path: root/site/datasets/final/lfw.csv

index,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year
0,LFW,lfw,0.0,0.0,,,7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22,main,http://pdfs.semanticscholar.org/7de6/e81d775e9cd7becbfd1bd685f4e2a5eebb22.pdf,Labeled Faces in the Wild: A Survey,2015
1,LFW,lfw,38.5336349,-121.79077264,"University of California, Davis",edu,e94dfdc5581f6bc0338e21ad555b5f1734f8697e,citation,https://arxiv.org/pdf/1803.11556.pdf,Learning to Anonymize Faces for Privacy Preserving Action Detection,2018
2,LFW,lfw,-33.88890695,151.18943366,University of Sydney,edu,aa892fe17c06e2b18db2b12314499a741e755df7,citation,https://doi.org/10.1109/IJCNN.2017.7966089,Improved performance of face recognition using CNN with constrained triplet loss layer,2017
3,LFW,lfw,39.329053,-76.619425,Johns Hopkins University,edu,672fae3da801b2a0d2bad65afdbbbf1b2320623e,citation,https://arxiv.org/pdf/1609.07042.pdf,Pose-Selective Max Pooling for Measuring Similarity,2016
4,LFW,lfw,42.718568,-84.47791571,Michigan State University,edu,450c6a57f19f5aa45626bb08d7d5d6acdb863b4b,citation,https://arxiv.org/pdf/1805.00611.pdf,Towards Interpretable Face Recognition,2018
5,LFW,lfw,29.5084174,106.57858552,Chongqing University,edu,a90e6751ae32cb2983891ef2216293311cd6a8e9,citation,https://pdfs.semanticscholar.org/a90e/6751ae32cb2983891ef2216293311cd6a8e9.pdf,Clustering using Ensemble Clustering Technique,2018
6,LFW,lfw,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,9be696618cfcea90879747a8512f21b10cceac48,citation,https://arxiv.org/pdf/1809.02129.pdf,Structural Consistency and Controllability for Diverse Colorization,2018
7,LFW,lfw,37.373444,-121.9648727,"Futurewei Technologies Inc., Santa Clara, CA",company,d8fbd3a16d2e2e59ce0cff98b3fd586863878dc1,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7952553,Face detection and recognition for home service robots with end-to-end deep neural networks,2017
8,LFW,lfw,40.47913175,-74.43168868,Rutgers University,edu,d4448f8aa320f04066cc43201d55ddd023eb712e,citation,https://pdfs.semanticscholar.org/d444/8f8aa320f04066cc43201d55ddd023eb712e.pdf,Clothing Change Aware Person Identification,0
9,LFW,lfw,33.9928298,-81.02685168,University of South Carolina,edu,d4448f8aa320f04066cc43201d55ddd023eb712e,citation,https://pdfs.semanticscholar.org/d444/8f8aa320f04066cc43201d55ddd023eb712e.pdf,Clothing Change Aware Person Identification,0
10,LFW,lfw,31.83907195,117.26420748,University of Science and Technology of China,edu,e1256ff535bf4c024dd62faeb2418d48674ddfa2,citation,https://arxiv.org/pdf/1803.11182.pdf,Towards Open-Set Identity Preserving Face Synthesis,2018
11,LFW,lfw,51.49887085,-0.17560797,Imperial College London,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017
12,LFW,lfw,51.59029705,-0.22963221,Middlesex University,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017
13,LFW,lfw,39.329053,-76.619425,Johns Hopkins University,edu,10e0e6f1ec00b20bc78a5453a00c792f1334b016,citation,http://pdfs.semanticscholar.org/672f/ae3da801b2a0d2bad65afdbbbf1b2320623e.pdf,Temporal Selective Max Pooling Towards Practical Face Recognition,2016
14,LFW,lfw,33.5866784,-101.87539204,Electrical and Computer Engineering,edu,ebb3d5c70bedf2287f9b26ac0031004f8f617b97,citation,https://doi.org/10.1109/MSP.2017.2764116,"Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans",2018
15,LFW,lfw,39.2899685,-76.62196103,University of Maryland,edu,ebb3d5c70bedf2287f9b26ac0031004f8f617b97,citation,https://doi.org/10.1109/MSP.2017.2764116,"Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans",2018
16,LFW,lfw,24.4469025,54.3942563,Khalifa University,edu,0c1d85a197a1f5b7376652a485523e616a406273,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2017.169,Joint Registration and Representation Learning for Unconstrained Face Identification,2017
17,LFW,lfw,-35.23656905,149.08446994,University of Canberra,edu,0c1d85a197a1f5b7376652a485523e616a406273,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2017.169,Joint Registration and Representation Learning for Unconstrained Face Identification,2017
18,LFW,lfw,22.3386304,114.2620337,Hong Kong University of Science and Technology,edu,585260468d023ffc95f0e539c3fa87254c28510b,citation,http://pdfs.semanticscholar.org/5852/60468d023ffc95f0e539c3fa87254c28510b.pdf,Cardea: Context-Aware Visual Privacy Protection from Pervasive Cameras,2016
19,LFW,lfw,55.6801502,12.572327,University of Copenhagen,edu,3dfd94d3fad7e17f52a8ae815eb9cc5471172bc0,citation,http://pdfs.semanticscholar.org/3dfd/94d3fad7e17f52a8ae815eb9cc5471172bc0.pdf,Face2Text: Collecting an Annotated Image Description Corpus for the Generation of Rich Face Descriptions,2018
20,LFW,lfw,35.9023226,14.4834189,University of Malta,edu,3dfd94d3fad7e17f52a8ae815eb9cc5471172bc0,citation,http://pdfs.semanticscholar.org/3dfd/94d3fad7e17f52a8ae815eb9cc5471172bc0.pdf,Face2Text: Collecting an Annotated Image Description Corpus for the Generation of Rich Face Descriptions,2018
21,LFW,lfw,36.00146435,120.11624057,Shandong University of Science and Technology,edu,db84c6fd771a073023f2b42e48a68eb2d9d31e4a,citation,https://pdfs.semanticscholar.org/db84/c6fd771a073023f2b42e48a68eb2d9d31e4a.pdf,A Deep Variational Autoencoder Approach for Robust Facial Symmetrization,2018
22,LFW,lfw,36.16161795,120.49355276,Ocean University of China,edu,db84c6fd771a073023f2b42e48a68eb2d9d31e4a,citation,https://pdfs.semanticscholar.org/db84/c6fd771a073023f2b42e48a68eb2d9d31e4a.pdf,A Deep Variational Autoencoder Approach for Robust Facial Symmetrization,2018
23,LFW,lfw,45.7835966,4.7678948,École Centrale de Lyon,edu,486840f4f524e97f692a7f6b42cd19019ee71533,citation,https://arxiv.org/pdf/1703.08388v2.pdf,DeepVisage: Making Face Recognition Simple Yet With Powerful Generalization Skills,2017
24,LFW,lfw,48.832493,2.267474,Safran Identity and Security,company,486840f4f524e97f692a7f6b42cd19019ee71533,citation,https://arxiv.org/pdf/1703.08388v2.pdf,DeepVisage: Making Face Recognition Simple Yet With Powerful Generalization Skills,2017
25,LFW,lfw,28.2290209,112.99483204,"National University of Defense Technology, China",edu,511a8cdf2127ef8aa07cbdf9660fe9e0e2dfbde7,citation,https://pdfs.semanticscholar.org/511a/8cdf2127ef8aa07cbdf9660fe9e0e2dfbde7.pdf,A Community Detection Approach to Cleaning Extremely Large Face Database,2018
26,LFW,lfw,22.304572,114.17976285,Hong Kong Polytechnic University,edu,50b58becaf67e92a6d9633e0eea7d352157377c3,citation,https://pdfs.semanticscholar.org/50b5/8becaf67e92a6d9633e0eea7d352157377c3.pdf,Dependency-Aware Attention Control for Unconstrained Face Recognition with Image Sets,2018
27,LFW,lfw,39.2899685,-76.62196103,University of Maryland,edu,5865b6d83ba6dbbf9167f1481e9339c2ef1d1f6b,citation,https://doi.org/10.1109/ICPR.2016.7900278,Regularized metric adaptation for unconstrained face verification,2016
28,LFW,lfw,42.718568,-84.47791571,Michigan State University,edu,86204fc037936754813b91898377e8831396551a,citation,https://arxiv.org/pdf/1709.01442.pdf,Dense Face Alignment,2017
29,LFW,lfw,40.48256135,-3.6906079,Universidad Autonoma de Madrid,edu,24b5ea4e262e22768813e7b6581f60e4ab9a8de7,citation,https://doi.org/10.1109/TIFS.2018.2807791,"Facial Soft Biometrics for Recognition in the Wild: Recent Works, Annotation, and COTS Evaluation",2018
30,LFW,lfw,40.3905914,-74.1863851,"Nokia Bell-Labs, Madrid, Spain",company,24b5ea4e262e22768813e7b6581f60e4ab9a8de7,citation,https://doi.org/10.1109/TIFS.2018.2807791,"Facial Soft Biometrics for Recognition in the Wild: Recent Works, Annotation, and COTS Evaluation",2018
31,LFW,lfw,56.66340325,12.87929727,Halmstad University,edu,24b5ea4e262e22768813e7b6581f60e4ab9a8de7,citation,https://doi.org/10.1109/TIFS.2018.2807791,"Facial Soft Biometrics for Recognition in the Wild: Recent Works, Annotation, and COTS Evaluation",2018
32,LFW,lfw,39.2899685,-76.62196103,University of Maryland,edu,def2983576001bac7d6461d78451159800938112,citation,https://arxiv.org/pdf/1705.07426.pdf,The Do’s and Don’ts for CNN-Based Face Verification,2017
33,LFW,lfw,30.19331415,120.11930822,Zhejiang University,edu,35700f9a635bd3c128ab41718b040a0c28d6361a,citation,http://pdfs.semanticscholar.org/3570/0f9a635bd3c128ab41718b040a0c28d6361a.pdf,DeepGait: A Learning Deep Convolutional Representation for View-Invariant Gait Recognition Using Joint Bayesian,2017
34,LFW,lfw,30.2931534,120.1620458,Zhejiang University of Technology,edu,35700f9a635bd3c128ab41718b040a0c28d6361a,citation,http://pdfs.semanticscholar.org/3570/0f9a635bd3c128ab41718b040a0c28d6361a.pdf,DeepGait: A Learning Deep Convolutional Representation for View-Invariant Gait Recognition Using Joint Bayesian,2017
35,LFW,lfw,43.614386,7.071125,EURECOM,edu,1648cf24c042122af2f429641ba9599a2187d605,citation,https://doi.org/10.1109/BTAS.2017.8272698,Boosting cross-age face verification via generative age normalization,2017
36,LFW,lfw,1.3037257,103.7737763,"Advanced Digital Sciences Center, Singapore",edu,856cc83a3121de89d4a6d9283afbcd5d7ef7aa2b,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6417014,Image-to-Set Face Recognition Using Locality Repulsion Projections and Sparse Reconstruction-Based Similarity Measure,2013
37,LFW,lfw,1.3484104,103.68297965,Nanyang Technological University,edu,856cc83a3121de89d4a6d9283afbcd5d7ef7aa2b,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6417014,Image-to-Set Face Recognition Using Locality Repulsion Projections and Sparse Reconstruction-Based Similarity Measure,2013
38,LFW,lfw,51.7534538,-1.25400997,University of Oxford,edu,30180f66d5b4b7c0367e4b43e2b55367b72d6d2a,citation,http://www.robots.ox.ac.uk/~vgg/publications/2017/Crosswhite17/crosswhite17.pdf,Template Adaptation for Face Verification and Identification,2017
39,LFW,lfw,31.83907195,117.26420748,University of Science and Technology of China,edu,3107316f243233d45e3c7e5972517d1ed4991f91,citation,http://arxiv.org/abs/1703.10155,CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training,2017
40,LFW,lfw,49.10184375,8.4331256,Karlsruhe Institute of Technology,edu,10f66f6550d74b817a3fdcef7fdeba13ccdba51c,citation,http://pdfs.semanticscholar.org/10f6/6f6550d74b817a3fdcef7fdeba13ccdba51c.pdf,Benchmarking Face Alignment,2011
41,LFW,lfw,39.2899685,-76.62196103,University of Maryland,edu,19458454308a9f56b7de76bf7d8ff8eaa52b0173,citation,https://pdfs.semanticscholar.org/1945/8454308a9f56b7de76bf7d8ff8eaa52b0173.pdf,Deep Features for Recognizing Disguised Faces in the Wild,0
42,LFW,lfw,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,44b827df6c433ca49bcf44f9f3ebfdc0774ee952,citation,https://doi.org/10.1109/LSP.2017.2726105,Deep Correlation Feature Learning for Face Verification in the Wild,2017
43,LFW,lfw,37.4102193,-122.05965487,Carnegie Mellon University,edu,a0b1990dd2b4cd87e4fd60912cc1552c34792770,citation,https://pdfs.semanticscholar.org/a0b1/990dd2b4cd87e4fd60912cc1552c34792770.pdf,Deep Constrained Local Models for Facial Landmark Detection,2016
44,LFW,lfw,30.284151,-97.73195598,University of Texas at Austin,edu,3c57e28a4eb463d532ea2b0b1ba4b426ead8d9a0,citation,http://pdfs.semanticscholar.org/73cc/fdedbd7d72a147925727ba1932f9488cfde3.pdf,Defeating Image Obfuscation with Deep Learning,2016
45,LFW,lfw,40.8080562,29.3561202,"Gebze Technical University, Turkey",edu,d3a3d15a32644beffaac4322b9f165ed51cfd99b,citation,https://doi.org/10.1109/SIU.2016.7496197,Eye detection by using deep learning,2016
46,LFW,lfw,40.8419836,-73.94368971,Columbia University,edu,b13bf657ca6d34d0df90e7ae739c94a7efc30dc3,citation,http://pdfs.semanticscholar.org/b13b/f657ca6d34d0df90e7ae739c94a7efc30dc3.pdf,Attribute and Simile Classifiers for Face Verification (In submission please do not distribute.),2009
47,LFW,lfw,33.776033,-84.39884086,Georgia Institute of Technology,edu,93af36da08bf99e68c9b0d36e141ed8154455ac2,citation,https://pdfs.semanticscholar.org/93af/36da08bf99e68c9b0d36e141ed8154455ac2.pdf,A Dditive M Argin S Oftmax for F Ace V Erification,2018
48,LFW,lfw,40.0141905,-83.0309143,University of Electronic Science and Technology of China,edu,93af36da08bf99e68c9b0d36e141ed8154455ac2,citation,https://pdfs.semanticscholar.org/93af/36da08bf99e68c9b0d36e141ed8154455ac2.pdf,A Dditive M Argin S Oftmax for F Ace V Erification,2018
49,LFW,lfw,31.4006332,74.2137296,"COMSATS Institute of Information Technology, Lahore",edu,280bc9751593897091015aaf2cab39805768b463,citation,http://pdfs.semanticscholar.org/280b/c9751593897091015aaf2cab39805768b463.pdf,Gender Perception From Faces Using Boosted LBPH (Local Binary Patten Histograms),2013
50,LFW,lfw,55.91029135,-3.32345777,Heriot-Watt University,edu,e57ce6244ec696ff9aa42d6af7f09eed176153a8,citation,https://doi.org/10.1109/ICIP.2015.7351449,Instantaneous real-time head pose at a distance,2015
51,LFW,lfw,36.3697191,127.362537,Korea Advanced Institute of Science and Technology,edu,bd9e0b6a90b51cc19b65f51dacd08ce1a7ccaac5,citation,https://doi.org/10.1109/VSMM.2014.7136653,Avatar recommendation method based on facial attributes,2014
52,LFW,lfw,32.1119889,34.80459702,Tel Aviv University,edu,9821669a989a3df9d598c1b4332d17ae8e35e294,citation,http://pdfs.semanticscholar.org/9821/669a989a3df9d598c1b4332d17ae8e35e294.pdf,Minimal Correlation Classification,2012
53,LFW,lfw,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,04661729f0ff6afe4b4d6223f18d0da1d479accf,citation,http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.419,From Facial Parts Responses to Face Detection: A Deep Learning Approach,2015
54,LFW,lfw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,04661729f0ff6afe4b4d6223f18d0da1d479accf,citation,http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.419,From Facial Parts Responses to Face Detection: A Deep Learning Approach,2015
55,LFW,lfw,42.718568,-84.47791571,Michigan State University,edu,1d5aad4f7fae6d414ffb212cec1f7ac876de48bf,citation,https://doi.org/10.1109/ICB.2015.7139112,Face retriever: Pre-filtering the gallery via deep neural net,2015
56,LFW,lfw,22.304572,114.17976285,Hong Kong Polytechnic University,edu,03babadaaa7e71d4b65203e27e8957db649155c6,citation,https://doi.org/10.1109/TIP.2017.2725578,Distance Metric Learning via Iterated Support Vector Machines,2017
57,LFW,lfw,34.250803,108.983693,Xi’an Jiaotong University,edu,03babadaaa7e71d4b65203e27e8957db649155c6,citation,https://doi.org/10.1109/TIP.2017.2725578,Distance Metric Learning via Iterated Support Vector Machines,2017
58,LFW,lfw,23.09461185,113.28788994,Sun Yat-Sen University,edu,03babadaaa7e71d4b65203e27e8957db649155c6,citation,https://doi.org/10.1109/TIP.2017.2725578,Distance Metric Learning via Iterated Support Vector Machines,2017
59,LFW,lfw,40.3494632,-74.714815,Educational Testing Service,company,03babadaaa7e71d4b65203e27e8957db649155c6,citation,https://doi.org/10.1109/TIP.2017.2725578,Distance Metric Learning via Iterated Support Vector Machines,2017
60,LFW,lfw,45.7413921,126.62552755,Harbin Institute of Technology,edu,03babadaaa7e71d4b65203e27e8957db649155c6,citation,https://doi.org/10.1109/TIP.2017.2725578,Distance Metric Learning via Iterated Support Vector Machines,2017
61,LFW,lfw,30.2931534,120.1620458,Zhejiang University of Technology,edu,20eabf10e9591443de95b726d90cda8efa7e53bb,citation,https://doi.org/10.1007/s11390-017-1740-0,Discriminative Histogram Intersection Metric Learning and Its Applications,2017
62,LFW,lfw,33.8898728,130.70856205,Waseda University,edu,20eabf10e9591443de95b726d90cda8efa7e53bb,citation,https://doi.org/10.1007/s11390-017-1740-0,Discriminative Histogram Intersection Metric Learning and Its Applications,2017
63,LFW,lfw,-27.49741805,153.01316956,University of Queensland,edu,0cdb49142f742f5edb293eb9261f8243aee36e12,citation,http://arxiv.org/abs/1303.2783,Combined Learning of Salient Local Descriptors and Distance Metrics for Image Set Face Verification,2012
64,LFW,lfw,30.481761,114.31096,Hubei University,edu,24b637c98b22cd932f74acfeecdb50533abea9ae,citation,https://doi.org/10.1109/TIP.2015.2492819,Robust Face Recognition via Minimum Error Entropy-Based Atomic Representation,2015
