[4] ZHOU S P,WANG F,HUANG Z Y,et al.Discriminative Feature Learning with Consistent Attention Regularization for Person Re-identification//Proc of the IEEE/CVF International Conference on Computer Vision.Washington,USA:IEEE,2019.8039-8048.
[5] ZHANG Z Z,LAN C L,ZENG W J,et al.Relation-Aware Global Attention for Person Re-identification//Proc of the IEEE/CVF Confe-rence on Computer Vision and Pattern Recognition.Washington,USA:IEEE,2020:3186-3195.
[6] ZHU Z H,JIANG X Y,ZHENG F,et al.Viewpoint-Aware Loss with Angular Regularization for Person Re-identification//Proc of the 34th AAAI Conference on Artificial Intelligence.Palo Alto,USA:AAAI Press,2020:13114-13121.
[7] ZHENG L,SHEN L Y,TIAN L,et al.Scalable Person Re-identification:A Benchmark//Proc of the IEEE International Conference on Computer Vision.Washington,USA:IEEE,2015:1116-1124.
[8] ZHANG X,LUO H,FAN X,et al.AlignedReID:Surpassing Human-Level Performance in Person Re-identification[C/OL].[2020-09-30].https://arxiv.org/pdf/1711.08184.pdf.
[9] ZHANG Z Z,LAN C L,ZENG W J,et al.Densely Semantically Aligned Person Re-identification//Proc of the IEEE/CVF Confe-rence on Computer Vision and Pattern Recognition.Washington,USA:IEEE,2019:667-676.
[10] ZENG Z L,WANG Z X,WANG Z,et al.Illumination-Adaptive Person Re-identification.IEEE Transactions on Multimedia,2020,22(12):3064-3074.
[11] ZHENG W S,LI X,XIANG T,et al.Partial Person Re-identification//Proc of the IEEE International Conference on Computer Vision.Washington,USA:IEEE,2015:4678-4686.
[12] HUANG B Y,CHEN J,WANG Y M,et al.Sparsity-Based Occlusion Handling Method for Person Re-identification//Proc of the International Conference on Multimedia Modeling.Berlin,Germany: Springer,2015:61-73.
[13] WANG X G,DORETTO G,SEBASTIAN T,et al.Shape and Appearance Context Modeling//Proc of the 11th IEEE International Conference on Computer Vision.Washington,USA:IEEE,2007.DOI:10.1109/ICCV.2007.4409019.
[14] FERIS R,BOBBITT R,BROWN L,et al.Attribute-Based People Search:Lessons Learnt from a Practical Surveillance System//Proc of the International Conference on Multimedia Retrieval.New York,USA:ACM,2014:153-160.
[15] SHI Z Y,HOSPEDALES T M,XIANG T.Transferring a Semantic Representation for Person Re-identification and Search//Proc of the IEEE Conference on Computer Vision and Pattern Recognition.Washington,USA:IEEE,2015:4184-4193.
[16] WANG Z,HU R M,YU Y,et al.Multi-level Fusion for Person Re-identification with Incomplete Marks//Proc of the 23rd ACM International Conference on Multimedia.New York,USA:ACM,2015:1267-1270.
[17] YIN Z,ZHENG W S,WU A C,et al.Adversarial Attribute-Image Person Re-identification//Proc of the 27th International Joint Conference on Artificial Intelligence.New York,USA:ACM,2018:1100-1106.
[18] WU J J,LIU H,JIANG J G,et al.Person Attribute Recognition by Sequence Contextual Relation Learning.IEEE Transactions on Circuits and Systems for Video Technology,2020,30(10):3398-3412.
[19] SU C,YANG F,ZHANG S L,et al.Deep Attributes Driven Multi-camera Person Re-identification//Proc of the European Conference on Computer Vision.Berlin,Germany:Springer,2016:475-491.
[20] SU C,YANG F,ZHANG S L,et al.Multi-task Learning with Low Rank Attribute Embedding for Multi-camera Person Re-identification.IEEE Transactions on Pattern Analysis and Machine Intelligence,2018,40(5):1167-1181.
[21] DENG Y B,LUO P,LOY C C,et al.Pedestrian Attribute Recognition at Far Distance//Proc of the 22nd ACM International Conference on Multimedia.New York,USA:ACM,2014:789-792.
[22] YE M,SHEN J B,LIN C J,et al.Deep Learning for Person Re-identification:A Survey and Outlook[C/OL].[2020-09-30].https://arxiv.org/pdf/2001.04193v1.pdf.