65,LFW,lfw,22.1240187,113.54510901,University of Macau,edu,24b637c98b22cd932f74acfeecdb50533abea9ae,citation,https://doi.org/10.1109/TIP.2015.2492819,Robust Face Recognition via Minimum Error Entropy-Based Atomic Representation,2015
66,LFW,lfw,41.8361963,-87.62655913,Illinois Institute of Technology,edu,9d66de2a59ec20ca00a618481498a5320ad38481,citation,http://www.cs.iit.edu/~xli/paper/Conf/POP-ICDCS15.pdf,POP: Privacy-Preserving Outsourced Photo Sharing and Searching for Mobile Devices,2015
67,LFW,lfw,40.00229045,116.32098908,Tsinghua University,edu,9d66de2a59ec20ca00a618481498a5320ad38481,citation,http://www.cs.iit.edu/~xli/paper/Conf/POP-ICDCS15.pdf,POP: Privacy-Preserving Outsourced Photo Sharing and Searching for Mobile Devices,2015
68,LFW,lfw,32.42143805,-81.78450529,Georgia Southern University,edu,2a98b850139b911df5a336d6ebf33be7819ae122,citation,https://doi.org/10.1109/ICIP.2015.7350806,Maximum entropy regularized group collaborative representation for face recognition,2015
69,LFW,lfw,23.09461185,113.28788994,Sun Yat-Sen University,edu,2a98b850139b911df5a336d6ebf33be7819ae122,citation,https://doi.org/10.1109/ICIP.2015.7350806,Maximum entropy regularized group collaborative representation for face recognition,2015
70,LFW,lfw,37.3351908,-121.88126008,San Jose State University,edu,14b016c7a87d142f4b9a0e6dc470dcfc073af517,citation,http://ws680.nist.gov/publication/get_pdf.cfm?pub_id=918912,Modest proposals for improving biometric recognition papers,2015
71,LFW,lfw,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,0aeb5020003e0c89219031b51bd30ff1bceea363,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.525,Sparsifying Neural Network Connections for Face Recognition,2016
72,LFW,lfw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,0aeb5020003e0c89219031b51bd30ff1bceea363,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.525,Sparsifying Neural Network Connections for Face Recognition,2016
73,LFW,lfw,52.22165395,21.00735776,Warsaw University of Technology,edu,3ff79cf6df1937949cc9bc522041a9a39d314d83,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8406730,Adversarial examples: A survey,2018
74,LFW,lfw,40.51865195,-74.44099801,State University of New Jersey,edu,676f9eabf4cfc1fd625228c83ff72f6499c67926,citation,http://pdfs.semanticscholar.org/676f/9eabf4cfc1fd625228c83ff72f6499c67926.pdf,Face Identification and Clustering,2017
75,LFW,lfw,49.10184375,8.4331256,Karlsruhe Institute of Technology,edu,9ed4ad41cbad645e7109e146ef6df73f774cd75d,citation,http://pdfs.semanticscholar.org/a83e/175ad5b2066e207f5d2ec830ae05bac266b9.pdf,RPM: Random Points Matching for Pair wise Face-Similarity,2013
76,LFW,lfw,47.3764534,8.54770931,ETH Zürich,edu,9ed4ad41cbad645e7109e146ef6df73f774cd75d,citation,http://pdfs.semanticscholar.org/a83e/175ad5b2066e207f5d2ec830ae05bac266b9.pdf,RPM: Random Points Matching for Pair wise Face-Similarity,2013
77,LFW,lfw,42.3889785,-72.5286987,University of Massachusetts,edu,33ad23377eaead8955ed1c2b087a5e536fecf44e,citation,http://vis-www.cs.umass.edu/papers/gloc_cvpr13.pdf,Augmenting CRFs with Boltzmann Machine Shape Priors for Image Labeling,2013
78,LFW,lfw,42.2942142,-83.71003894,University of Michigan,edu,33ad23377eaead8955ed1c2b087a5e536fecf44e,citation,http://vis-www.cs.umass.edu/papers/gloc_cvpr13.pdf,Augmenting CRFs with Boltzmann Machine Shape Priors for Image Labeling,2013
79,LFW,lfw,40.62984145,22.9588935,Aristotle University of Thessaloniki,edu,e8aa1f207b4b0bb710f79ab47a671d5639696a56,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7362364,Exploiting symmetry in two-dimensional clustering-based discriminant analysis for face recognition,2015
80,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,adce9902dca7f4e8a9b9cf6686ec6a7c0f2a0ba6,citation,http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.435,"Two Birds, One Stone: Jointly Learning Binary Code for Large-Scale Face Image Retrieval and Attributes Prediction",2015
81,LFW,lfw,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,adce9902dca7f4e8a9b9cf6686ec6a7c0f2a0ba6,citation,http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.435,"Two Birds, One Stone: Jointly Learning Binary Code for Large-Scale Face Image Retrieval and Attributes Prediction",2015
82,LFW,lfw,42.3504253,-71.10056114,Boston University,edu,fe961cbe4be0a35becd2d722f9f364ec3c26bd34,citation,http://pdfs.semanticscholar.org/fe96/1cbe4be0a35becd2d722f9f364ec3c26bd34.pdf,"Computer-based Tracking, Analysis, and Visualization of Linguistically Significant Nonmanual Events in American Sign Language (ASL)",2014
83,LFW,lfw,40.47913175,-74.43168868,Rutgers University,edu,fe961cbe4be0a35becd2d722f9f364ec3c26bd34,citation,http://pdfs.semanticscholar.org/fe96/1cbe4be0a35becd2d722f9f364ec3c26bd34.pdf,"Computer-based Tracking, Analysis, and Visualization of Linguistically Significant Nonmanual Events in American Sign Language (ASL)",2014
84,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,b98e7a8f605c21e25ac5e32bfb1851a01f30081b,citation,http://doi.acm.org/10.1145/2393347.2396303,Deep nonlinear metric learning with independent subspace analysis for face verification,2012
85,LFW,lfw,30.572815,104.066801,University of the Chinese Academy of Sciences,edu,3d89f9b4da3d6fb1fdb33dea7592b5992069a096,citation,https://doi.org/10.1109/CISP-BMEI.2017.8302003,Face recognition based on convolution siamese networks,2017
86,LFW,lfw,25.01682835,121.53846924,National Taiwan University,edu,e51927b125640bfc47bbf1aa00c3c026748c75bd,citation,http://doi.acm.org/10.1145/2647868.2655015,Automatic Facial Image Annotation and Retrieval by Integrating Voice Label and Visual Appearance,2014
87,LFW,lfw,1.2962018,103.77689944,National University of Singapore,edu,4e8c608fc4b8198f13f8a68b9c1a0780f6f50105,citation,http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Yang_How_Related_Exemplars_2013_ICCV_paper.pdf,How Related Exemplars Help Complex Event Detection in Web Videos?,2013
88,LFW,lfw,-27.49741805,153.01316956,University of Queensland,edu,4e8c608fc4b8198f13f8a68b9c1a0780f6f50105,citation,http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Yang_How_Related_Exemplars_2013_ICCV_paper.pdf,How Related Exemplars Help Complex Event Detection in Web Videos?,2013
89,LFW,lfw,37.4102193,-122.05965487,Carnegie Mellon University,edu,4e8c608fc4b8198f13f8a68b9c1a0780f6f50105,citation,http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Yang_How_Related_Exemplars_2013_ICCV_paper.pdf,How Related Exemplars Help Complex Event Detection in Web Videos?,2013
90,LFW,lfw,37.5901411,127.0362318,Korea University,edu,ce8db0fe11e7c96d08de561506f9f8f399dabbb2,citation,https://doi.org/10.1109/ICIP.2015.7351677,Weighted sparse representation using a learned distance metric for face recognition,2015
91,LFW,lfw,1.2962018,103.77689944,National University of Singapore,edu,2836d68c86f29bb87537ea6066d508fde838ad71,citation,http://arxiv.org/pdf/1510.06503v1.pdf,Personalized Age Progression with Aging Dictionary,2015
92,LFW,lfw,32.0565957,118.77408833,Nanjing University,edu,2836d68c86f29bb87537ea6066d508fde838ad71,citation,http://arxiv.org/pdf/1510.06503v1.pdf,Personalized Age Progression with Aging Dictionary,2015
93,LFW,lfw,40.62984145,22.9588935,Aristotle University of Thessaloniki,edu,3f7cf52fb5bf7b622dce17bb9dfe747ce4a65b96,citation,https://doi.org/10.1109/TMM.2014.2315595,Person Identity Label Propagation in Stereo Videos,2014
94,LFW,lfw,41.9037626,12.5144384,Sapienza University of Rome,edu,dac34b590adddef2fc31f26e2aeb0059115d07a1,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8436078,House in the (Biometric) Cloud: A Possible Application,2018
95,LFW,lfw,41.9037626,12.5144384,Sapienza Univertsity of Rome,edu,dac34b590adddef2fc31f26e2aeb0059115d07a1,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8436078,House in the (Biometric) Cloud: A Possible Application,2018
96,LFW,lfw,-31.95040445,115.79790037,University of Western Australia,edu,210b98394c3be96e7fd75d3eb11a391da1b3a6ca,citation,http://pdfs.semanticscholar.org/210b/98394c3be96e7fd75d3eb11a391da1b3a6ca.pdf,Spatiotemporal Derivative Pattern: A Dynamic Texture Descriptor for Video Matching,2014
97,LFW,lfw,34.8452999,48.5596212,Islamic Azad University,edu,210b98394c3be96e7fd75d3eb11a391da1b3a6ca,citation,http://pdfs.semanticscholar.org/210b/98394c3be96e7fd75d3eb11a391da1b3a6ca.pdf,Spatiotemporal Derivative Pattern: A Dynamic Texture Descriptor for Video Matching,2014
98,LFW,lfw,34.68092465,50.05341352,Tafresh University,edu,210b98394c3be96e7fd75d3eb11a391da1b3a6ca,citation,http://pdfs.semanticscholar.org/210b/98394c3be96e7fd75d3eb11a391da1b3a6ca.pdf,Spatiotemporal Derivative Pattern: A Dynamic Texture Descriptor for Video Matching,2014
99,LFW,lfw,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,125d82fee1b9fbcc616622b0977f3d06771fc152,citation,http://www.ee.cuhk.edu.hk/~xgwang/papers/luoWTcvpr12.pdf,Hierarchical face parsing via deep learning,2012
100,LFW,lfw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,125d82fee1b9fbcc616622b0977f3d06771fc152,citation,http://www.ee.cuhk.edu.hk/~xgwang/papers/luoWTcvpr12.pdf,Hierarchical face parsing via deep learning,2012
101,LFW,lfw,57.01590275,9.97532827,Aalborg University,edu,ccfebdf7917cb50b5fcd56fb837f841a2246a149,citation,https://doi.org/10.1109/ICIP.2015.7351065,A feature subtraction method for image based kinship verification under uncontrolled environments,2015
102,LFW,lfw,43.66333345,-79.39769975,University of Toronto,edu,a538b05ebb01a40323997629e171c91aa28b8e2f,citation,http://pdfs.semanticscholar.org/a538/b05ebb01a40323997629e171c91aa28b8e2f.pdf,Rectified Linear Units Improve Restricted Boltzmann Machines,2010
103,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,4cdb6144d56098b819076a8572a664a2c2d27f72,citation,https://arxiv.org/pdf/1806.01196.pdf,Face Synthesis for Eyeglass-Robust Face Recognition,2018
104,LFW,lfw,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,4cdb6144d56098b819076a8572a664a2c2d27f72,citation,https://arxiv.org/pdf/1806.01196.pdf,Face Synthesis for Eyeglass-Robust Face Recognition,2018
105,LFW,lfw,52.9387428,-1.20029569,University of Nottingham,edu,472ba8dd4ec72b34e85e733bccebb115811fd726,citation,http://pdfs.semanticscholar.org/472b/a8dd4ec72b34e85e733bccebb115811fd726.pdf,Cosine Similarity Metric Learning for Face Verification,2010
106,LFW,lfw,23.0502042,113.39880323,South China University of Technology,edu,6880013eb0b91a2b334e0be0dced0a1a79943469,citation,https://arxiv.org/pdf/1810.11809.pdf,Discrimination-aware Channel Pruning for Deep Neural Networks,2018
107,LFW,lfw,32.7283683,-97.11201835,University of Texas at Arlington,edu,6880013eb0b91a2b334e0be0dced0a1a79943469,citation,https://arxiv.org/pdf/1810.11809.pdf,Discrimination-aware Channel Pruning for Deep Neural Networks,2018
108,LFW,lfw,21.2982795,-157.8186923,University of Hawaii,edu,86afb1e38a96f2ac00e792ef353a971fd13c8474,citation,https://doi.org/10.1109/BigData.2016.7840742,How interesting images are: An atypicality approach for social networks,2016
109,LFW,lfw,42.3626295,-71.0914481,McGovern Institute for Brain Research,edu,0b242d5123f79defd5f775d49d8a7047ad3153bc,citation,http://pdfs.semanticscholar.org/84db/c0010ae4f5206d689cf9f5bb176d18990bcd.pdf,How Important Is Weight Symmetry in Backpropagation?,2016
110,LFW,lfw,33.776033,-84.39884086,Georgia Institute of Technology,edu,e293a31260cf20996d12d14b8f29a9d4d99c4642,citation,https://arxiv.org/pdf/1703.01560.pdf,LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation,2017
111,LFW,lfw,50.89273635,-1.39464295,University of Southampton,edu,6f9824c5cb5ac08760b08e374031cbdabc953bae,citation,https://doi.org/10.1109/BTAS.2016.7791206,Unconstrained human identification using comparative facial soft biometrics,2016
112,LFW,lfw,29.6328784,-82.3490133,University of Florida,edu,21258aa3c48437a2831191b71cd069c05fb84cf7,citation,http://pdfs.semanticscholar.org/2125/8aa3c48437a2831191b71cd069c05fb84cf7.pdf,A Robust and Efficient Doubly Regularized Metric Learning Approach,2012
113,LFW,lfw,46.109237,7.08453549,IDIAP Research Institute,edu,78d645d5b426247e9c8f359694080186681f57db,citation,http://pdfs.semanticscholar.org/78d6/45d5b426247e9c8f359694080186681f57db.pdf,Gender Classification by LUT Based Boosting of Overlapping Block Patterns,2015
114,LFW,lfw,61.44964205,23.85877462,Tampere University of Technology,edu,78d645d5b426247e9c8f359694080186681f57db,citation,http://pdfs.semanticscholar.org/78d6/45d5b426247e9c8f359694080186681f57db.pdf,Gender Classification by LUT Based Boosting of Overlapping Block Patterns,2015
115,LFW,lfw,39.9586652,116.30971281,Beijing Institute of Technology,edu,2a35d20b2c0a045ea84723f328321c18be6f555c,citation,http://pdfs.semanticscholar.org/d1be/cba3c460892453939f9f3639d8beddf2a133.pdf,Boost Picking: A Universal Method on Converting Supervised Classification to Semi-supervised Classification,2016
116,LFW,lfw,29.7207902,-95.34406271,University of Houston,edu,5ef3e7a2c8d2876f3c77c5df2bbaea8a777051a7,citation,https://doi.org/10.1109/ISBA.2016.7477244,Rendering or normalization? An analysis of the 3D-aided pose-invariant face recognition,2016
117,LFW,lfw,61.44964205,23.85877462,Tampere University of Technology,edu,9be653e1bc15ef487d7f93aad02f3c9552f3ee4a,citation,https://pdfs.semanticscholar.org/9be6/53e1bc15ef487d7f93aad02f3c9552f3ee4a.pdf,Computer Vision for Head Pose Estimation: Review of a Competition,2015
118,LFW,lfw,47.6543238,-122.30800894,University of Washington,edu,96e0cfcd81cdeb8282e29ef9ec9962b125f379b0,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.527,The MegaFace Benchmark: 1 Million Faces for Recognition at Scale,2016
119,LFW,lfw,2.92749755,101.64185301,Multimedia University,edu,3d0c21d4780489bd624a74b07e28c16175df6355,citation,http://pdfs.semanticscholar.org/3d0c/21d4780489bd624a74b07e28c16175df6355.pdf,Deep or Shallow Facial Descriptors? A Case for Facial Attribute Classification and Face Retrieval,2016
120,LFW,lfw,50.7791703,6.06728733,RWTH Aachen University,edu,48906f609446afcdaacbe1d65770d7a6165a8eee,citation,https://doi.org/10.1007/s12559-017-9482-4,Storages Are Not Forever,2017
121,LFW,lfw,37.43131385,-122.16936535,Stanford University,edu,48906f609446afcdaacbe1d65770d7a6165a8eee,citation,https://doi.org/10.1007/s12559-017-9482-4,Storages Are Not Forever,2017
122,LFW,lfw,1.3484104,103.68297965,Nanyang Technological University,edu,48906f609446afcdaacbe1d65770d7a6165a8eee,citation,https://doi.org/10.1007/s12559-017-9482-4,Storages Are Not Forever,2017
123,LFW,lfw,40.00229045,116.32098908,Tsinghua University,edu,31f905d40a4ac3c16c91d5be8427762fa91277f1,citation,https://doi.org/10.1109/TIP.2017.2704661,Learning Rotation-Invariant Local Binary Descriptor,2017
124,LFW,lfw,51.0784038,-114.1287077,University of Calgary,edu,0f64e26d6dd6f1c99fe2050887fac26cafe9ed60,citation,https://doi.org/10.1109/MCI.2016.2627668,Bridging the Gap Between Forensics and Biometric-Enabled Watchlists for e-Borders,2017
125,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,2969f822b118637af29d8a3a0811ede2751897b5,citation,http://iip.ict.ac.cn/sites/default/files/publication/2013_ICCV_xwzhao_Cascaded%20Shape%20Space%20Pruning%20for%20Robust%20Facial%20Landmark%20Detection.pdf,Cascaded Shape Space Pruning for Robust Facial Landmark Detection,2013
126,LFW,lfw,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,4934d44aa89b6d871eb6709dd1d1eebf16f3aaf1,citation,http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Luo_A_Deep_Sum-Product_2013_ICCV_paper.pdf,A Deep Sum-Product Architecture for Robust Facial Attributes Analysis,2013
127,LFW,lfw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,4934d44aa89b6d871eb6709dd1d1eebf16f3aaf1,citation,http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Luo_A_Deep_Sum-Product_2013_ICCV_paper.pdf,A Deep Sum-Product Architecture for Robust Facial Attributes Analysis,2013
128,LFW,lfw,30.19331415,120.11930822,Zhejiang University,edu,134f1cee8408cca648d8b4ca44b38b0a7023af71,citation,https://pdfs.semanticscholar.org/134f/1cee8408cca648d8b4ca44b38b0a7023af71.pdf,Partially Shared MultiTask Convolutional Neural Network with Local Constraint for Face Attribute Learning,0