[23] ZHENG L,YANG Y,HAUPTMANN A G.Person Re-identification:Past,Present and Future[C/OL].[2020-09-30].https://arxiv.org/pdf/1610.02984.pdf.
[24] YE M,LIANG C,WANG Z,et al.Specific Person Retrieval via Incomplete Text Description//Proc of the 5th ACM International Conference on Multimedia Retrieval.New York,USA:ACM,2015:547-550.
[25] LI S,XIAO T,LI H S,et al.Person Search with Natural Language Description//Proc of the IEEE Conference on Computer Vision and Pattern Recognition.Washington,USA:IEEE,2017:5187-5196.
[26] LIU J W,ZHA Z J,HONG R C,et al.Deep Adversarial Graph Attention Convolution Network for Text-Based Person Search//Proc of the 27th ACM International Conference on Multimedia.New York,USA:ACM,2019:665-673.
[27] LI S,XIAO T,LI H S,et al.Identity-Aware Textual-Visual Mat-ching with Latent Co-attention//Proc of the IEEE International Conference on Computer Vision.Washington,USA:IEEE,2017:1908-1917.
[28] ZHANG Y,LU H C.Deep Cross-Modal Projection Learning for Image-Text Matching//Proc of the 15th European Conference on Computer Vision.Berlin,Germany:Springer,2018:708-723.
[29] CHEN T L,XU C L,LUO J B.Improving Text-Based Person Search by Spatial Matching and Adaptive Threshold//Proc of the IEEE Winter Conference on Applications of Computer Vision.Washington,USA:IEEE,2018:1879-1887.
[30] CHEN D P,LI H S,LIU X H,et al.Improving Deep Visual Re-presentation for Person Re-identification by Global and Local Image-Language Association//Proc of the European Conference on Computer Vision.Berlin,Germany:Springer,2018:56-73.
[31] NIU K,HUANG Y,OUYANG W L,et al.Improving Description-based Person Re-identification by Multi-granularity Image-Text Alignments.IEEE Transactions on Image Processing,2020,29:5542-5556.
[32] LI D W,ZHANG Z,CHEN X T,et al.A Richly Annotated Dataset for Pedestrian Attribute Recognition[C/OL].[2020-09-30].https://arxiv.org/pdf/1603.07054.pdf.
[33] SHARMA G,JURIE F.Learning Discriminative Spatial Representation for Image Classification[C/OL].[2020-09-30].https://hal.inria.fr/hal-00722820/document.
[34] LI Y N,HUANG C,LOY C C,et al.Human Attribute Recognition by Deep Hierarchical Contexts//Proc of the European Confe-rence on Computer Vision.Berlin,Germany:Springer,2016:684-700.
[35] LIU X H,ZHAO H Y,TIAN M Q,et al.HydraPlus-Net:Attentive Deep Features for Pedestrian Analysis//Proc of the IEEE International Conference on Computer Vision.Washington,USA:IEEE,2017:350-359.
[36] LIN Y T,ZHENG L,ZHENG Z D,et al.Improving Person Re-identification by Attribute and Identity Learning.Pattern Recognition,2019,95:151-161.
[37] WANG X,ZHENG S F,YANG R,et al.Pedestrian Attribute Recognition:A Survey[C/OL].[2020-09-30].https://arxiv.org/pdf/1901.07474.pdf.
[38] GRAY D,BRENNAN S,TAO H.Evaluating Appearance Models for Recognition,Reacquisition,and Tracking//Proc of the 10th International Workshop on Performance Evaluation for Tracking and Surveillance.Washington,USA:IEEE,2007:1-6.
[39] LI W,ZHAO R,XIAO T,et al.Deep ReID:Deep Filter Pairing Neural Network for Person Re-identification//Proc of the IEEE Conference on Computer Vision and Pattern Recognition.Washington,USA:IEEE, 2014:152-159.
[40] YOU J J,WU A C,LI X,et al.Top-Push Video-Based Person Re-identification//Proc of the IEEE Conference on Computer Vision and Pattern Recognition.Washington,USA:IEEE,2016:1345-1353.
[41] GU X Q,CHANG H,MA B P,et al.Appearance-Preserving 3D Convolution for Video-Based Person Re-identification[C/OL].[2020-09-30].https://arxiv.org/pdf/2007.08434.pdf.
[42] ZHU X K,JIANG X Y,WU F,et al.Learning Heterogeneous Dictionary Pair with Feature Projection Matrix for Pedestrian Video Retrieval via Single Query Image//Proc of the 31st AAAI Confe-rence on Artificial Intelligence.Palo Alto,USA:AAAI Press,2017:4341-4347.
[43] ZHANG D Y,WU W X,CHENG H,et al.Image-to-Video Person Re-identification with Temporally Memorized Similarity Learning.IEEE Transactions on Circuits and Systems for Video Technology,2018,28(10):2622-2632.
[44] WANG G C,LAI J H,XIE X H.P2SNet:Can an Image Match a Video for Person Re-identification in an End-to-End Way? IEEE Transactions on Circuits and Systems for Video Technology,2018,28(10):2777-2787.
[45] GU X Q,MA B P,CHANG H,et al.Temporal Knowledge Pro-pagation for Image-to-Video Person Re-identification//Proc of the IEEE/CVF International Conference on Computer Vision.Wa-shington,USA:IEEE,2019:9646-9655.
[46] ZHANG L,SHI Z L,ZHOU J T,et al.Ordered or Orderless:A Revisit for Video Based Person Re-identification[J/OL].[2020-09-30].https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9018082.
[47] WANG T Q,GONG S G,ZHU X T,et al.Person Re-identification by Video Ranking//Proc of the European Conference on Computer Vision.Berlin,Germany:Springer,2014:688-703.
[48] HIRZER M,BELEZNAI C,ROTH P M,et al.Person Re-identification by Descriptive and Discriminative Classification//Proc of the Scandinavian Conference on Image Analysis.Berlin,Germany:Springer,2011:91-102.
[49] SIMONNET D,LEWANDOWSKI M,VELASTIN S A,et al.Re-identification of Pedestrians in Crowds Using Dynamic Time Warping//Proc of the European Conference on Computer Vision.Berlin,Germany:Springer, 2012:423-432.
[50] JIANG J J,MA X,CHEN C,et al.Single Image Super-Resolution via Locally Regularized Anchored Neighborhood Regression and Nonlocal Means.IEEE Transactions on Multimedia,2017,19(1):15-26.
[51] JIANG J J,CHEN C,MA J Y,et al.SRLSP:A Face Image Super-Resolution Algorithm Using Smooth Regression with Local Structure Prior.IEEE Transactions on Multimedia,2017,19(1):27-40.
[52] JING X Y,ZHU X K,WU F,et al.Super-Resolution Person Re-identification with Semi-coupled Low-Rank Discriminant Dictionary Learning.IEEE Transactions on Image Processing,2017,26(3):1363-1378.
[53] LI X,ZHENG W S,WANG X J,et al.Multi-scale Learning for Low-Resolution Person Re-identification//Proc of the IEEE International Conference on Computer Vision.Washington,USA:IEEE,2015:3765-3773.
[54] WANG Z,HU R M,YU Y,et al.Scale-Adaptive Low-Resolution Person Re-identification via Learning a Discriminating Surface//Proc of the 25th International Joint Conference on Artificial Intelligence.New York,USA:ACM,2016:2669-2675.
[55] LI K,DING Z M,LI S,et al.Discriminative Semi-coupled Projective Dictionary Learning for Low-Resolution Person Re-identification//Proc of the 32nd AAAI Conference on Artificial Intelligence.Palo Alto,USA:AAAI Press,2018:2331-2338.
[56] JIAO J N,ZHENG W S,WU A C,et al.Deep Low-Resolution Person Re-identification//Proc of the 32th AAAI Conference on Artificial Intelligence.Palo Alto,USA:AAAI Press,2018:6967-6974.
[57] ZHU J Y,PARK T,ISOLA P,et al.Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks//Proc of the IEEE International Conference on Computer Vision.Washington,USA:IEEE,2017:2242-2251.
[58] WANG Z,YE M,YANG F,et al.Cascaded SR-GAN for Scale-Adaptive Low Resolution Person Re-identification//Proc of the 27th International Joint Conferences on Artificial Intelligence.New York,USA:ACM,2018:3891-3897.
[59] CHENG Z Y,DONG Q,GONG S G,et al.Inter-task Association Critic for Cross-Resolution Person Re-identification//Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington,USA:IEEE,2020:2602-2612.
[60] CHEN Y C,LI Y J,DU X F,et al.Learning Resolution-Invariant Deep Representations for Person Re-identification//Proc of the 33rd AAAI Conference on Artificial Intelligence.Palo Alto,USA:AAAI Press,2019:8215-8222.