129,LFW,lfw,43.293621,5.358066,"Aix Marseille University, France",edu,d77f18917a58e7d4598d31af4e7be2762d858370,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6289062,Detecting person presence in TV shows with linguistic and structural features,2012
130,LFW,lfw,48.754168,-3.4584586,"Orange Labs, Lannion, France",company,d77f18917a58e7d4598d31af4e7be2762d858370,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6289062,Detecting person presence in TV shows with linguistic and structural features,2012
131,LFW,lfw,39.9808333,116.34101249,Beihang University,edu,bd8f3fef958ebed5576792078f84c43999b1b207,citation,http://pdfs.semanticscholar.org/bd8f/3fef958ebed5576792078f84c43999b1b207.pdf,BUAA-iCC at ImageCLEF 2015 Scalable Concept Image Annotation Challenge,2015
132,LFW,lfw,-27.49741805,153.01316956,University of Queensland,edu,c51fbd2574e488e486483e39702a3d7754cc769b,citation,https://pdfs.semanticscholar.org/c51f/bd2574e488e486483e39702a3d7754cc769b.pdf,Face Recognition from Still Images to Video Sequences: A Local-Feature-Based Framework,2011
133,LFW,lfw,51.7534538,-1.25400997,University of Oxford,edu,b13e2e43672e66ba45d1b852a34737e4ce04226b,citation,https://pdfs.semanticscholar.org/3552/4e63c11f13fe08b2996a7bc0a9105e7c407b.pdf,Face Painting: querying art with photos,2015
134,LFW,lfw,32.0565957,118.77408833,Nanjing University,edu,65b1760d9b1541241c6c0222cc4ee9df078b593a,citation,http://pdfs.semanticscholar.org/65b1/760d9b1541241c6c0222cc4ee9df078b593a.pdf,Enhanced Pictorial Structures for Precise Eye Localization Under Uncontrolled Conditions,2009
135,LFW,lfw,40.8419836,-73.94368971,Columbia University,edu,518a3ce2a290352afea22027b64bf3950bffc65a,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5204174,Finding iconic images,2009
136,LFW,lfw,51.49887085,-0.17560797,Imperial College London,edu,c87f7ee391d6000aef2eadb49f03fc237f4d1170,citation,https://arxiv.org/pdf/1804.03547.pdf,A real-time and unsupervised face Re-Identification system for Human-Robot Interaction,2017
137,LFW,lfw,41.10427915,29.02231159,Istanbul Technical University,edu,14b87359f6874ff9b8ee234b18b418e57e75b762,citation,http://pdfs.semanticscholar.org/1b62/6c14544f249cd52ef86a4efc17f3d3834003.pdf,Face Alignment Using a Ranking Model based on Regression Trees,2012
138,LFW,lfw,49.10184375,8.4331256,Karlsruhe Institute of Technology,edu,14b87359f6874ff9b8ee234b18b418e57e75b762,citation,http://pdfs.semanticscholar.org/1b62/6c14544f249cd52ef86a4efc17f3d3834003.pdf,Face Alignment Using a Ranking Model based on Regression Trees,2012
139,LFW,lfw,32.0565957,118.77408833,Nanjing University,edu,0e2d956790d3b8ab18cee8df6c949504ee78ad42,citation,https://doi.org/10.1109/IVCNZ.2013.6727024,Scalable face image retrieval integrating multi-feature quantization and constrained reference re-ranking,2013
140,LFW,lfw,47.5612651,7.5752961,University of Basel,edu,323f9ae6bdd2a4e4dce4168f7f7e19c70585c9b5,citation,https://arxiv.org/pdf/1712.01619.pdf,Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systems,2017
141,LFW,lfw,47.6543238,-122.30800894,University of Washington,edu,1ce3a91214c94ed05f15343490981ec7cc810016,citation,http://grail.cs.washington.edu/photobios/paper.pdf,Exploring photobios,2011
142,LFW,lfw,33.776033,-84.39884086,Georgia Institute of Technology,edu,69eb6c91788e7c359ddd3500d01fb73433ce2e65,citation,http://pdfs.semanticscholar.org/69eb/6c91788e7c359ddd3500d01fb73433ce2e65.pdf,CAMGRAPH: Distributed Graph Processing for Camera Networks,2015
143,LFW,lfw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,2296d79753118cfcd0fecefece301557f4cb66e2,citation,https://arxiv.org/pdf/1804.03487.pdf,Exploring Disentangled Feature Representation Beyond Face Identification,2018
144,LFW,lfw,39.993008,116.329882,SenseTime,company,2296d79753118cfcd0fecefece301557f4cb66e2,citation,https://arxiv.org/pdf/1804.03487.pdf,Exploring Disentangled Feature Representation Beyond Face Identification,2018
145,LFW,lfw,34.8452999,48.5596212,Islamic Azad University,edu,53ce84598052308b86ba79d873082853022aa7e9,citation,https://pdfs.semanticscholar.org/4f07/b70883a98a69be3b3e29de06c73e59a9ba0e.pdf,Optimized Method for Real-Time Face Recognition System Based on PCA and Multiclass Support Vector Machine,2013
146,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,a29a22878e1881d6cbf6acff2d0b209c8d3f778b,citation,http://pdfs.semanticscholar.org/a29a/22878e1881d6cbf6acff2d0b209c8d3f778b.pdf,Benchmarking Still-to-Video Face Recognition via Partial and Local Linear Discriminant Analysis on COX-S2V Dataset,2012
147,LFW,lfw,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,a29a22878e1881d6cbf6acff2d0b209c8d3f778b,citation,http://pdfs.semanticscholar.org/a29a/22878e1881d6cbf6acff2d0b209c8d3f778b.pdf,Benchmarking Still-to-Video Face Recognition via Partial and Local Linear Discriminant Analysis on COX-S2V Dataset,2012
148,LFW,lfw,38.88140235,121.52281098,Dalian University of Technology,edu,052f994898c79529955917f3dfc5181586282cf8,citation,https://arxiv.org/pdf/1708.02191.pdf,Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos,2017
149,LFW,lfw,42.3583961,-71.09567788,MIT,edu,a0061dae94d916f60a5a5373088f665a1b54f673,citation,http://pdfs.semanticscholar.org/a006/1dae94d916f60a5a5373088f665a1b54f673.pdf,Lensless computational imaging through deep learning,2017
150,LFW,lfw,51.49887085,-0.17560797,Imperial College London,edu,809ea255d144cff780300440d0f22c96e98abd53,citation,http://pdfs.semanticscholar.org/809e/a255d144cff780300440d0f22c96e98abd53.pdf,ArcFace: Additive Angular Margin Loss for Deep Face Recognition,2018
151,LFW,lfw,40.47913175,-74.43168868,Rutgers University,edu,439ca6ded75dffa5ddea203dde5e621dc4a88c3e,citation,https://doi.org/10.1109/ICPR.2016.7899906,Robust real-time performance-driven 3D face tracking,2016
152,LFW,lfw,1.3484104,103.68297965,Nanyang Technological University,edu,439ca6ded75dffa5ddea203dde5e621dc4a88c3e,citation,https://doi.org/10.1109/ICPR.2016.7899906,Robust real-time performance-driven 3D face tracking,2016
153,LFW,lfw,45.7833244,4.8781984,University of Lyon,edu,9c59bb28054eee783a40b467c82f38021c19ff3e,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7178311,Logistic similarity metric learning for face verification,2015
154,LFW,lfw,40.00229045,116.32098908,Tsinghua University,edu,6043006467fb3fd1e9783928d8040ee1f1db1f3a,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2010.5539992,Face recognition with learning-based descriptor,2010
155,LFW,lfw,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,6043006467fb3fd1e9783928d8040ee1f1db1f3a,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2010.5539992,Face recognition with learning-based descriptor,2010
156,LFW,lfw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,6043006467fb3fd1e9783928d8040ee1f1db1f3a,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2010.5539992,Face recognition with learning-based descriptor,2010
157,LFW,lfw,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,9729930ab0f9cbcd07f1105bc69c540330cda50a,citation,https://doi.org/10.1109/ACCESS.2017.2749331,Compressing Fisher Vector for Robust Face Recognition,2017
158,LFW,lfw,39.65404635,-79.96475355,West Virginia University,edu,3b9b200e76a35178da940279d566bbb7dfebb787,citation,http://pdfs.semanticscholar.org/3b9b/200e76a35178da940279d566bbb7dfebb787.pdf,Learning Channel Inter-dependencies at Multiple Scales on Dense Networks for Face Recognition,2017
159,LFW,lfw,42.3383668,-71.08793524,Northeastern University,edu,feea73095b1be0cbae1ad7af8ba2c4fb6f316d35,citation,http://dl.acm.org/citation.cfm?id=3126693,Deep Face Recognition with Center Invariant Loss,2017
160,LFW,lfw,51.4584837,-2.6097752,University of Bristol,edu,54948ee407b5d32da4b2eee377cc44f20c3a7e0c,citation,https://arxiv.org/pdf/1806.06296.pdf,Right for the Right Reason: Training Agnostic Networks,2018
161,LFW,lfw,32.0565957,118.77408833,Nanjing University,edu,77869f274d4be4d4b4c438dbe7dff4baed521bd8,citation,https://doi.org/10.1109/TIP.2016.2551362,Face Recognition With Pose Variations and Misalignment via Orthogonal Procrustes Regression,2016
162,LFW,lfw,32.1119889,34.80459702,Tel Aviv University,edu,0faee699eccb2da6cf4307ded67ba8434368257b,citation,http://pdfs.semanticscholar.org/2396/5bd9b557b04b2c81a35ee5c16951c0e420f3.pdf,TAIGMAN: MULTIPLE ONE-SHOTS FOR UTILIZING CLASS LABEL INFORMATION 1 Multiple One-Shots for Utilizing Class Label Information,2009
163,LFW,lfw,32.77824165,34.99565673,Open University of Israel,edu,0faee699eccb2da6cf4307ded67ba8434368257b,citation,http://pdfs.semanticscholar.org/2396/5bd9b557b04b2c81a35ee5c16951c0e420f3.pdf,TAIGMAN: MULTIPLE ONE-SHOTS FOR UTILIZING CLASS LABEL INFORMATION 1 Multiple One-Shots for Utilizing Class Label Information,2009
164,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,1eb9c859ff7537182a25556635954bcd11830822,citation,https://doi.org/10.1109/ICDSP.2015.7252004,Multi-features fusion based CRFs for face segmentation,2015
165,LFW,lfw,31.32235655,121.38400941,Shanghai University,edu,1eb9c859ff7537182a25556635954bcd11830822,citation,https://doi.org/10.1109/ICDSP.2015.7252004,Multi-features fusion based CRFs for face segmentation,2015
166,LFW,lfw,51.4584837,-2.6097752,University of Bristol,edu,3cd9b0a61bdfa1bb8a0a1bf0369515a76ecd06e3,citation,http://pdfs.semanticscholar.org/51f7/3cfcc6d671bd99b5c3c512ff9b7bb959f33b.pdf,Distance Metric Learning with Eigenvalue Optimization,2012
167,LFW,lfw,50.7369302,-3.53647672,University of Exeter,edu,3cd9b0a61bdfa1bb8a0a1bf0369515a76ecd06e3,citation,http://pdfs.semanticscholar.org/51f7/3cfcc6d671bd99b5c3c512ff9b7bb959f33b.pdf,Distance Metric Learning with Eigenvalue Optimization,2012
168,LFW,lfw,51.7534538,-1.25400997,University of Oxford,edu,f5aee1529b98136194ef80961ba1a6de646645fe,citation,http://pdfs.semanticscholar.org/f5ae/e1529b98136194ef80961ba1a6de646645fe.pdf,Large-scale learning of discriminative image representations,2013
169,LFW,lfw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,abdd17e411a7bfe043f280abd4e560a04ab6e992,citation,https://arxiv.org/pdf/1803.00839.pdf,Pose-Robust Face Recognition via Deep Residual Equivariant Mapping,2018
170,LFW,lfw,39.94976005,116.33629046,Beijing Jiaotong University,edu,d40c16285d762f7a1c862b8ac05a0fdb24af1202,citation,https://doi.org/10.1109/BESC.2017.8256378,Coarse-to-fine facial landmarks localization based on convolutional feature,2017
171,LFW,lfw,38.2167565,-85.75725023,University of Louisville,edu,780c8a795baca1ba4cb4956cded877dd3d1ca313,citation,http://doi.ieeecomputersociety.org/10.1109/ISSPIT.2013.6781879,Simulation of face recognition at a distance by scaling down images,2013
172,LFW,lfw,44.8055716,-0.6051972,"Bordeaux INP, France",edu,c222f8079c246ead285894c47bdbb2dfc7741044,citation,https://doi.org/10.1109/ICIP.2015.7351631,Face de-identification with expressions preservation,2015
173,LFW,lfw,44.808375,-0.596705,"University of Bordeaux, France",edu,c222f8079c246ead285894c47bdbb2dfc7741044,citation,https://doi.org/10.1109/ICIP.2015.7351631,Face de-identification with expressions preservation,2015
174,LFW,lfw,1.2962018,103.77689944,National University of Singapore,edu,2f2aa67c5d6dbfaf218c104184a8c807e8b29286,citation,http://sesame.comp.nus.edu.sg/components/com_flexicontent/uploads/lekhaicon13.pdf,Video analytics for surveillance camera networks,2013
175,LFW,lfw,45.7413921,126.62552755,Harbin Institute of Technology,edu,982d4f1dee188f662a4b5616a045d69fc5c21b54,citation,https://doi.org/10.1109/IJCNN.2016.7727859,Learning to link human objects in videos and advertisements with clothes retrieval,2016
176,LFW,lfw,42.3383668,-71.08793524,Northeastern University,edu,982d4f1dee188f662a4b5616a045d69fc5c21b54,citation,https://doi.org/10.1109/IJCNN.2016.7727859,Learning to link human objects in videos and advertisements with clothes retrieval,2016
177,LFW,lfw,16.46007565,102.81211798,Khon Kaen University,edu,81a80b26979b40d5ebe3f5ba70b03cb9f19dd7a5,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8369725,Histogram equalized deep PCA with ELM classification for expressive face recognition,2018
178,LFW,lfw,37.2520226,127.0555019,"Samsung SAIT, Korea",company,86fa086d02f424705bbea53943390f009191740a,citation,https://doi.org/10.1109/ICIP.2015.7351651,Precise eye localization with improved SDM,2015
179,LFW,lfw,39.9041999,116.4073963,"Samsung SAIT, Beijing",company,86fa086d02f424705bbea53943390f009191740a,citation,https://doi.org/10.1109/ICIP.2015.7351651,Precise eye localization with improved SDM,2015
180,LFW,lfw,39.14004,-77.218506,NIST,edu,a35ed55dc330d470be2f610f4822f5152fcac4e1,citation,https://doi.org/10.1109/ISBA.2015.7126369,Tattoo recognition technology - challenge (Tatt-C): an open tattoo database for developing tattoo recognition research,2015
181,LFW,lfw,36.3697191,127.362537,Korea Advanced Institute of Science and Technology,edu,3ba74755c530347f14ec8261996dd9eae896e383,citation,https://doi.org/10.1109/JSSC.2017.2767705,A Low-Power Convolutional Neural Network Face Recognition Processor and a CIS Integrated With Always-on Face Detector,2018
182,LFW,lfw,32.77824165,34.99565673,Open University of Israel,edu,7fc3442c8b4c96300ad3e860ee0310edb086de94,citation,http://pdfs.semanticscholar.org/82f3/b7cacc15e026fd3a7639091d54162f6ae064.pdf,Similarity Scores Based on Background Samples,2009
183,LFW,lfw,32.1119889,34.80459702,Tel Aviv University,edu,7fc3442c8b4c96300ad3e860ee0310edb086de94,citation,http://pdfs.semanticscholar.org/82f3/b7cacc15e026fd3a7639091d54162f6ae064.pdf,Similarity Scores Based on Background Samples,2009
184,LFW,lfw,37.43131385,-122.16936535,Stanford University,edu,42f6f5454dda99d8989f9814989efd50fe807ee8,citation,http://pdfs.semanticscholar.org/42f6/f5454dda99d8989f9814989efd50fe807ee8.pdf,Conditional generative adversarial nets for convolutional face generation,2015
185,LFW,lfw,47.6423318,-122.1369302,Microsoft,company,0aebe97a92f590bdf21cdadfddec8061c682cdb2,citation,http://doi.ieeecomputersociety.org/10.1109/TPAMI.2017.2695183,Probabilistic Elastic Part Model: A Pose-Invariant Representation for Real-World Face Verification,2018
186,LFW,lfw,22.2081469,114.25964115,University of Hong Kong,edu,7ffef9f26c39377ee937d29b8990580266a7a8a5,citation,https://arxiv.org/pdf/1810.06951.pdf,Deep Metric Learning with Hierarchical Triplet Loss,2018
187,LFW,lfw,47.6543238,-122.30800894,University of Washington,edu,7862f646d640cbf9f88e5ba94a7d642e2a552ec9,citation,http://pdfs.semanticscholar.org/7862/f646d640cbf9f88e5ba94a7d642e2a552ec9.pdf,Being John Malkovich,2010
188,LFW,lfw,41.10427915,29.02231159,Istanbul Technical University,edu,754626bd5fb06fee5e10962fdfeddd495513e84b,citation,https://doi.org/10.1109/SIU.2017.7960646,Facial expression pair matching,2017
189,LFW,lfw,42.3383668,-71.08793524,Northeastern University,edu,e3c8e49ffa7beceffca3f7f276c27ae6d29b35db,citation,https://arxiv.org/pdf/1604.02182.pdf,Families in the Wild (FIW): Large-Scale Kinship Image Database and Benchmarks,2016
190,LFW,lfw,55.94951105,-3.19534913,University of Edinburgh,edu,b306bd9b485c6a6c1e4550beb1910ed9b6585359,citation,https://pdfs.semanticscholar.org/b306/bd9b485c6a6c1e4550beb1910ed9b6585359.pdf,Learning generative models of mid-level structure in natural images,2012
191,LFW,lfw,40.00229045,116.32098908,Tsinghua University,edu,aed6af12148b43e4a24ee6e2bc3604ca59bd99a5,citation,https://doi.org/10.1109/TIP.2017.2717505,Discriminative Deep Metric Learning for Face and Kinship Verification,2017
192,LFW,lfw,1.3484104,103.68297965,Nanyang Technological University,edu,aed6af12148b43e4a24ee6e2bc3604ca59bd99a5,citation,https://doi.org/10.1109/TIP.2017.2717505,Discriminative Deep Metric Learning for Face and Kinship Verification,2017
193,LFW,lfw,43.7743911,-79.50481085,York University,edu,ffe4bb47ec15f768e1744bdf530d5796ba56cfc1,citation,https://arxiv.org/pdf/1706.04277.pdf,AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces,2017
194,LFW,lfw,27.18794105,31.17009498,Assiut University,edu,ffe4bb47ec15f768e1744bdf530d5796ba56cfc1,citation,https://arxiv.org/pdf/1706.04277.pdf,AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces,2017