[61] MAO S N,ZHANG S L,YANG M.Resolution-Invariant Person Re-identification[C/OL].[2020-09-30].https://arxiv.org/pdf/1906.09748v2.pdf.
[62] ROTH P M,HIRZER M,KSTINGER M,et al.Mahalanobis Distance Learning for Person Re-identification//GONG S G,CRISTANI M,YAN S C,et al.,eds.Person Re-identification.Berlin,Germany:Springer,2014:247-267.
[63] CHENG D S,CRISTANI M,STOPPA M,et al.Custom Pictorial Structures for Re-identification[C/OL].[2020-09-30].http://www.bmva.org/bmvc/2011/proceedings/paper68/paper68.pdf.
[64] ZHUANG Z J,AI H Z,CHEN L,et al.Cross-Resolution Person Re-identification with Deep Antithetical Learning//Proc of the Asian Conference on Computer Vision.Berlin,Germany:Sprin-ger,2018:233-248.
[65] KAI J L,ARENS M.Local Feature Based Person Re-identification in Infrared Image Sequences//Proc of the 7th IEEE International Conference on Advanced Video and Signal Based Surveillance.Washington,USA:IEEE,2010:448-455.
[66] WU A C,ZHENG W S,YU H X,et al.RGB-Infrared Cross-Modality Person Re-identification//Proc of the IEEE International Conference on Computer Vision.Washington,USA:IEEE,2017:5380-5389.
[67] YE M,LAN X Y,LI J W,et al.Hierarchical Discriminative Learning for Visible Thermal Person Re-identification//Proc of the 32nd AAAI Conference on Artificial Intelligence.Palo Alto,USA:AAAI Press,2018:7501-7508.
[68] YE M,WANG Z,LAN X Y,et al.Visible Thermal Person Re-identification via Dual-Constrained Top-Ranking//Proc of the 27th International Joint Conference on Artificial Intelligence.Berlin,Germany:Springer,2018:1092-1099.
[69] YE M,LAN X Y,WANG Z,et al.Bi-directional Center-Constrained Top-Ranking for Visible Thermal Person Re-identification.IEEE Transactions on Information Forensics and Security,2019,15:407-419.
[70] LU Y,WU Y,LIU B,et al.Cross-Modality Person Re-identification with Shared-Specific Feature Transfer//Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington,USA:IEEE,2020:13376-13386.
[71] YANG F,WANG Z,XIAO J,et al.Mining on Heterogeneous Manifolds for Zero-Shot Cross-Modal Image Retrieval//Proc of the AAAI Conference on Artificial Intelligence.Palo Alto,USA:AAAI Press,2020:12589-12596.
[72] DAI P Y,JI R R,WANG H B,et al.Cross-Modality Person Re-identification with Generative Adversarial Training//Proc of the 27th International Joint Conference on Artificial Intelligence.New York,USA:ACM,2018:677-683.
[73] WANG G A,ZHANG T Z,CHENG J,et al.RGB-Infrared Cross-Modality Person Re-identification via Joint Pixel and Feature Alignment//Proc of the IEEE/CVF International Conference on Computer Vision.Washington,USA:IEEE,2019:3623-3632.
[74] GOODFELLOW I J,POUGET-ABADIE J,MIRZA M,et al.Ge-nerative Adversarial Nets//Proc of the 27th International Confe-rence on Neural Information Processing Systems.Cambridge,USA:The MIT Press,2014:2672-2680.
[75] WANG Z X,WANG Z,ZHENG Y Q,et al.Learning to Reduce Dual-Level Discrepancy for Infrared-Visible Person Re-identification//Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington,USA:IEEE,2019:618-626.
[76] WANG G A,YANG T Z,TANG Y,et al.Cross-Modality Paired-Images Generation for RGB-Infrared Person Re-identification[C/OL].[2020-09-30].https://arxiv.org/pdf/2002.04114.pdf.
[77] LI D G,WEI X,HONG X P,et al.Infrared-Visible Cross-Modal Person Re-identification with an X Modality//Proc of the AAAI Conference on Artificial Intelligence.Palo Alto,USA:AAAI Press,2020:4610-4617.
[78] KANSAL K,SUBRAMANYAM A V,WANG Z,et al.SDL:Spectrum-Disentangled Representation Learning for Visible-Infrared Person Re-identification.IEEE Transactions on Circuits and Systems for Video Technology,2020,30(10):3422-3432.
[79] JIN X,LAN C L,ZENG W J,et al.Style Normalization and Restitution for Generalizable Person Re-identification//Proc of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington,USA:IEEE,2020:3143-3152.