195,LFW,lfw,51.5231607,-0.1282037,University College London,edu,467b602a67cfd7c347fe7ce74c02b38c4bb1f332,citation,http://pdfs.semanticscholar.org/467b/602a67cfd7c347fe7ce74c02b38c4bb1f332.pdf,Large Margin Local Metric Learning,2014
196,LFW,lfw,32.0565957,118.77408833,Nanjing University,edu,9887ab220254859ffc7354d5189083a87c9bca6e,citation,http://pdfs.semanticscholar.org/9887/ab220254859ffc7354d5189083a87c9bca6e.pdf,Generic Image Classification Approaches Excel on Face Recognition,2013
197,LFW,lfw,-34.9189226,138.60423668,University of Adelaide,edu,9887ab220254859ffc7354d5189083a87c9bca6e,citation,http://pdfs.semanticscholar.org/9887/ab220254859ffc7354d5189083a87c9bca6e.pdf,Generic Image Classification Approaches Excel on Face Recognition,2013
198,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,0034e37a0faf0f71395245b266aacbf5412f190a,citation,https://doi.org/10.1109/TMM.2014.2355134,Face Distortion Recovery Based on Online Learning Database for Conversational Video,2014
199,LFW,lfw,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,0034e37a0faf0f71395245b266aacbf5412f190a,citation,https://doi.org/10.1109/TMM.2014.2355134,Face Distortion Recovery Based on Online Learning Database for Conversational Video,2014
200,LFW,lfw,31.28473925,121.49694909,Tongji University,edu,fe0cf8eaa5a5f59225197ef1bb8613e603cd96d4,citation,https://pdfs.semanticscholar.org/4e20/8cfff33327863b5aeef0bf9b327798a5610c.pdf,Improved Face Verification with Simple Weighted Feature Combination,2017
201,LFW,lfw,39.65404635,-79.96475355,West Virginia University,edu,e20e2db743e8db1ff61279f4fda32bf8cf381f8e,citation,https://arxiv.org/pdf/1801.01486.pdf,Deep Cross Polarimetric Thermal-to-Visible Face Recognition,2018
202,LFW,lfw,32.198055,119.46326791,Jiangsu University of Science and Technology,edu,9b4d2cd2e5edbf5c8efddbdcce1db9a02a853534,citation,https://doi.org/10.1016/j.neucom.2016.02.063,Exponential Discriminant Locality Preserving Projection for face recognition,2016
203,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,7783095a565094ae5b3dccf082d504ddd7255a5c,citation,http://dl.acm.org/citation.cfm?id=2502258,"""Wow! you are so beautiful today!""",2013
204,LFW,lfw,1.2962018,103.77689944,National University of Singapore,edu,7783095a565094ae5b3dccf082d504ddd7255a5c,citation,http://dl.acm.org/citation.cfm?id=2502258,"""Wow! you are so beautiful today!""",2013
205,LFW,lfw,22.3386304,114.2620337,Hong Kong University of Science and Technology,edu,fff31548617f208cd5ae5c32917afd48abc4ff6a,citation,http://doi.acm.org/10.1145/3139295.3139309,Mobile situated analytics of ego-centric network data,2017
206,LFW,lfw,37.21872455,-80.42542519,Virginia Polytechnic Institute and State University,edu,0de1450369cb57e77ef61cd334c3192226e2b4c2,citation,https://doi.org/10.1109/BTAS.2017.8272747,"In defense of low-level structural features and SVMs for facial attribute classification: Application to detection of eye state, Mouth State, and eyeglasses in the wild",2017
207,LFW,lfw,34.8452999,48.5596212,Islamic Azad University,edu,15cf7bdc36ec901596c56d04c934596cf7b43115,citation,http://pdfs.semanticscholar.org/15cf/7bdc36ec901596c56d04c934596cf7b43115.pdf,Face Extraction from Image based on K-Means Clustering Algorithms,2017
208,LFW,lfw,29.5084174,106.57858552,Chongqing University,edu,3a49507c46a2b8c6411809c81ac47b2b1d2282c3,citation,http://doi.org/10.1007/s11042-017-5319-0,Exploring joint encoding of multi-direction local binary patterns for image classification,2017
209,LFW,lfw,45.504384,-73.6128829,Polytechnique Montreal,edu,b81cae2927598253da37954fb36a2549c5405cdb,citation,http://pdfs.semanticscholar.org/d892/753827950a227179b691e6df85820ab7c417.pdf,Experiments on Visual Information Extraction with the Faces of Wikipedia,2014
210,LFW,lfw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,58d76380d194248b3bb291b8c7c5137a0a376897,citation,https://pdfs.semanticscholar.org/58d7/6380d194248b3bb291b8c7c5137a0a376897.pdf,FaceID-GAN : Learning a Symmetry Three-Player GAN for Identity-Preserving Face Synthesis,2018
211,LFW,lfw,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,58d76380d194248b3bb291b8c7c5137a0a376897,citation,https://pdfs.semanticscholar.org/58d7/6380d194248b3bb291b8c7c5137a0a376897.pdf,FaceID-GAN : Learning a Symmetry Three-Player GAN for Identity-Preserving Face Synthesis,2018
212,LFW,lfw,39.9808333,116.34101249,Beihang University,edu,5dd57b7e0e82a33420c054da7ea3f435d49e910e,citation,https://doi.org/10.1007/s10851-014-0493-4,Matching and Perturbation Theories for Affine-Invariant Shapes Using QR Factorization with Column Pivoting,2014
213,LFW,lfw,30.527151,114.400762,China University of Geosciences,edu,110919f803740912e02bb7e1424373d325f558a9,citation,http://doi.acm.org/10.1145/3123266.3123421,Statistical Inference of Gaussian-Laplace Distribution for Person Verification,2017
214,LFW,lfw,35.6924853,139.7582533,"National Institute of Informatics, Japan",edu,110919f803740912e02bb7e1424373d325f558a9,citation,http://doi.acm.org/10.1145/3123266.3123421,Statistical Inference of Gaussian-Laplace Distribution for Person Verification,2017
215,LFW,lfw,30.60903415,114.3514284,Wuhan University of Technology,edu,110919f803740912e02bb7e1424373d325f558a9,citation,http://doi.acm.org/10.1145/3123266.3123421,Statistical Inference of Gaussian-Laplace Distribution for Person Verification,2017
216,LFW,lfw,42.4505507,-76.4783513,Cornell University,edu,537328af75f50d49696972a6c34bca97c14bc762,citation,https://arxiv.org/pdf/1805.04049.pdf,Exploiting Unintended Feature Leakage in Collaborative Learning,2018
217,LFW,lfw,43.47061295,-80.54724732,University of Waterloo,edu,9825c4dddeb2ed7eaab668b55403aa2c38bc3320,citation,https://arxiv.org/pdf/1807.09532.pdf,Aerial Imagery for Roof Segmentation: A Large-Scale Dataset towards Automatic Mapping of Buildings,2018
218,LFW,lfw,35.9020448,139.93622009,University of Tokyo,edu,9825c4dddeb2ed7eaab668b55403aa2c38bc3320,citation,https://arxiv.org/pdf/1807.09532.pdf,Aerial Imagery for Roof Segmentation: A Large-Scale Dataset towards Automatic Mapping of Buildings,2018
219,LFW,lfw,39.65404635,-79.96475355,West Virginia University,edu,7a65fc9e78eff3ab6062707deaadde024d2fad40,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/papers/Zhu_A_Study_on_ICCV_2015_paper.pdf,A Study on Apparent Age Estimation,2015
220,LFW,lfw,29.5357046,106.60482474,Chongqing University of Posts and Telecommunications,edu,0750c796467b6ef60b0caff5fb199337d54d431e,citation,https://doi.org/10.1109/ICMLC.2016.7873015,Face detection method based on histogram of sparse code in tree deformable model,2016
221,LFW,lfw,34.2375581,-77.9270129,University of North Carolina Wilmington,edu,0750c796467b6ef60b0caff5fb199337d54d431e,citation,https://doi.org/10.1109/ICMLC.2016.7873015,Face detection method based on histogram of sparse code in tree deformable model,2016
222,LFW,lfw,48.8225067,2.2687541,"Morpho, SAFRAN Group, France",company,e66a6ae542907d6a0ebc45da60a62d3eecf17839,citation,https://doi.org/10.1109/EUVIP.2014.7018366,3D-aided face recognition from videos,2014
223,LFW,lfw,45.7833244,4.8781984,University of Lyon,edu,e66a6ae542907d6a0ebc45da60a62d3eecf17839,citation,https://doi.org/10.1109/EUVIP.2014.7018366,3D-aided face recognition from videos,2014
224,LFW,lfw,28.2290209,112.99483204,"National University of Defense Technology, China",edu,ae425a2654a1064c2eda29b08a492c8d5aab27a2,citation,https://doi.org/10.23919/MVA.2017.7986845,An incremental face recognition system based on deep learning,2017
225,LFW,lfw,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,39c10888a470b92b917788c57a6fd154c97b421c,citation,https://doi.org/10.1109/VCIP.2017.8305036,Joint multi-feature fusion and attribute relationships for facial attribute prediction,2017
226,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,d04d5692461d208dd5f079b98082eda887b62323,citation,http://www.cbsr.ia.ac.cn/users/zlei/papers/ICB2015/ZLEI-ICB-15.pdf,Subspace learning with frequency regularizer: Its application to face recognition,2015
227,LFW,lfw,40.00229045,116.32098908,Tsinghua University,edu,e908ce44fa94bb7ecf2a8b70cb5ec0b1a00b311a,citation,http://doi.ieeecomputersociety.org/10.1109/ICME.2017.8019548,Topology preserving graph matching for partial face recognition,2017
228,LFW,lfw,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,84574aa43a98ad8a29470977e7b091f5a5ec2366,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7301321,Latent max-margin metric learning for comparing video face tubes,2015
229,LFW,lfw,48.831533,2.28066283,"Technicolor, France",edu,84574aa43a98ad8a29470977e7b091f5a5ec2366,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7301321,Latent max-margin metric learning for comparing video face tubes,2015
230,LFW,lfw,51.24303255,-0.59001382,University of Surrey,edu,9103148dd87e6ff9fba28509f3b265e1873166c9,citation,http://pdfs.semanticscholar.org/9103/148dd87e6ff9fba28509f3b265e1873166c9.pdf,Face Analysis using 3D Morphable Models,2015
231,LFW,lfw,32.77824165,34.99565673,Open University of Israel,edu,32c20afb5c91ed7cdbafb76408c3a62b38dd9160,citation,http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Hassner_Viewing_Real-World_Faces_2013_ICCV_paper.pdf,Viewing Real-World Faces in 3D,2013
232,LFW,lfw,40.00229045,116.32098908,Tsinghua University,edu,f5eb0cf9c57716618fab8e24e841f9536057a28a,citation,https://arxiv.org/pdf/1803.02988.pdf,Rethinking Feature Distribution for Loss Functions in Image Classification,2018
233,LFW,lfw,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,b8bcf9c773da1c5ee76db4bf750c9ff5d159f1a0,citation,http://doi.acm.org/10.1145/2911996.2911999,Homemade TS-Net for Automatic Face Recognition,2016
234,LFW,lfw,39.2899685,-76.62196103,University of Maryland,edu,24e82eaf3257e761d6ca0ffcc2cbca30dfca82e9,citation,https://doi.org/10.1109/GlobalSIP.2016.7906030,An analysis of the robustness of deep face recognition networks to noisy training labels,2016
235,LFW,lfw,39.65404635,-79.96475355,West Virginia University,edu,24e82eaf3257e761d6ca0ffcc2cbca30dfca82e9,citation,https://doi.org/10.1109/GlobalSIP.2016.7906030,An analysis of the robustness of deep face recognition networks to noisy training labels,2016
236,LFW,lfw,24.6004712,118.0816574,Huaqiao University,edu,4b9b30066a05bdeb0e05025402668499ebf99a6b,citation,https://doi.org/10.1109/ISPACS.2012.6473448,Real-time face detection using Gentle AdaBoost algorithm and nesting cascade structure,2012
237,LFW,lfw,43.6158131,7.06838,INRIA Méditerranée,edu,23e824d1dfc33f3780dd18076284f07bd99f1c43,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7947686,Spoofing faces using makeup: An investigative study,2017
238,LFW,lfw,42.718568,-84.47791571,Michigan State University,edu,23e824d1dfc33f3780dd18076284f07bd99f1c43,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7947686,Spoofing faces using makeup: An investigative study,2017
239,LFW,lfw,32.7283683,-97.11201835,University of Texas at Arlington,edu,612075999e82596f3b42a80e6996712cc52880a3,citation,http://doi.ieeecomputersociety.org/10.1109/AVSS.2017.8078554,CNNs with cross-correlation matching for face recognition in video surveillance using a single training sample per person,2017
240,LFW,lfw,55.70229715,37.53179777,Lomonosov Moscow State University,edu,6bfb0f8dd1a2c0b44347f09006dc991b8a08559c,citation,https://www.computer.org/web/csdl/index/-/csdl/proceedings/fg/2013/5545/00/06553724.pdf,Multiview discriminative learning for age-invariant face recognition,2013
241,LFW,lfw,1.3037257,103.7737763,"Advanced Digital Sciences Center, Singapore",edu,6bfb0f8dd1a2c0b44347f09006dc991b8a08559c,citation,https://www.computer.org/web/csdl/index/-/csdl/proceedings/fg/2013/5545/00/06553724.pdf,Multiview discriminative learning for age-invariant face recognition,2013
242,LFW,lfw,1.3484104,103.68297965,Nanyang Technological University,edu,6bfb0f8dd1a2c0b44347f09006dc991b8a08559c,citation,https://www.computer.org/web/csdl/index/-/csdl/proceedings/fg/2013/5545/00/06553724.pdf,Multiview discriminative learning for age-invariant face recognition,2013
243,LFW,lfw,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,6bfb0f8dd1a2c0b44347f09006dc991b8a08559c,citation,https://www.computer.org/web/csdl/index/-/csdl/proceedings/fg/2013/5545/00/06553724.pdf,Multiview discriminative learning for age-invariant face recognition,2013
244,LFW,lfw,-35.2776999,149.118527,Australian National University,edu,c58b7466f2855ffdcff1bebfad6b6a027b8c5ee1,citation,http://pdfs.semanticscholar.org/d6f1/42f5ddcb027e7b346eb20703abbf5cc4e883.pdf,Ultra-Resolving Face Images by Discriminative Generative Networks,2016
245,LFW,lfw,52.2380139,6.8566761,University of Twente,edu,d8288322f32ee4501cef5a9b667e5bb79ebd7018,citation,https://doi.org/10.1016/j.patcog.2011.12.018,Facing scalability: Naming faces in an online social network,2012
246,LFW,lfw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,de7f5e4ccc2f38e0c8f3f72a930ae1c43e0fdcf0,citation,https://arxiv.org/pdf/1707.03986.pdf,Merge or Not? Learning to Group Faces via Imitation Learning,2018
247,LFW,lfw,41.21002475,-73.80407056,IBM Thomas J. Watson Research Center,company,eb87151fd2796ff5b4bbcf1906d41d53ac6c5595,citation,https://doi.org/10.1109/ICPR.2016.7899719,Enhanced face detection using body part detections for wearable cameras,2016
248,LFW,lfw,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,edb5813a32ce1167feb263ca2803d0ae934d902c,citation,https://arxiv.org/pdf/1807.08571.pdf,Invisible Steganography via Generative Adversarial Networks,2018
249,LFW,lfw,38.99203005,-76.9461029,University of Maryland College Park,edu,7ca7255c2e0c86e4adddbbff2ce74f36b1dc522d,citation,https://pdfs.semanticscholar.org/7ca7/255c2e0c86e4adddbbff2ce74f36b1dc522d.pdf,Stereo Matching for Unconstrained Face Recognition Ph . D . Proposal,2009
250,LFW,lfw,39.2899685,-76.62196103,University of Maryland,edu,7ca7255c2e0c86e4adddbbff2ce74f36b1dc522d,citation,https://pdfs.semanticscholar.org/7ca7/255c2e0c86e4adddbbff2ce74f36b1dc522d.pdf,Stereo Matching for Unconstrained Face Recognition Ph . D . Proposal,2009
251,LFW,lfw,37.8687126,-122.25586815,"University of California, Berkeley",edu,53bfe2ab770e74d064303f3bd2867e5bf7b86379,citation,https://pdfs.semanticscholar.org/d989/c3064d49bf8e63587ada4ed2bdb0d32b120a.pdf,Learning to Synthesize and Manipulate Natural Images,2017
252,LFW,lfw,40.4319722,-86.92389368,Purdue University,edu,1a46d3a9bc1e4aff0ccac6403b49a13c8a89fc1d,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2012.6247878,Online robust image alignment via iterative convex optimization,2012
253,LFW,lfw,32.0565957,118.77408833,Nanjing University,edu,1a46d3a9bc1e4aff0ccac6403b49a13c8a89fc1d,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2012.6247878,Online robust image alignment via iterative convex optimization,2012
254,LFW,lfw,39.95472495,-75.15346905,Temple University,edu,1a46d3a9bc1e4aff0ccac6403b49a13c8a89fc1d,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2012.6247878,Online robust image alignment via iterative convex optimization,2012
255,LFW,lfw,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,3dfb822e16328e0f98a47209d7ecd242e4211f82,citation,https://arxiv.org/pdf/1708.08197.pdf,Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments,2017
256,LFW,lfw,61.44964205,23.85877462,Tampere University of Technology,edu,27dafedccd7b049e87efed72cabaa32ec00fdd45,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/2A_074.pdf,Unsupervised visual alignment with similarity graphs,2015
257,LFW,lfw,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,e9c008d31da38d9eef67a28d2c77cb7daec941fb,citation,https://arxiv.org/pdf/1708.03769.pdf,Noisy Softmax: Improving the Generalization Ability of DCNN via Postponing the Early Softmax Saturation,2017
258,LFW,lfw,23.09461185,113.28788994,Sun Yat-Sen University,edu,39f525f3a0475e6bbfbe781ae3a74aca5b401125,citation,http://pdfs.semanticscholar.org/39f5/25f3a0475e6bbfbe781ae3a74aca5b401125.pdf,Deep Joint Face Hallucination and Recognition,2016
259,LFW,lfw,40.00229045,116.32098908,Tsinghua University,edu,88cd4209db62a34d9cba0b9cbe9d45d1e57d21e5,citation,https://pdfs.semanticscholar.org/88cd/4209db62a34d9cba0b9cbe9d45d1e57d21e5.pdf,Runtime Neural Pruning,2017
260,LFW,lfw,-34.9181706,-56.1665725,"Universidad de la República, Uruguay",edu,3b75681f0162752865d85befd8b15e7d954ebfe6,citation,https://doi.org/10.1109/CLEI.2014.6965097,Evaluation of a face recognition system performance's variation on a citizen passports database,2014
261,LFW,lfw,45.5039761,-73.5749687,McGill University,edu,ed9d11e995baeec17c5d2847ec1a8d5449254525,citation,https://pdfs.semanticscholar.org/ed9d/11e995baeec17c5d2847ec1a8d5449254525.pdf,Efficient Gender Classification Using a Deep LDA-Pruned Net,2017
262,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,21d1315761131ea6b3e2afe7a745b606341616fd,citation,https://pdfs.semanticscholar.org/21d1/315761131ea6b3e2afe7a745b606341616fd.pdf,Generative Adversarial Network with Spatial Attention for Face Attribute Editing,2018
263,LFW,lfw,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,21d1315761131ea6b3e2afe7a745b606341616fd,citation,https://pdfs.semanticscholar.org/21d1/315761131ea6b3e2afe7a745b606341616fd.pdf,Generative Adversarial Network with Spatial Attention for Face Attribute Editing,2018
264,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,13a994d489c15d440c1238fc1ac37dad06dd928c,citation,http://pdfs.semanticscholar.org/13a9/94d489c15d440c1238fc1ac37dad06dd928c.pdf,Learning Discriminant Face Descriptor for Face Recognition,2012
265,LFW,lfw,33.776033,-84.39884086,Georgia Institute of Technology,edu,96f0e7416994035c91f4e0dfa40fd45090debfc5,citation,https://arxiv.org/pdf/1803.01260.pdf,Unsupervised Learning of Face Representations,2018
266,LFW,lfw,42.718568,-84.47791571,Michigan State University,edu,0141cb33c822e87e93b0c1bad0a09db49b3ad470,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7298876,Unconstrained 3D face reconstruction,2015
267,LFW,lfw,37.52914535,45.04886077,Urmia University,edu,d2f2b10a8f29165d815e652f8d44955a12d057e6,citation,http://doi.org/10.1007/s10044-015-0475-1,Multiscale binarised statistical image features for symmetric face matching using multiple descriptor fusion based on class-specific LDA,2015
268,LFW,lfw,32.77824165,34.99565673,Open University of Israel,edu,582edc19f2b1ab2ac6883426f147196c8306685a,citation,http://pdfs.semanticscholar.org/be6c/db7b181e73f546d43cf2ab6bc7181d7d619b.pdf,Do We Really Need to Collect Millions of Faces for Effective Face Recognition?,2016
269,LFW,lfw,30.19331415,120.11930822,Zhejiang University,edu,2d3c17ced03e4b6c4b014490fe3d40c62d02e914,citation,http://pdfs.semanticscholar.org/2d3c/17ced03e4b6c4b014490fe3d40c62d02e914.pdf,Video-driven state-aware facial animation,2012
270,LFW,lfw,42.718568,-84.47791571,Michigan State University,edu,cd55fb30737625e86454a2861302b96833ed549d,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7139094,Annotating Unconstrained Face Imagery: A scalable approach,2015
271,LFW,lfw,38.95187,-77.363259,"Noblis, Falls Church, VA, U.S.A.",company,cd55fb30737625e86454a2861302b96833ed549d,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7139094,Annotating Unconstrained Face Imagery: A scalable approach,2015
272,LFW,lfw,31.28473925,121.49694909,Tongji University,edu,78f08685d44b6c6f82983d9b0f9c6ac2f7203a5e,citation,https://pdfs.semanticscholar.org/78f0/8685d44b6c6f82983d9b0f9c6ac2f7203a5e.pdf,An Adaptive Ensemble Approach to Ambient Intelligence Assisted People Search,2018
273,LFW,lfw,39.9041999,116.4073963,Chinese Academy of Science,edu,c4d0d09115a0df856cdb389fbccb20f62b07b14e,citation,https://doi.org/10.1109/ICIP.2012.6466925,Environment coupled metrics learning for unconstrained face verification,2012
274,LFW,lfw,39.8720489,32.75395155,Bilkent University,edu,955e2a39f51c0b6f967199942d77625009e580f9,citation,http://pdfs.semanticscholar.org/955e/2a39f51c0b6f967199942d77625009e580f9.pdf,Naming Faces on the Web,2010
275,LFW,lfw,45.7413921,126.62552755,Harbin Institute of Technology,edu,fdd19fee07f2404952e629cc7f7ffaac14febe01,citation,https://doi.org/10.1109/CISP-BMEI.2016.7852754,Face recognition based on dictionary learning with the locality constraints of atoms,2016
276,LFW,lfw,23.131707,113.371643,Guangdong Polytechnic Normal University,edu,fdd19fee07f2404952e629cc7f7ffaac14febe01,citation,https://doi.org/10.1109/CISP-BMEI.2016.7852754,Face recognition based on dictionary learning with the locality constraints of atoms,2016
277,LFW,lfw,42.3889785,-72.5286987,University of Massachusetts,edu,e39a66a6d1c5e753f8e6c33cd5d335f9bc9c07fa,citation,https://pdfs.semanticscholar.org/e39a/66a6d1c5e753f8e6c33cd5d335f9bc9c07fa.pdf,Weakly Supervised Learning for Unconstrained Face Processing,2014
278,LFW,lfw,42.718568,-84.47791571,Michigan State University,edu,051f03bc25ec633592aa2ff5db1d416b705eac6c,citation,http://www.cse.msu.edu/biometrics/Publications/Face/LiaoJain_PartialFR_AlignmentFreeApproach_ICJB11.pdf,Partial face recognition: An alignment free approach,2011
279,LFW,lfw,31.846918,117.29053367,Hefei University of Technology,edu,f6e6b4d0b7c16112dcb71ff502033a2187b1ec9b,citation,https://doi.org/10.1109/TMM.2015.2476657,Understanding Blooming Human Groups in Social Networks,2015
280,LFW,lfw,29.58333105,-98.61944505,University of Texas at San Antonio,edu,f6e6b4d0b7c16112dcb71ff502033a2187b1ec9b,citation,https://doi.org/10.1109/TMM.2015.2476657,Understanding Blooming Human Groups in Social Networks,2015
281,LFW,lfw,1.2962018,103.77689944,National University of Singapore,edu,f6e6b4d0b7c16112dcb71ff502033a2187b1ec9b,citation,https://doi.org/10.1109/TMM.2015.2476657,Understanding Blooming Human Groups in Social Networks,2015
282,LFW,lfw,39.9808333,116.34101249,Beihang University,edu,a961f1234e963a7945fed70197015678149b37d8,citation,http://dl.acm.org/citation.cfm?id=3206068,Facial Expression Synthesis by U-Net Conditional Generative Adversarial Networks,2018
283,LFW,lfw,34.2152538,117.1398541,China University of Mining and Technology,edu,660c99ac408b535bb0468ab3708d0d1d5db30180,citation,http://doi.org/10.1007/s11042-015-3083-6,An improved redundant dictionary based on sparse representation for face recognition,2015
284,LFW,lfw,1.2988926,103.7873107,"A*STAR, Singapore",edu,c444c4dab97dd6d6696f56c1cacda051dde60448,citation,http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.37,Multiview Face Detection and Registration Requiring Minimal Manual Intervention,2013
285,LFW,lfw,1.3484104,103.68297965,Nanyang Technological University,edu,c444c4dab97dd6d6696f56c1cacda051dde60448,citation,http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.37,Multiview Face Detection and Registration Requiring Minimal Manual Intervention,2013
286,LFW,lfw,28.3656193,75.5834953,"Central Electronics Research Institute, Pilani, India",edu,1aeef2ab062c27e0dbba481047e818d4c471ca57,citation,https://doi.org/10.1109/ICACCI.2015.7275860,Analyzing impact of image scaling algorithms on viola-jones face detection framework,2015
287,LFW,lfw,34.0224149,-118.28634407,University of Southern California,edu,d6ae7941dcec920d5726d50d1b1cdfe4dde34d35,citation,http://dl.acm.org/citation.cfm?id=31310887,Avatar digitization from a single image for real-time rendering,2017
288,LFW,lfw,28.54632595,77.27325504,Indian Institute of Technology Delhi,edu,fba95853ca3135cc52a4b2bc67089041c2a9408c,citation,https://pdfs.semanticscholar.org/fba9/5853ca3135cc52a4b2bc67089041c2a9408c.pdf,Disguised Faces in the Wild,2018
289,LFW,lfw,39.2899685,-76.62196103,University of Maryland,edu,fba95853ca3135cc52a4b2bc67089041c2a9408c,citation,https://pdfs.semanticscholar.org/fba9/5853ca3135cc52a4b2bc67089041c2a9408c.pdf,Disguised Faces in the Wild,2018
290,LFW,lfw,44.808375,-0.596705,University of Bordeaux,edu,4512b87d68458d9ba0956c0f74b60371b6c69df4,citation,https://doi.org/10.1109/TIP.2017.2708504,SuperPatchMatch: An Algorithm for Robust Correspondences Using Superpixel Patches,2017
291,LFW,lfw,38.99203005,-76.9461029,University of Maryland College Park,edu,38a9ca2c49a77b540be52377784b9f734e0417e4,citation,http://homepages.dcc.ufmg.br/~william/papers/paper_2011_IJCB_Faces.pdf,Face verification using large feature sets and one shot similarity,2011
292,LFW,lfw,-27.5953995,-48.6154218,University of Campinas,edu,38a9ca2c49a77b540be52377784b9f734e0417e4,citation,http://homepages.dcc.ufmg.br/~william/papers/paper_2011_IJCB_Faces.pdf,Face verification using large feature sets and one shot similarity,2011
293,LFW,lfw,39.2899685,-76.62196103,University of Maryland,edu,38a9ca2c49a77b540be52377784b9f734e0417e4,citation,http://homepages.dcc.ufmg.br/~william/papers/paper_2011_IJCB_Faces.pdf,Face verification using large feature sets and one shot similarity,2011
294,LFW,lfw,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,ac2881bdf7b57dc1672a17b221d68a438d79fce8,citation,https://arxiv.org/pdf/1806.08472.pdf,Learning a High Fidelity Pose Invariant Model for High-resolution Face Frontalization,2018
295,LFW,lfw,46.0501558,14.46907327,University of Ljubljana,edu,69adbfa7b0b886caac15ebe53b89adce390598a3,citation,https://arxiv.org/pdf/1805.10938.pdf,Face hallucination using cascaded super-resolution and identity priors,2018
296,LFW,lfw,41.70456775,-86.23822026,University of Notre Dame,edu,69adbfa7b0b886caac15ebe53b89adce390598a3,citation,https://arxiv.org/pdf/1805.10938.pdf,Face hallucination using cascaded super-resolution and identity priors,2018
297,LFW,lfw,-33.8840504,151.1992254,University of Technology,edu,8686b15802529ff8aea50995ef14079681788110,citation,https://doi.org/10.1109/TNNLS.2014.2376936,Deformed Graph Laplacian for Semisupervised Learning,2015
298,LFW,lfw,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,8686b15802529ff8aea50995ef14079681788110,citation,https://doi.org/10.1109/TNNLS.2014.2376936,Deformed Graph Laplacian for Semisupervised Learning,2015
299,LFW,lfw,35.9020448,139.93622009,University of Tokyo,edu,9f131b4e036208f2402182a1af2a59e3c5d7dd44,citation,http://dl.acm.org/citation.cfm?id=3206038,Face Retrieval Framework Relying on User's Visual Memory,2018
300,LFW,lfw,33.8898728,130.70856205,Waseda University,edu,9f131b4e036208f2402182a1af2a59e3c5d7dd44,citation,http://dl.acm.org/citation.cfm?id=3206038,Face Retrieval Framework Relying on User's Visual Memory,2018
301,LFW,lfw,47.6543238,-122.30800894,University of Washington,edu,09ce14b84af2dc2f76ae1cf227356fa0ba337d07,citation,http://grail.cs.washington.edu/3dfaces/paper.pdf,Face reconstruction in the wild,2011
302,LFW,lfw,13.0222347,77.56718325,Indian Institute of Science Bangalore,edu,d79365336115661b0e8dbbcd4b2aa1f504b91af6,citation,https://arxiv.org/pdf/1603.01801.pdf,Variational methods for conditional multimodal deep learning,2017
303,LFW,lfw,31.30104395,121.50045497,Fudan University,edu,7df4f96138a4e23492ea96cf921794fc5287ba72,citation,https://arxiv.org/pdf/1707.08705.pdf,A Jointly Learned Deep Architecture for Facial Attribute Analysis and Face Detection in the Wild,2017
304,LFW,lfw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4,citation,http://arxiv.org/pdf/1411.7766v2.pdf,Deep Learning Face Attributes in the Wild,2015
305,LFW,lfw,23.143197,113.34009651,South China Normal University,edu,dc6ad30c7a4bc79bb06b4725b16e202d3d7d8935,citation,http://doi.org/10.1007/s11042-017-4646-5,Age classification with deep learning face representation,2017
306,LFW,lfw,23.0502042,113.39880323,South China University of Technology,edu,dc6ad30c7a4bc79bb06b4725b16e202d3d7d8935,citation,http://doi.org/10.1007/s11042-017-4646-5,Age classification with deep learning face representation,2017
307,LFW,lfw,32.0565957,118.77408833,Nanjing University,edu,bbcb4920b312da201bf4d2359383fb4ee3b17ed9,citation,http://pdfs.semanticscholar.org/bbcb/4920b312da201bf4d2359383fb4ee3b17ed9.pdf,Robust Face Recognition via Multi-Scale Patch-Based Matrix Regression,2016
308,LFW,lfw,32.0575279,118.78682252,Southeast University,edu,feb6e267923868bff6e2108603d00fdfd65251ca,citation,http://pdfs.semanticscholar.org/feb6/e267923868bff6e2108603d00fdfd65251ca.pdf,Unsupervised Discovery of Visual Face Categories,2013
309,LFW,lfw,24.7246403,46.62335012,King Saud University,edu,feb6e267923868bff6e2108603d00fdfd65251ca,citation,http://pdfs.semanticscholar.org/feb6/e267923868bff6e2108603d00fdfd65251ca.pdf,Unsupervised Discovery of Visual Face Categories,2013
310,LFW,lfw,39.5469449,-119.81346566,University of Nevada,edu,feb6e267923868bff6e2108603d00fdfd65251ca,citation,http://pdfs.semanticscholar.org/feb6/e267923868bff6e2108603d00fdfd65251ca.pdf,Unsupervised Discovery of Visual Face Categories,2013
311,LFW,lfw,42.36782045,-71.12666653,Harvard University,edu,8f6263e4d3775757e804796e104631c7a2bb8679,citation,http://pdfs.semanticscholar.org/8f62/63e4d3775757e804796e104631c7a2bb8679.pdf,Characterizing Visual Representations within Convolutional Neural Networks: Toward a Quantitative Approach,2016
312,LFW,lfw,53.406179,-2.96670819,University of Liverpool,edu,2ab034e1f54c37bfc8ae93f7320160748310dc73,citation,https://arxiv.org/pdf/1805.07242.pdf,Siamese Capsule Networks,2018
313,LFW,lfw,44.4962318,11.354157,University of Bologna,edu,1d1a7ef193b958f9074f4f236060a5f5e7642fc1,citation,http://pdfs.semanticscholar.org/db40/804914afbb7f8279ca9a4f52e0ade695f19e.pdf,Ensemble of Patterns of Oriented Edge Magnitudes Descriptors For Face Recognition,2013
314,LFW,lfw,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,36486944b4feeb88c0499fecd253c5a53034a23f,citation,https://doi.org/10.1109/CISP-BMEI.2017.8301986,Deep feature selection and projection for cross-age face retrieval,2017
315,LFW,lfw,49.443232,1.099971,"IRSEEM Rouen, France",edu,e7436b8e68bb7139b823a7572af3decd96241e78,citation,https://doi.org/10.1109/ROBIO.2011.6181560,A new approach for face detection with omnidirectional sensors,2011
316,LFW,lfw,49.3849757,1.0683257,"University of Rouen, France",edu,e7436b8e68bb7139b823a7572af3decd96241e78,citation,https://doi.org/10.1109/ROBIO.2011.6181560,A new approach for face detection with omnidirectional sensors,2011
317,LFW,lfw,48.8476037,2.2639934,"Université Paris-Saclay, France",edu,96e318f8ff91ba0b10348d4de4cb7c2142eb8ba9,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8364450,State-of-the-art face recognition performance using publicly available software and datasets,2018
318,LFW,lfw,40.00229045,116.32098908,Tsinghua University,edu,e8523c4ac9d7aa21f3eb4062e09f2a3bc1eedcf7,citation,https://arxiv.org/pdf/1701.07174.pdf,Toward End-to-End Face Recognition Through Alignment Learning,2017
319,LFW,lfw,47.6543238,-122.30800894,University of Washington,edu,405526dfc79de98f5bf3c97bf4aa9a287700f15d,citation,http://pdfs.semanticscholar.org/8a6c/57fcd99a77982ec754e0b97fd67519ccb60c.pdf,MegaFace: A Million Faces for Recognition at Scale,2015
320,LFW,lfw,42.4505507,-76.4783513,Cornell University,edu,053b263b4a4ccc6f9097ad28ebf39c2957254dfb,citation,http://pdfs.semanticscholar.org/7a49/4b4489408ec3adea15817978ecd2e733f5fe.pdf,Cost-Effective HITs for Relative Similarity Comparisons,2014
321,LFW,lfw,32.87935255,-117.23110049,"University of California, San Diego",edu,053b263b4a4ccc6f9097ad28ebf39c2957254dfb,citation,http://pdfs.semanticscholar.org/7a49/4b4489408ec3adea15817978ecd2e733f5fe.pdf,Cost-Effective HITs for Relative Similarity Comparisons,2014
322,LFW,lfw,37.4102193,-122.05965487,Carnegie Mellon University,edu,82e66c4832386cafcec16b92ac88088ffd1a1bc9,citation,http://pdfs.semanticscholar.org/82e6/6c4832386cafcec16b92ac88088ffd1a1bc9.pdf,OpenFace: A general-purpose face recognition library with mobile applications,2016
323,LFW,lfw,52.4004837,16.95158083,Poznan University of Technology,edu,82e66c4832386cafcec16b92ac88088ffd1a1bc9,citation,http://pdfs.semanticscholar.org/82e6/6c4832386cafcec16b92ac88088ffd1a1bc9.pdf,OpenFace: A general-purpose face recognition library with mobile applications,2016
324,LFW,lfw,50.89273635,-1.39464295,University of Southampton,edu,8bbbdff11e88327816cad3c565f4ab1bb3ee20db,citation,http://doi.ieeecomputersociety.org/10.1109/FG.2017.31,Automatic Semantic Face Recognition,2017
325,LFW,lfw,42.9336278,-78.88394479,SUNY Buffalo,edu,2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d,citation,https://doi.org/10.1109/CVPRW.2011.5981801,Genealogical face recognition based on UB KinFace database,2011