[80] CHOI S,LEE S,KIM Y,et al.Hi-CMD:Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-identification//Proc of the IEEE/CVF Conference on Computer Vision and Pa-ttern Recognition.Washington,USA:IEEE,2020:10254-10263.
[81] NGUYEN D T,HONG H G,KIM K W,et al.Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras.Sensors,2017,17(3):605-633.
[82] MGELMOSE A,BAHNSEN C,MOESLUND T B,et al.Tri-modal Person Re-identification with RGB,Depth and Thermal Features//Proc of the IEEE Conference on Computer Vision and Pa-ttern Recognition Workshops.Washington,USA:IEEE,2013:301-307.
[83] BARBOSA I B,CRISTANI M,BUE A D,et al.Re-identification with RGB-D Sensors//Proc of the European Conference on Computer Vision.Berlin,Germany:Springer,2012:433-442.
[84] MUNARO M,BASSO A,FOSSATI A,et al.3D Reconstruction of Freely Moving Persons for Re-identification with a Depth Sensor//Proc of the IEEE International Conference on Robotics and Automation.Washington,USA:IEEE,2014:4512-4519.
[85] HAQUE A,ALAHI A,LI F F.Recurrent Attention Models for Depth-Based Person Identification//Proc of the IEEE Conference on Computer Vision and Pattern Recognition.Washington,USA:IEEE,2016:1229-1238.
[86] WU A C,ZHENG W S,LAI J H.Robust Depth-Based Person Re-identification.IEEE Transactions on Image Processing,2017,26(6):2588-2603.
[87] HAFNER F M,BHUIYAN A,KOOIJ J,et al.A Cross-Modal Distillation Network for Person Re-identification in RGB-Depth//Proc of the 31th IEEE Conference on Computer Vision and Pattern Recognition.Washington,USA:IEEE,2018:11-18.
[88] KARIANAKIS N,LIU Z C,CHEN Y P,et al.Reinforced Temporal Attention and Split-Rate Transfer for Depth-Based Person Re-identification//Proc of the European Conference on Computer Vision.Berlin,Germany:Springer,2018:737-756.
[89] RAO H C,WANG S,HU X,et al.Self-supervised Gait Encoding with Locality-Aware Attention for Person Re-identification[C/OL].[2020-09-30].https://arxiv.org/pdf/2008.09435v1.pdf.
[90] PALA F,SATTA R,FUMERA G,et al.Multimodal Person Re-identification Using RGB-D Cameras.IEEE Transactions on Circuits and Systems for Video Technology,2016,26(4):788-799.
[91] LIU H,HU L,MA L Q.Online RGB-D Person Re-identification Based on Metric Model Update.CAAI Transactions on Intelligence Technology,2017,2(1):48-55.
[92] HAFNER F M,BHUIYAN A,KOOIJ J F P,et al.RGB-Depth Cross-Modal Person Re-identification//Proc of the 16th IEEE International Conference on Advanced Video and Signal Based Surveillance(AVSS).Washington,USA:IEEE,2019. DOI:10.1109/AVSS.2019.8909838.
[93] HAN H,KLARE B F,BONNEN K,et al.Matching Composite Sketches to Face Photos:A Component-Based Approach.IEEE Transactions on Information Forensics and Security,2013,8(1):191-204.
[94] PANG L,WANG Y W,SONG Y Z,et al.Cross-Domain Adversarial Feature Learning for Sketch Re-identification//Proc of the 26th ACM International Conference on Multimedia.New York,USA:ACM,2018:609-617.
[95] YU Q,LIU F,SONG Y Z,et al.Sketch Me That Shoe//Proc of the IEEE Conference on Computer Vision and Pattern Recognition.Washington,USA:IEEE,2016:799-807.
[96] ZHU Y X,YANG Z,WANG L,et al.Hetero-Center Loss for Cross-Modality Person Re-identification.Neurocomputing,2020,386:97-109.
[97] ZHANG W,WANG X G,TANG X O.Coupled Information-Theoretic Encoding for Face Photo-Sketch Recognition//Proc of the 24th
IEEE Conference on Computer Vision and Pattern Recognition.Washington,USA:IEEE,2011:513-520.
[98] MIRJALILI V,ROSS A.Soft Biometric Privacy:Retaining Biometric Utility of Face Images While Perturbing Gender//Proc of the IEEE International Joint Conference on Biometrics.Washington,USA:IEEE,2017:564-573.