326,LFW,lfw,25.0410728,121.6147562,Institute of Information Science,edu,5f57a1a3a1e5364792b35e8f5f259f92ad561c1f,citation,http://pdfs.semanticscholar.org/5f57/a1a3a1e5364792b35e8f5f259f92ad561c1f.pdf,Implicit Sparse Code Hashing,2015
327,LFW,lfw,22.304572,114.17976285,Hong Kong Polytechnic University,edu,1677d29a108a1c0f27a6a630e74856e7bddcb70d,citation,http://pdfs.semanticscholar.org/1677/d29a108a1c0f27a6a630e74856e7bddcb70d.pdf,Efficient Misalignment-Robust Representation for Real-Time Face Recognition,2012
328,LFW,lfw,38.88140235,121.52281098,Dalian University of Technology,edu,5f4219118556d2c627137827a617cf4e26242a6e,citation,https://doi.org/10.1109/TMM.2017.2751143,Explicit Shape Regression With Characteristic Number for Facial Landmark Localization,2018
329,LFW,lfw,51.49887085,-0.17560797,Imperial College London,edu,84e6669b47670f9f4f49c0085311dce0e178b685,citation,http://pdfs.semanticscholar.org/84e6/669b47670f9f4f49c0085311dce0e178b685.pdf,Face frontalization for Alignment and Recognition,2015
330,LFW,lfw,52.2380139,6.8566761,University of Twente,edu,84e6669b47670f9f4f49c0085311dce0e178b685,citation,http://pdfs.semanticscholar.org/84e6/669b47670f9f4f49c0085311dce0e178b685.pdf,Face frontalization for Alignment and Recognition,2015
331,LFW,lfw,30.19331415,120.11930822,Zhejiang University,edu,5213549200bccec57232fc3ff788ddf1043af7b3,citation,http://doi.acm.org/10.1145/2601097.2601204,Displaced dynamic expression regression for real-time facial tracking and animation,2014
332,LFW,lfw,40.0433204,116.3418109,Beijing Institute of Science and Technology Information,edu,5039834df68600a24e7e8eefb6ba44a5124e67fc,citation,https://doi.org/10.1109/ICIP.2013.6738761,Modular hierarchical feature learning with deep neural networks for face verification,2013
333,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,5039834df68600a24e7e8eefb6ba44a5124e67fc,citation,https://doi.org/10.1109/ICIP.2013.6738761,Modular hierarchical feature learning with deep neural networks for face verification,2013
334,LFW,lfw,23.09461185,113.28788994,Sun Yat-Sen University,edu,44f48a4b1ef94a9104d063e53bf88a69ff0f55f3,citation,http://pdfs.semanticscholar.org/44f4/8a4b1ef94a9104d063e53bf88a69ff0f55f3.pdf,Automatically Building Face Datasets of New Domains from Weakly Labeled Data with Pretrained Models,2016
335,LFW,lfw,41.40657415,2.1945341,Universitat Oberta de Catalunya,edu,6584c3c877400e1689a11ef70133daa86a238602,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8039231,Supervised Committee of Convolutional Neural Networks in Automated Facial Expression Analysis,2018
336,LFW,lfw,51.24303255,-0.59001382,University of Surrey,edu,5763b09ebca9a756b4adebf74d6d7de27e80e298,citation,https://doi.org/10.1109/BTAS.2013.6712738,Picture-specific cohort score normalization for face pair matching,2013
337,LFW,lfw,39.7487516,30.47653071,Eskisehir Osmangazi University,edu,13bda03fc8984d5943ed8d02e49a779d27c84114,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2012.6248047,Efficient object detection using cascades of nearest convex model classifiers,2012
338,LFW,lfw,40.742252,-74.0270949,Stevens Institute of Technology,edu,1e1d7cbbef67e9e042a3a0a9a1bcefcc4a9adacf,citation,http://personal.stevens.edu/~hli18//data/papers/CVPR2016_CameraReady.pdf,A Multi-level Contextual Model for Person Recognition in Photo Albums,2016
339,LFW,lfw,1.3484104,103.68297965,Nanyang Technological University,edu,47190d213caef85e8b9dd0d271dbadc29ed0a953,citation,https://arxiv.org/pdf/1807.11649.pdf,The Devil of Face Recognition is in the Noise,2018
340,LFW,lfw,32.87935255,-117.23110049,"University of California, San Diego",edu,47190d213caef85e8b9dd0d271dbadc29ed0a953,citation,https://arxiv.org/pdf/1807.11649.pdf,The Devil of Face Recognition is in the Noise,2018
341,LFW,lfw,51.0784038,-114.1287077,University of Calgary,edu,e66b4aa85524f493dafde8c75176ac0afad5b79c,citation,https://doi.org/10.1109/SSCI.2017.8285219,Watchlist risk assessment using multiparametric cost and relative entropy,2017
342,LFW,lfw,37.5953979,127.0630499,Hankuk University of Foreign Studies,edu,8af411697e73f6cfe691fe502d4bfb42510b4835,citation,http://pdfs.semanticscholar.org/8af4/11697e73f6cfe691fe502d4bfb42510b4835.pdf,Dynamic Local Ternary Pattern for Face Recognition and Verification,2013
343,LFW,lfw,23.7289899,90.3982682,Institute of Information Technology,edu,8af411697e73f6cfe691fe502d4bfb42510b4835,citation,http://pdfs.semanticscholar.org/8af4/11697e73f6cfe691fe502d4bfb42510b4835.pdf,Dynamic Local Ternary Pattern for Face Recognition and Verification,2013
344,LFW,lfw,23.7316957,90.3965275,University of Dhaka,edu,8af411697e73f6cfe691fe502d4bfb42510b4835,citation,http://pdfs.semanticscholar.org/8af4/11697e73f6cfe691fe502d4bfb42510b4835.pdf,Dynamic Local Ternary Pattern for Face Recognition and Verification,2013
345,LFW,lfw,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,1d3dd9aba79a53390317ec1e0b7cd742cba43132,citation,http://www.cise.ufl.edu/~dihong/assets/Gong_A_Maximum_Entropy_2015_CVPR_paper.pdf,A maximum entropy feature descriptor for age invariant face recognition,2015
346,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,1d3dd9aba79a53390317ec1e0b7cd742cba43132,citation,http://www.cise.ufl.edu/~dihong/assets/Gong_A_Maximum_Entropy_2015_CVPR_paper.pdf,A maximum entropy feature descriptor for age invariant face recognition,2015
347,LFW,lfw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,1d3dd9aba79a53390317ec1e0b7cd742cba43132,citation,http://www.cise.ufl.edu/~dihong/assets/Gong_A_Maximum_Entropy_2015_CVPR_paper.pdf,A maximum entropy feature descriptor for age invariant face recognition,2015
348,LFW,lfw,32.0565957,118.77408833,Nanjing University,edu,539f55c0e2501c1d86791c8b54b225d9b3187b9c,citation,https://doi.org/10.1109/TIP.2017.2738560,Low-Rank Latent Pattern Approximation With Applications to Robust Image Classification,2017
349,LFW,lfw,36.3173432,50.0367286,Azad University,edu,82ccd62f70e669ec770daf11d9611cab0a13047e,citation,http://www.csse.uwa.edu.au/~ajmal/papers/Farshid_DICTA2013.pdf,Sparse Variation Pattern for Texture Classification,2013
350,LFW,lfw,34.68092465,50.05341352,Tafresh University,edu,82ccd62f70e669ec770daf11d9611cab0a13047e,citation,http://www.csse.uwa.edu.au/~ajmal/papers/Farshid_DICTA2013.pdf,Sparse Variation Pattern for Texture Classification,2013
351,LFW,lfw,-31.95040445,115.79790037,University of Western Australia,edu,82ccd62f70e669ec770daf11d9611cab0a13047e,citation,http://www.csse.uwa.edu.au/~ajmal/papers/Farshid_DICTA2013.pdf,Sparse Variation Pattern for Texture Classification,2013
352,LFW,lfw,56.3411984,-2.7930938,University of St Andrews,edu,1b5875dbebc76fec87e72cee7a5263d325a77376,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.528,Learnt Quasi-Transitive Similarity for Retrieval from Large Collections of Faces,2016
353,LFW,lfw,24.4399419,118.09301781,Xiamen University,edu,5632ba72b2652df3b648b2ee698233e76a4eee65,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8346387,Reconstruction of 3D facial image using a single 2D image,2018
354,LFW,lfw,-33.3578899,151.37834708,University of Newcastle,edu,5632ba72b2652df3b648b2ee698233e76a4eee65,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8346387,Reconstruction of 3D facial image using a single 2D image,2018
355,LFW,lfw,33.5609504,73.07125966,Foundation University Rawalpindi Campus,edu,7c42371bae54050dbbf7ded1e7a9b4109a23a482,citation,http://pdfs.semanticscholar.org/7c42/371bae54050dbbf7ded1e7a9b4109a23a482.pdf,Optimized features selection using hybrid PSO-GA for multi-view gender classification,2015
356,LFW,lfw,31.4466149,74.2679762,University of Central Punjab,edu,7c42371bae54050dbbf7ded1e7a9b4109a23a482,citation,http://pdfs.semanticscholar.org/7c42/371bae54050dbbf7ded1e7a9b4109a23a482.pdf,Optimized features selection using hybrid PSO-GA for multi-view gender classification,2015
357,LFW,lfw,26.39793625,50.19807924,University of Dammam,edu,7c42371bae54050dbbf7ded1e7a9b4109a23a482,citation,http://pdfs.semanticscholar.org/7c42/371bae54050dbbf7ded1e7a9b4109a23a482.pdf,Optimized features selection using hybrid PSO-GA for multi-view gender classification,2015
358,LFW,lfw,48.14955455,11.56775314,Technical University Munich,edu,d23ec100432d860b12308941f8539af82a28843f,citation,https://arxiv.org/pdf/1810.10901.pdf,Adversarial Semantic Scene Completion from a Single Depth Image,2018
359,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,43fe03ec1acb6ea9d05d2b22eeddb2631bd30437,citation,https://doi.org/10.1109/ICIP.2017.8296394,Weakly supervised multiscale-inception learning for web-scale face recognition,2017
360,LFW,lfw,46.109237,7.08453549,IDIAP Research Institute,edu,93971a49ef6cc88a139420349a1dfd85fb5d3f5c,citation,http://pdfs.semanticscholar.org/9397/1a49ef6cc88a139420349a1dfd85fb5d3f5c.pdf,Scalable Probabilistic Models: Applied to Face Identification in the Wild,2014
361,LFW,lfw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,bf8a520533f401347e2f55da17383a3e567ef6d8,citation,http://pdfs.semanticscholar.org/bf8a/520533f401347e2f55da17383a3e567ef6d8.pdf,Bounded-Distortion Metric Learning,2015
362,LFW,lfw,40.00229045,116.32098908,Tsinghua University,edu,bf8a520533f401347e2f55da17383a3e567ef6d8,citation,http://pdfs.semanticscholar.org/bf8a/520533f401347e2f55da17383a3e567ef6d8.pdf,Bounded-Distortion Metric Learning,2015
363,LFW,lfw,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,bf8a520533f401347e2f55da17383a3e567ef6d8,citation,http://pdfs.semanticscholar.org/bf8a/520533f401347e2f55da17383a3e567ef6d8.pdf,Bounded-Distortion Metric Learning,2015
364,LFW,lfw,13.01119095,74.79498825,"National Institute of Technology, Karnataka",edu,e9e40e588f8e6510fa5537e0c9e083ceed5d07ad,citation,http://pdfs.semanticscholar.org/e9e4/0e588f8e6510fa5537e0c9e083ceed5d07ad.pdf,Fast Face Detection Using Graphics Processor,2011
365,LFW,lfw,37.4102193,-122.05965487,Carnegie Mellon University,edu,192235f5a9e4c9d6a28ec0d333e36f294b32f764,citation,http://www.andrew.cmu.edu/user/sjayasur/iccv.pdf,Reconfiguring the Imaging Pipeline for Computer Vision,2017
366,LFW,lfw,42.4505507,-76.4783513,Cornell University,edu,192235f5a9e4c9d6a28ec0d333e36f294b32f764,citation,http://www.andrew.cmu.edu/user/sjayasur/iccv.pdf,Reconfiguring the Imaging Pipeline for Computer Vision,2017
367,LFW,lfw,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,cd6aaa37fffd0b5c2320f386be322b8adaa1cc68,citation,https://arxiv.org/pdf/1804.06655.pdf,Deep Face Recognition: A Survey,2018
368,LFW,lfw,-33.8809651,151.20107299,University of Technology Sydney,edu,e4e3faa47bb567491eaeaebb2213bf0e1db989e1,citation,http://pdfs.semanticscholar.org/e4e3/faa47bb567491eaeaebb2213bf0e1db989e1.pdf,Empirical Risk Minimization for Metric Learning Using Privileged Information,2016
369,LFW,lfw,31.846918,117.29053367,Hefei University of Technology,edu,e4e3faa47bb567491eaeaebb2213bf0e1db989e1,citation,http://pdfs.semanticscholar.org/e4e3/faa47bb567491eaeaebb2213bf0e1db989e1.pdf,Empirical Risk Minimization for Metric Learning Using Privileged Information,2016
370,LFW,lfw,23.1353836,113.29470496,Guangdong University of Technology,edu,4b02387c2db968a70b69d98da3c443f139099e91,citation,http://pdfs.semanticscholar.org/4b02/387c2db968a70b69d98da3c443f139099e91.pdf,Detecting facial landmarks in the video based on a hybrid framework,2016
371,LFW,lfw,1.3484104,103.68297965,Nanyang Technological University,edu,f7c50d2be9fba0e4527fd9fbe3095e9d9a94fdd3,citation,http://pdfs.semanticscholar.org/f7c5/0d2be9fba0e4527fd9fbe3095e9d9a94fdd3.pdf,Large Margin Multi-metric Learning for Face and Kinship Verification in the Wild,2014
372,LFW,lfw,40.8419836,-73.94368971,Columbia University,edu,217de4ff802d4904d3f90d2e24a29371307942fe,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2013.128,"POOF: Part-Based One-vs.-One Features for Fine-Grained Categorization, Face Verification, and Attribute Estimation",2013
373,LFW,lfw,40.47913175,-74.43168868,Rutgers University,edu,e3b324101157daede3b4d16bdc9c2388e849c7d4,citation,https://pdfs.semanticscholar.org/e3b3/24101157daede3b4d16bdc9c2388e849c7d4.pdf,"Robust Real-Time 3 D Face Tracking from RGBD Videos under Extreme Pose , Depth , and Expression Variations",2017
374,LFW,lfw,49.10184375,8.4331256,Karlsruhe Institute of Technology,edu,8ee5b1c9fb0bded3578113c738060290403ed472,citation,https://infoscience.epfl.ch/record/200452/files/wacv2014-RGE.pdf,Extending explicit shape regression with mixed feature channels and pose priors,2014
375,LFW,lfw,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,0c59071ddd33849bd431165bc2d21bbe165a81e0,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Oh_Person_Recognition_in_ICCV_2015_paper.pdf,Person Recognition in Personal Photo Collections,2015
376,LFW,lfw,36.20304395,117.05842113,Tianjin University,edu,4223917177405eaa6bdedca061eb28f7b440ed8e,citation,http://pdfs.semanticscholar.org/4223/917177405eaa6bdedca061eb28f7b440ed8e.pdf,B-spline Shape from Motion & Shading: An Automatic Free-form Surface Modeling for Face Reconstruction,2016
377,LFW,lfw,2.92749755,101.64185301,Multimedia University,edu,90ad0daa279c3e30b360f9fe9371293d68f4cebf,citation,http://pdfs.semanticscholar.org/90ad/0daa279c3e30b360f9fe9371293d68f4cebf.pdf,Spatio-temporal Framework and Algorithms for Video-based Face Recognition,2015
378,LFW,lfw,23.09461185,113.28788994,Sun Yat-Sen University,edu,c675534be881e59a78a5986b8fb4e649ddd2abbe,citation,https://doi.org/10.1109/ICIP.2017.8296548,Face recognition by landmark pooling-based CNN with concentrate loss,2017
379,LFW,lfw,50.7338124,7.1022465,Rheinische-Friedrich-Wilhelms University,edu,561ae67de137e75e9642ab3512d3749b34484310,citation,http://pdfs.semanticscholar.org/561a/e67de137e75e9642ab3512d3749b34484310.pdf,DeepGestalt - Identifying Rare Genetic Syndromes Using Deep Learning,2018
380,LFW,lfw,32.1119889,34.80459702,Tel Aviv University,edu,561ae67de137e75e9642ab3512d3749b34484310,citation,http://pdfs.semanticscholar.org/561a/e67de137e75e9642ab3512d3749b34484310.pdf,DeepGestalt - Identifying Rare Genetic Syndromes Using Deep Learning,2018
381,LFW,lfw,32.87935255,-117.23110049,"University of California, San Diego",edu,561ae67de137e75e9642ab3512d3749b34484310,citation,http://pdfs.semanticscholar.org/561a/e67de137e75e9642ab3512d3749b34484310.pdf,DeepGestalt - Identifying Rare Genetic Syndromes Using Deep Learning,2018
382,LFW,lfw,1.3484104,103.68297965,Nanyang Technological University,edu,39f03d1dfd94e6f06c1565d7d1bb14ab0eee03bc,citation,http://openaccess.thecvf.com/content_iccv_2015/papers/Lu_Simultaneous_Local_Binary_ICCV_2015_paper.pdf,Simultaneous Local Binary Feature Learning and Encoding for Face Recognition,2015
383,LFW,lfw,40.00229045,116.32098908,Tsinghua University,edu,39f03d1dfd94e6f06c1565d7d1bb14ab0eee03bc,citation,http://openaccess.thecvf.com/content_iccv_2015/papers/Lu_Simultaneous_Local_Binary_ICCV_2015_paper.pdf,Simultaneous Local Binary Feature Learning and Encoding for Face Recognition,2015
384,LFW,lfw,37.4102193,-122.05965487,Carnegie Mellon University,edu,2b869d5551b10f13bf6fcdb8d13f0aa4d1f59fc4,citation,https://arxiv.org/pdf/1803.00130.pdf,Ring loss: Convex Feature Normalization for Face Recognition,2018
385,LFW,lfw,37.4102193,-122.05965487,Carnegie Mellon University,edu,b1fdd4ae17d82612cefd4e78b690847b071379d3,citation,https://pdfs.semanticscholar.org/4fc5/416b6c7173d3462e5be796bda3ad8d5645a1.pdf,Supervised Descent Method,2015
386,LFW,lfw,51.49887085,-0.17560797,Imperial College London,edu,1921795408345751791b44b379f51b7dd54ebfa2,citation,https://arxiv.org/pdf/1807.07872.pdf,From Face Recognition to Models of Identity: A Bayesian Approach to Learning About Unknown Identities from Unsupervised Data,2018
387,LFW,lfw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,f05ad40246656a977cf321c8299158435e3f3b61,citation,http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Lu_Face_Recognition_Using_2013_ICCV_paper.pdf,Face Recognition Using Face Patch Networks,2013
388,LFW,lfw,1.3484104,103.68297965,Nanyang Technological University,edu,5134353bd01c4ea36bd007c460e8972b1541d0ad,citation,https://pdfs.semanticscholar.org/5134/353bd01c4ea36bd007c460e8972b1541d0ad.pdf,Face Recognition with Multi-Resolution Spectral Feature Images,2013
389,LFW,lfw,31.76909325,117.17795091,Anhui University,edu,5134353bd01c4ea36bd007c460e8972b1541d0ad,citation,https://pdfs.semanticscholar.org/5134/353bd01c4ea36bd007c460e8972b1541d0ad.pdf,Face Recognition with Multi-Resolution Spectral Feature Images,2013
390,LFW,lfw,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,29db046dd1f8100b279c3f5f5c5ef19bdbf5af9a,citation,https://arxiv.org/pdf/1706.04717.pdf,Recent Progress of Face Image Synthesis,2017
391,LFW,lfw,38.88140235,121.52281098,Dalian University of Technology,edu,940e5c45511b63f609568dce2ad61437c5e39683,citation,https://doi.org/10.1109/TIP.2015.2390976,Fiducial Facial Point Extraction Using a Novel Projective Invariant,2015
392,LFW,lfw,42.36782045,-71.12666653,Harvard University,edu,785eeac2e236a85a45b4e0356c0745279c31e089,citation,https://doi.org/10.1109/TIFS.2014.2359543,Learning Person-Specific Representations From Faces in the Wild,2014
393,LFW,lfw,-22.8137765,-47.0640004,State University of Campinas,edu,785eeac2e236a85a45b4e0356c0745279c31e089,citation,https://doi.org/10.1109/TIFS.2014.2359543,Learning Person-Specific Representations From Faces in the Wild,2014
394,LFW,lfw,52.17638955,0.14308882,University of Cambridge,edu,9901f473aeea177a55e58bac8fd4f1b086e575a4,citation,https://arxiv.org/pdf/1509.04954.pdf,Human and sheep facial landmarks localisation by triplet interpolated features,2016
395,LFW,lfw,25.4299114,81.7711827,"IIIT Allahabad, India",edu,e1449be4951ba7519945cd1ad50656c3516113da,citation,https://doi.org/10.1109/TCSVT.2016.2603535,Local Gradient Hexa Pattern: A Descriptor for Face Recognition and Retrieval,2018
396,LFW,lfw,41.1664858,-73.1920564,University of Bridgeport,edu,f92ade569cbe54344ffd3bb25efd366dcd8ad659,citation,https://arxiv.org/pdf/1704.01464.pdf,Effect of Super Resolution on High Dimensional Features for Unsupervised Face Recognition in the Wild,2017
397,LFW,lfw,25.2873992,110.3324277,Guilin University of Electronic Technology Guangxi Guilin,edu,9989ad33b64accea8042e386ff3f1216386ba7f1,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8393320,Facial feature extraction method based on shallow and deep fusion CNN,2017
398,LFW,lfw,53.21967825,6.56251482,University of Groningen,edu,8efda5708bbcf658d4f567e3866e3549fe045bbb,citation,http://pdfs.semanticscholar.org/8efd/a5708bbcf658d4f567e3866e3549fe045bbb.pdf,Pre-trained Deep Convolutional Neural Networks for Face Recognition,2018
399,LFW,lfw,39.2899685,-76.62196103,University of Maryland,edu,3b092733f428b12f1f920638f868ed1e8663fe57,citation,http://www.math.jhu.edu/~data/RamaPapers/PerformanceBounds.pdf,On the size of Convolutional Neural Networks and generalization performance,2016
400,LFW,lfw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,55966926e7c28b1eee1c7eb7a0b11b10605a1af0,citation,http://pdfs.semanticscholar.org/baa8/bdeb5aa545af5b5f43efaf9dda08490da0bc.pdf,Surpassing Human-Level Face Verification Performance on LFW with GaussianFace,2015
401,LFW,lfw,65.0592157,25.46632601,University of Oulu,edu,e6d6203fa911429d76f026e2ec2de260ec520432,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7899663,Siamese network features for image matching,2016
402,LFW,lfw,60.18558755,24.8242733,Aalto University,edu,e6d6203fa911429d76f026e2ec2de260ec520432,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7899663,Siamese network features for image matching,2016
403,LFW,lfw,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,4d3c4c3fe8742821242368e87cd72da0bd7d3783,citation,http://www.ee.cuhk.edu.hk/~xgwang/papers/sunWTiccv13.pdf,Hybrid Deep Learning for Face Verification,2013
404,LFW,lfw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,4d3c4c3fe8742821242368e87cd72da0bd7d3783,citation,http://www.ee.cuhk.edu.hk/~xgwang/papers/sunWTiccv13.pdf,Hybrid Deep Learning for Face Verification,2013
405,LFW,lfw,51.49887085,-0.17560797,Imperial College London,edu,9af9a88c60d9e4b53e759823c439fc590a4b5bc5,citation,https://arxiv.org/pdf/1708.00277.pdf,Learning Deep Convolutional Embeddings for Face Representation Using Joint Sample- and Set-Based Supervision,2017
406,LFW,lfw,46.109237,7.08453549,IDIAP Research Institute,edu,939123cf21dc9189a03671484c734091b240183e,citation,http://publications.idiap.ch/downloads/papers/2015/Erdogmus_MMSP_2015.pdf,Within- and cross- database evaluations for face gender classification via befit protocols,2014
407,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,061e29eae705f318eee703b9e17dc0989547ba0c,citation,http://pdfs.semanticscholar.org/061e/29eae705f318eee703b9e17dc0989547ba0c.pdf,Enhancing Expression Recognition in the Wild with Unlabeled Reference Data,2012
408,LFW,lfw,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,435642641312364e45f4989fac0901b205c49d53,citation,http://pdfs.semanticscholar.org/4356/42641312364e45f4989fac0901b205c49d53.pdf,Face Model Compression by Distilling Knowledge from Neurons,2016
409,LFW,lfw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,435642641312364e45f4989fac0901b205c49d53,citation,http://pdfs.semanticscholar.org/4356/42641312364e45f4989fac0901b205c49d53.pdf,Face Model Compression by Distilling Knowledge from Neurons,2016
410,LFW,lfw,23.09461185,113.28788994,Sun Yat-Sen University,edu,80d42f74ee9bf03f3790c8d0f5a307deffe0b3b7,citation,https://doi.org/10.1109/TNNLS.2016.2522431,Learning Kernel Extended Dictionary for Face Recognition,2017
411,LFW,lfw,36.3693473,120.673818,Shandong University,edu,edbddf8c176d6e914f0babe64ad56c051597d415,citation,https://doi.org/10.1109/TMM.2016.2644866,Predicting Image Memorability Through Adaptive Transfer Learning From External Sources,2017
412,LFW,lfw,36.20304395,117.05842113,Tianjin University,edu,edbddf8c176d6e914f0babe64ad56c051597d415,citation,https://doi.org/10.1109/TMM.2016.2644866,Predicting Image Memorability Through Adaptive Transfer Learning From External Sources,2017
413,LFW,lfw,42.3583961,-71.09567788,MIT,edu,18c6c92c39c8a5a2bb8b5673f339d3c26b8dcaae,citation,http://pdfs.semanticscholar.org/18c6/c92c39c8a5a2bb8b5673f339d3c26b8dcaae.pdf,Learning invariant representations and applications to face verification,2013
414,LFW,lfw,42.3626295,-71.0914481,McGovern Institute for Brain Research,edu,18c6c92c39c8a5a2bb8b5673f339d3c26b8dcaae,citation,http://pdfs.semanticscholar.org/18c6/c92c39c8a5a2bb8b5673f339d3c26b8dcaae.pdf,Learning invariant representations and applications to face verification,2013
415,LFW,lfw,50.0764296,14.41802312,Czech Technical University,edu,37c8514df89337f34421dc27b86d0eb45b660a5e,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w25/papers/Uricar_Facial_Landmark_Tracking_ICCV_2015_paper.pdf,Facial Landmark Tracking by Tree-Based Deformable Part Model Based Detector,2015
416,LFW,lfw,39.86948105,-84.87956905,Indiana University,edu,f3a59d85b7458394e3c043d8277aa1ffe3cdac91,citation,https://arxiv.org/pdf/1802.09900.pdf,Query-Free Attacks on Industry-Grade Face Recognition Systems under Resource Constraints,2018
417,LFW,lfw,17.4454957,78.34854698,International Institute of Information Technology,edu,96e1ccfe96566e3c96d7b86e134fa698c01f2289,citation,https://arxiv.org/pdf/1712.00321.pdf,Semi-adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images,2018
418,LFW,lfw,42.718568,-84.47791571,Michigan State University,edu,96e1ccfe96566e3c96d7b86e134fa698c01f2289,citation,https://arxiv.org/pdf/1712.00321.pdf,Semi-adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images,2018
419,LFW,lfw,5.4409448,10.07120561,University of Dschang,edu,d78fbd11f12cbc194e8ede761d292dc2c02d38a2,citation,http://pdfs.semanticscholar.org/d78f/bd11f12cbc194e8ede761d292dc2c02d38a2.pdf,Enhancing Gray Scale Images for Face Detection under Unstable Lighting Condition,2017
420,LFW,lfw,26.88111275,112.62850666,Hunan University,edu,86d0127e1fd04c3d8ea78401c838af621647dc95,citation,https://arxiv.org/pdf/1804.02810.pdf,A Novel Multi-Task Tensor Correlation Neural Network for Facial Attribute Prediction,2018
421,LFW,lfw,28.2290209,112.99483204,"National University of Defense Technology, China",edu,86d0127e1fd04c3d8ea78401c838af621647dc95,citation,https://arxiv.org/pdf/1804.02810.pdf,A Novel Multi-Task Tensor Correlation Neural Network for Facial Attribute Prediction,2018
422,LFW,lfw,29.58333105,-98.61944505,University of Texas at San Antonio,edu,86d0127e1fd04c3d8ea78401c838af621647dc95,citation,https://arxiv.org/pdf/1804.02810.pdf,A Novel Multi-Task Tensor Correlation Neural Network for Facial Attribute Prediction,2018
423,LFW,lfw,43.47061295,-80.54724732,University of Waterloo,edu,103a7c3eba36792886ae8005f6492332e6b05bad,citation,https://arxiv.org/pdf/1809.06218.pdf,Facial Recognition with Encoded Local Projections,2018
424,LFW,lfw,47.6423318,-122.1369302,Microsoft,company,291265db88023e92bb8c8e6390438e5da148e8f5,citation,http://pdfs.semanticscholar.org/4603/cb8e05258bb0572ae912ad20903b8f99f4b1.pdf,MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition,2016
425,LFW,lfw,39.1254938,-77.22293475,National Institute of Standards and Technology,edu,35d42f4e7a1d898bc8e2d052c38e1106f3e80188,citation,https://doi.org/10.1109/BTAS.2015.7358765,Human and algorithm performance on the PaSC face Recognition Challenge,2015
426,LFW,lfw,32.9820799,-96.7566278,University of Texas at Dallas,edu,35d42f4e7a1d898bc8e2d052c38e1106f3e80188,citation,https://doi.org/10.1109/BTAS.2015.7358765,Human and algorithm performance on the PaSC face Recognition Challenge,2015
427,LFW,lfw,51.49887085,-0.17560797,Imperial College London,edu,06d7ef72fae1be206070b9119fb6b61ce4699587,citation,http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Zafeiriou_On_One-Shot_Similarity_2013_ICCV_paper.pdf,On One-Shot Similarity Kernels: Explicit Feature Maps and Properties,2013
428,LFW,lfw,51.59029705,-0.22963221,Middlesex University,edu,06d7ef72fae1be206070b9119fb6b61ce4699587,citation,http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Zafeiriou_On_One-Shot_Similarity_2013_ICCV_paper.pdf,On One-Shot Similarity Kernels: Explicit Feature Maps and Properties,2013
429,LFW,lfw,38.2899482,21.7886469,University of Patras,edu,06d7ef72fae1be206070b9119fb6b61ce4699587,citation,http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Zafeiriou_On_One-Shot_Similarity_2013_ICCV_paper.pdf,On One-Shot Similarity Kernels: Explicit Feature Maps and Properties,2013
430,LFW,lfw,1.2962018,103.77689944,National University of Singapore,edu,c17c7b201cfd0bcd75441afeaa734544c6ca3416,citation,https://doi.org/10.1109/TCSVT.2016.2587389,Layerwise Class-Aware Convolutional Neural Network,2017
431,LFW,lfw,32.0575279,118.78682252,Southeast University,edu,c17c7b201cfd0bcd75441afeaa734544c6ca3416,citation,https://doi.org/10.1109/TCSVT.2016.2587389,Layerwise Class-Aware Convolutional Neural Network,2017
432,LFW,lfw,29.7207902,-95.34406271,University of Houston,edu,e6da1fcd2a8cda0c69b3d94812caa7d844903007,citation,http://dl.acm.org/citation.cfm?id=3137154,"Sonicdoor: scaling person identification with ultrasonic sensors by novel modeling of shape, behavior and walking patterns",2017
433,LFW,lfw,23.09461185,113.28788994,Sun Yat-Sen University,edu,cd74d606e76ecddee75279679d9770cdc0b49861,citation,https://doi.org/10.1109/TIP.2014.2365725,Transfer Learning of Structured Representation for Face Recognition,2014
434,LFW,lfw,23.7289899,90.3982682,Institute of Information Technology,edu,2e58ec57d71b2b2a3e71086234dd7037559cc17e,citation,https://pdfs.semanticscholar.org/2e58/ec57d71b2b2a3e71086234dd7037559cc17e.pdf,A Gender Recognition System from Facial Image,2018
435,LFW,lfw,23.7316957,90.3965275,University of Dhaka,edu,2e58ec57d71b2b2a3e71086234dd7037559cc17e,citation,https://pdfs.semanticscholar.org/2e58/ec57d71b2b2a3e71086234dd7037559cc17e.pdf,A Gender Recognition System from Facial Image,2018
436,LFW,lfw,36.3697191,127.362537,Korea Advanced Institute of Science and Technology,edu,8f99f7ccb85af6d4b9e015a9b215c529126e7844,citation,https://doi.org/10.1109/ROMAN.2017.8172359,Face image-based age and gender estimation with consideration of ethnic difference,2017
437,LFW,lfw,42.718568,-84.47791571,Michigan State University,edu,4b605e6a9362485bfe69950432fa1f896e7d19bf,citation,http://biometrics.cse.msu.edu/Publications/Face/BlantonAllenMillerKalkaJain_CVPRWB2016_HID.pdf,A Comparison of Human and Automated Face Verification Accuracy on Unconstrained Image Sets,2016
438,LFW,lfw,22.34000115,114.16970291,City University of Hong Kong,edu,2af2b74c3462ccff3a6881ff7cf4f321b3242fa9,citation,http://yugangjiang.info/publication/JCST-nameface.pdf,"Name-Face Association in Web Videos: A Large-Scale Dataset, Baselines, and Open Issues",2014
439,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,2af2b74c3462ccff3a6881ff7cf4f321b3242fa9,citation,http://yugangjiang.info/publication/JCST-nameface.pdf,"Name-Face Association in Web Videos: A Large-Scale Dataset, Baselines, and Open Issues",2014
440,LFW,lfw,31.30104395,121.50045497,Fudan University,edu,2af2b74c3462ccff3a6881ff7cf4f321b3242fa9,citation,http://yugangjiang.info/publication/JCST-nameface.pdf,"Name-Face Association in Web Videos: A Large-Scale Dataset, Baselines, and Open Issues",2014
441,LFW,lfw,29.7207902,-95.34406271,University of Houston,edu,9ff931ca721d50e470e1a38e583c7b18b6cdc2cc,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7407637,An Overview and Empirical Comparison of Distance Metric Learning Methods,2017
442,LFW,lfw,40.48256135,-3.6906079,Universidad Autonoma de Madrid,edu,578117ff493d691166fefc52fd61bad70d8752a9,citation,https://doi.org/10.1109/CCST.2016.7815707,Dealing with occlusions in face recognition by region-based fusion,2016
443,LFW,lfw,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,3cea3aba77649d718991d0cb30135887267c11e8,citation,https://arxiv.org/pdf/1809.00594.pdf,Adversarial Attack Type I: Generating False Positives,2018
444,LFW,lfw,38.99203005,-76.9461029,University of Maryland College Park,edu,053931267af79a89791479b18d1b9cde3edcb415,citation,https://pdfs.semanticscholar.org/0539/31267af79a89791479b18d1b9cde3edcb415.pdf,Attributes for Improved Attributes: A Multi-Task Network Utilizing Implicit and Explicit Relationships for Facial Attribute Classification,2017
445,LFW,lfw,39.2899685,-76.62196103,University of Maryland,edu,053931267af79a89791479b18d1b9cde3edcb415,citation,https://pdfs.semanticscholar.org/0539/31267af79a89791479b18d1b9cde3edcb415.pdf,Attributes for Improved Attributes: A Multi-Task Network Utilizing Implicit and Explicit Relationships for Facial Attribute Classification,2017
446,LFW,lfw,42.718568,-84.47791571,Michigan State University,edu,02467703b6e087799e04e321bea3a4c354c5487d,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2016.27,Grouper: Optimizing Crowdsourced Face Annotations,2016
447,LFW,lfw,46.0501558,14.46907327,University of Ljubljana,edu,afe9cfba90d4b1dbd7db1cf60faf91f24d12b286,citation,http://pdfs.semanticscholar.org/afe9/cfba90d4b1dbd7db1cf60faf91f24d12b286.pdf,Principal Directions of Synthetic Exact Filters for Robust Real-Time Eye Localization,2011
448,LFW,lfw,53.21967825,6.56251482,University of Groningen,edu,4ff4c27e47b0aa80d6383427642bb8ee9d01c0ac,citation,https://doi.org/10.1109/SSCI.2015.37,Deep Convolutional Neural Networks and Support Vector Machines for Gender Recognition,2015
449,LFW,lfw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,0ce3a786aed896d128f5efdf78733cc675970854,citation,http://pdfs.semanticscholar.org/3689/2b6bb4848a9c21158b8eded7f14a6654dd7e.pdf,Learning the Face Prior for Bayesian Face Recognition,2014
450,LFW,lfw,37.3936717,-122.0807262,Facebook,company,628a3f027b7646f398c68a680add48c7969ab1d9,citation,https://pdfs.semanticscholar.org/628a/3f027b7646f398c68a680add48c7969ab1d9.pdf,Plan for Final Year Project : HKU-Face : A Large Scale Dataset for Deep Face Recognition,2017
451,LFW,lfw,40.00229045,116.32098908,Tsinghua University,edu,9fc993aeb0a007ccfaca369a9a8c0ccf7697261d,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7936534,Context-Aware Local Binary Feature Learning for Face Recognition,2018
452,LFW,lfw,43.7776426,11.259765,University of Florence,edu,746c0205fdf191a737df7af000eaec9409ede73f,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8423119,Investigating Nuisances in DCNN-Based Face Recognition,2018
453,LFW,lfw,53.8338371,10.7035939,Institute of Systems and Robotics,edu,3802c97f925cb03bac91d9db13d8b777dfd29dcc,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2014.232,Non-parametric Bayesian Constrained Local Models,2014
454,LFW,lfw,43.7776426,11.259765,University of Florence,edu,71ca8b6e84c17b3e68f980bfb8cddc837100f8bf,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7899774,Effective 3D based frontalization for unconstrained face recognition,2016
455,LFW,lfw,37.5901411,127.0362318,Korea University,edu,5957936195c10521dadc9b90ca9b159eb1fc4871,citation,https://doi.org/10.1109/TCE.2016.7838098,LBP-ferns-based feature extraction for robust facial recognition,2016
456,LFW,lfw,25.37461295,51.48980354,Qatar University,edu,d0d75a7116a76ccd98a3aeb6f6fff10ba91de1c1,citation,https://doi.org/10.1109/TIP.2015.2502144,Constrained Metric Learning by Permutation Inducing Isometries,2016
457,LFW,lfw,-31.95040445,115.79790037,University of Western Australia,edu,d0d75a7116a76ccd98a3aeb6f6fff10ba91de1c1,citation,https://doi.org/10.1109/TIP.2015.2502144,Constrained Metric Learning by Permutation Inducing Isometries,2016
458,LFW,lfw,-35.2776999,149.118527,Australian National University,edu,79db191ca1268dc88271abef3179c4fe4ee92aed,citation,https://pdfs.semanticscholar.org/79db/191ca1268dc88271abef3179c4fe4ee92aed.pdf,Facial Expression Based Automatic Album Creation,2010
459,LFW,lfw,-35.23656905,149.08446994,University of Canberra,edu,79db191ca1268dc88271abef3179c4fe4ee92aed,citation,https://pdfs.semanticscholar.org/79db/191ca1268dc88271abef3179c4fe4ee92aed.pdf,Facial Expression Based Automatic Album Creation,2010
460,LFW,lfw,-33.8809651,151.20107299,University of Technology Sydney,edu,3983370efe7a7521bde255017171724d845b3383,citation,https://arxiv.org/pdf/1810.01152.pdf,Learning Discriminators as Energy Networks in Adversarial Learning,2018
461,LFW,lfw,41.6659,-91.57310307,University of Iowa,edu,3983370efe7a7521bde255017171724d845b3383,citation,https://arxiv.org/pdf/1810.01152.pdf,Learning Discriminators as Energy Networks in Adversarial Learning,2018
462,LFW,lfw,40.7423025,-74.17928172,New Jersey Institute of Technology,edu,faf19885431cb39360158982c3a1127f6090a1f6,citation,https://doi.org/10.1109/BTAS.2015.7358768,Inheritable Fisher vector feature for kinship verification,2015
463,LFW,lfw,51.4584837,-2.6097752,University of Bristol,edu,c4f3185f010027a0a97fcb9753d74eb27a9cfd3e,citation,http://doi.org/10.1016/j.patrec.2015.02.006,Learning to classify gender from four million images,2015
464,LFW,lfw,12.81608485,74.92449278,Mangalore University,edu,b68452e28951bf8db5f1193eca3a8fd9e2d0d7ef,citation,https://doi.org/10.1109/ICACCI.2015.7275752,Approximate radial gradient transform based face recognition,2015
465,LFW,lfw,42.3583961,-71.09567788,MIT,edu,5e0e516226413ea1e973f1a24e2fdedde98e7ec0,citation,http://pdfs.semanticscholar.org/74ce/97da57ec848db660ee69dec709f226c74f43.pdf,The Invariance Hypothesis and the Ventral Stream,2013
466,LFW,lfw,53.21967825,6.56251482,University of Groningen,edu,d8896861126b7fd5d2ceb6fed8505a6dff83414f,citation,http://pdfs.semanticscholar.org/d889/6861126b7fd5d2ceb6fed8505a6dff83414f.pdf,In-plane Rotational Alignment of Faces by Eye and Eye-pair Detection,2015
467,LFW,lfw,39.9808333,116.34101249,Beihang University,edu,70d2ab1af0edd5c0a30d576a5d4aa397c4f92d3e,citation,http://doi.org/10.1007/s11042-018-5608-2,Elastic preserving projections based on L1-norm maximization,2018
468,LFW,lfw,38.99203005,-76.9461029,University of Maryland College Park,edu,b2cd92d930ed9b8d3f9dfcfff733f8384aa93de8,citation,http://pdfs.semanticscholar.org/b2cd/92d930ed9b8d3f9dfcfff733f8384aa93de8.pdf,"HyperFace: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition",2016
469,LFW,lfw,39.2899685,-76.62196103,University of Maryland,edu,b2cd92d930ed9b8d3f9dfcfff733f8384aa93de8,citation,http://pdfs.semanticscholar.org/b2cd/92d930ed9b8d3f9dfcfff733f8384aa93de8.pdf,"HyperFace: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition",2016
470,LFW,lfw,35.9042272,-78.85565763,"IBM Research, North Carolina",company,148eb413bede35487198ce7851997bf8721ea2d6,citation,http://pdfs.semanticscholar.org/148e/b413bede35487198ce7851997bf8721ea2d6.pdf,People Search in Surveillance Videos,2009
471,LFW,lfw,38.8964679,-104.8050594,University of Colorado at Colorado Springs,edu,e3e2c106ccbd668fb9fca851498c662add257036,citation,http://www.vast.uccs.edu/~tboult/PAPERS/BTAS13-Sapkota-et-al-Ensembles.pdf,"Appearance, context and co-occurrence ensembles for identity recognition in personal photo collections",2013
472,LFW,lfw,42.3383668,-71.08793524,Northeastern University,edu,d22b378fb4ef241d8d210202893518d08e0bb213,citation,http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Zhang_Random_Faces_Guided_2013_ICCV_paper.pdf,Random Faces Guided Sparse Many-to-One Encoder for Pose-Invariant Face Recognition,2013
473,LFW,lfw,1.2962018,103.77689944,National University of Singapore,edu,dbb9601a1d2febcce4c07dd2b819243d81abb2c2,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8361884,Landmark Free Face Attribute Prediction,2018
474,LFW,lfw,1.27486,103.797787,"SAP Innovation Center Network, Singapore",company,dbb9601a1d2febcce4c07dd2b819243d81abb2c2,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8361884,Landmark Free Face Attribute Prediction,2018
475,LFW,lfw,37.4092265,-122.0236615,"Baidu Research, USA",company,8633732d9f787f8497c2696309c7d70176995c15,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7298967,Multi-objective convolutional learning for face labeling,2015
476,LFW,lfw,37.36566745,-120.42158888,"University of California, Merced",edu,8633732d9f787f8497c2696309c7d70176995c15,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7298967,Multi-objective convolutional learning for face labeling,2015
477,LFW,lfw,42.718568,-84.47791571,Michigan State University,edu,7fb5006b6522436ece5bedf509e79bdb7b79c9a7,citation,https://pdfs.semanticscholar.org/7fb5/006b6522436ece5bedf509e79bdb7b79c9a7.pdf,Multi-Task Convolutional Neural Network for Face Recognition,2017
478,LFW,lfw,42.3889785,-72.5286987,University of Massachusetts,edu,368e99f669ea5fd395b3193cd75b301a76150f9d,citation,https://arxiv.org/pdf/1506.01342.pdf,One-to-many face recognition with bilinear CNNs,2016
479,LFW,lfw,33.5866784,-101.87539204,Electrical and Computer Engineering,edu,c18a03568d4b512a0d8380cbb1fbf6bd56d11f05,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8430403,A Wearable IoT with Complex Artificial Perception Embedding for Alzheimer Patients,2018
480,LFW,lfw,32.1119889,34.80459702,Tel Aviv University,edu,a6b5ffb5b406abfda2509cae66cdcf56b4bb3837,citation,http://pdfs.semanticscholar.org/bce2/02717ce134b317b39f0a18151659d643875b.pdf,One Shot Similarity Metric Learning for Action Recognition,2011
481,LFW,lfw,52.02453775,-0.70927481,Open University,edu,a6b5ffb5b406abfda2509cae66cdcf56b4bb3837,citation,http://pdfs.semanticscholar.org/bce2/02717ce134b317b39f0a18151659d643875b.pdf,One Shot Similarity Metric Learning for Action Recognition,2011
482,LFW,lfw,31.9078499,34.81334092,Weizmann Institute of Science,edu,a6b5ffb5b406abfda2509cae66cdcf56b4bb3837,citation,http://pdfs.semanticscholar.org/bce2/02717ce134b317b39f0a18151659d643875b.pdf,One Shot Similarity Metric Learning for Action Recognition,2011
483,LFW,lfw,38.99203005,-76.9461029,University of Maryland College Park,edu,4f36c14d1453fc9d6481b09c5a09e91d8d9ee47a,citation,http://pdfs.semanticscholar.org/4f36/c14d1453fc9d6481b09c5a09e91d8d9ee47a.pdf,Video-Based Face Recognition Using the Intra/Extra-Personal Difference Dictionary,2014
484,LFW,lfw,39.2899685,-76.62196103,University of Maryland,edu,4f36c14d1453fc9d6481b09c5a09e91d8d9ee47a,citation,http://pdfs.semanticscholar.org/4f36/c14d1453fc9d6481b09c5a09e91d8d9ee47a.pdf,Video-Based Face Recognition Using the Intra/Extra-Personal Difference Dictionary,2014
485,LFW,lfw,42.0958077,-75.91455689,Binghamton University,edu,c07ab025d9e3c885ad5386e6f000543efe091c4b,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8302601,Preserving Model Privacy for Machine Learning in Distributed Systems,2018
486,LFW,lfw,29.6328784,-82.3490133,University of Florida,edu,c07ab025d9e3c885ad5386e6f000543efe091c4b,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8302601,Preserving Model Privacy for Machine Learning in Distributed Systems,2018
487,LFW,lfw,39.9922379,116.30393816,Peking University,edu,5798055e11e25c404b1b0027bc9331bcc6e00555,citation,http://doi.acm.org/10.1145/2393347.2396357,PDSS: patch-descriptor-similarity space for effective face verification,2012
488,LFW,lfw,51.49887085,-0.17560797,Imperial College London,edu,c43ed9b34cad1a3976bac7979808eb038d88af84,citation,https://arxiv.org/pdf/1804.03675.pdf,Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model,2018
489,LFW,lfw,51.24303255,-0.59001382,University of Surrey,edu,c43ed9b34cad1a3976bac7979808eb038d88af84,citation,https://arxiv.org/pdf/1804.03675.pdf,Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model,2018
490,LFW,lfw,42.3889785,-72.5286987,University of Massachusetts,edu,c98983592777952d1751103b4d397d3ace00852d,citation,https://pdfs.semanticscholar.org/c989/83592777952d1751103b4d397d3ace00852d.pdf,Face Synthesis from Facial Identity Features,2017
491,LFW,lfw,22.304572,114.17976285,Hong Kong Polytechnic University,edu,48174c414cfce7f1d71c4401d2b3d49ba91c5338,citation,http://pdfs.semanticscholar.org/4817/4c414cfce7f1d71c4401d2b3d49ba91c5338.pdf,Robust Performance-driven 3D Face Tracking in Long Range Depth Scenes,2015
492,LFW,lfw,40.47913175,-74.43168868,Rutgers University,edu,48174c414cfce7f1d71c4401d2b3d49ba91c5338,citation,http://pdfs.semanticscholar.org/4817/4c414cfce7f1d71c4401d2b3d49ba91c5338.pdf,Robust Performance-driven 3D Face Tracking in Long Range Depth Scenes,2015
493,LFW,lfw,1.3484104,103.68297965,Nanyang Technological University,edu,48174c414cfce7f1d71c4401d2b3d49ba91c5338,citation,http://pdfs.semanticscholar.org/4817/4c414cfce7f1d71c4401d2b3d49ba91c5338.pdf,Robust Performance-driven 3D Face Tracking in Long Range Depth Scenes,2015
494,LFW,lfw,35.9542493,-83.9307395,University of Tennessee,edu,c2e03efd8c5217188ab685e73cc2e52c54835d1a,citation,http://doi.ieeecomputersociety.org/10.1109/WACV.2016.7477585,Deep tree-structured face: A unified representation for multi-task facial biometrics,2016
495,LFW,lfw,39.94976005,116.33629046,Beijing Jiaotong University,edu,21959bc56a160ebd450606867dce1462a913afab,citation,http://doi.org/10.1007/s11042-018-6071-9,Face recognition based on manifold constrained joint sparse sensing with K-SVD,2018
496,LFW,lfw,-32.00686365,115.89691775,Curtin University,edu,21959bc56a160ebd450606867dce1462a913afab,citation,http://doi.org/10.1007/s11042-018-6071-9,Face recognition based on manifold constrained joint sparse sensing with K-SVD,2018
497,LFW,lfw,31.32235655,121.38400941,Shanghai University,edu,21959bc56a160ebd450606867dce1462a913afab,citation,http://doi.org/10.1007/s11042-018-6071-9,Face recognition based on manifold constrained joint sparse sensing with K-SVD,2018
498,LFW,lfw,31.2284923,121.40211389,East China Normal University,edu,06518858bd99cddf9bc9200fac5311fc29ac33b4,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8392777,Sparse Low-Rank Component-Based Representation for Face Recognition With Low-Quality Images,2019
499,LFW,lfw,31.28473925,121.49694909,Tongji University,edu,06518858bd99cddf9bc9200fac5311fc29ac33b4,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8392777,Sparse Low-Rank Component-Based Representation for Face Recognition With Low-Quality Images,2019
500,LFW,lfw,39.87391435,116.47722285,Beijing University of Technology,edu,f1d6da83dcf71eda45a56a86c5ae13e7f45a8536,citation,https://doi.org/10.1109/ACCESS.2017.2737544,A Secure Face-Verification Scheme Based on Homomorphic Encryption and Deep Neural Networks,2017
501,LFW,lfw,25.0410728,121.6147562,Institute of Information Science,edu,337dd4aaca2c5f9b5d2de8e0e2401b5a8feb9958,citation,https://arxiv.org/pdf/1810.11160.pdf,Data-specific Adaptive Threshold for Face Recognition and Authentication,2018
502,LFW,lfw,37.4102193,-122.05965487,Carnegie Mellon University,edu,4d16337cc0431cd43043dfef839ce5f0717c3483,citation,http://pdfs.semanticscholar.org/4d16/337cc0431cd43043dfef839ce5f0717c3483.pdf,A Scalable and Privacy-Aware IoT Service for Live Video Analytics,2017
503,LFW,lfw,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,1d696a1beb42515ab16f3a9f6f72584a41492a03,citation,http://www.ee.cuhk.edu.hk/~xgwang/papers/sunWTcvpr15.pdf,"Deeply learned face representations are sparse, selective, and robust",2015
504,LFW,lfw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,1d696a1beb42515ab16f3a9f6f72584a41492a03,citation,http://www.ee.cuhk.edu.hk/~xgwang/papers/sunWTcvpr15.pdf,"Deeply learned face representations are sparse, selective, and robust",2015
505,LFW,lfw,53.8925662,-122.81471592,University of Northern British Columbia,edu,683ec608442617d11200cfbcd816e86ce9ec0899,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2014.342,Dual Linear Regression Based Classification for Face Cluster Recognition,2014
506,LFW,lfw,45.42580475,-75.68740118,University of Ottawa,edu,65293ecf6a4c5ab037a2afb4a9a1def95e194e5f,citation,http://pdfs.semanticscholar.org/6529/3ecf6a4c5ab037a2afb4a9a1def95e194e5f.pdf,"Face , Age and Gender Recognition using Local Descriptors",2014
507,LFW,lfw,28.54632595,77.27325504,Indian Institute of Technology Delhi,edu,25d514d26ecbc147becf4117512523412e1f060b,citation,https://doi.org/10.1109/ICB.2015.7139083,Annotated crowd video face database,2015
508,LFW,lfw,40.62984145,22.9588935,Aristotle University of Thessaloniki,edu,e40cb4369c6402ae53c81ce52b73df3ef89f578b,citation,http://doi.org/10.1016/j.image.2015.01.009,Facial image clustering in stereoscopic videos using double spectral analysis,2015
509,LFW,lfw,42.3889785,-72.5286987,University of Massachusetts,edu,2d3482dcff69c7417c7b933f22de606a0e8e42d4,citation,http://pdfs.semanticscholar.org/2d34/82dcff69c7417c7b933f22de606a0e8e42d4.pdf,Labeled Faces in the Wild : Updates and New Reporting Procedures,2014
510,LFW,lfw,40.00229045,116.32098908,Tsinghua University,edu,4ea4116f57c5d5033569690871ba294dc3649ea5,citation,http://pdfs.semanticscholar.org/4ea4/116f57c5d5033569690871ba294dc3649ea5.pdf,Multi-View Face Alignment Using 3D Shape Model for View Estimation,2009
511,LFW,lfw,51.49887085,-0.17560797,Imperial College London,edu,7cffcb4f24343a924a8317d560202ba9ed26cd0b,citation,https://arxiv.org/pdf/1708.06997.pdf,The unconstrained ear recognition challenge,2017
512,LFW,lfw,34.8452999,48.5596212,Islamic Azad University,edu,7cffcb4f24343a924a8317d560202ba9ed26cd0b,citation,https://arxiv.org/pdf/1708.06997.pdf,The unconstrained ear recognition challenge,2017
513,LFW,lfw,38.8920756,-104.79716389,"University of Colorado, Colorado Springs",edu,7cffcb4f24343a924a8317d560202ba9ed26cd0b,citation,https://arxiv.org/pdf/1708.06997.pdf,The unconstrained ear recognition challenge,2017
514,LFW,lfw,46.0501558,14.46907327,University of Ljubljana,edu,7cffcb4f24343a924a8317d560202ba9ed26cd0b,citation,https://arxiv.org/pdf/1708.06997.pdf,The unconstrained ear recognition challenge,2017
515,LFW,lfw,17.4454957,78.34854698,International Institute of Information Technology,edu,185263189a30986e31566394680d6d16b0089772,citation,https://pdfs.semanticscholar.org/1852/63189a30986e31566394680d6d16b0089772.pdf,Efficient Annotation of Objects for Video Analysis,2018
516,LFW,lfw,42.718568,-84.47791571,Michigan State University,edu,5da827fe558fb2e1124dcc84ef08311241761726,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7139096,Attribute preserved face de-identification,2015
517,LFW,lfw,24.4399419,118.09301781,Xiamen University,edu,1de23d7fe718d9fab0159f58f422099e44ad3f0a,citation,http://doi.org/10.1007/s11063-016-9558-2,Locality Preserving Collaborative Representation for Face Recognition,2016
518,LFW,lfw,39.2899685,-76.62196103,University of Maryland,edu,4f7b92bd678772552b3c3edfc9a7c5c4a8c60a8e,citation,https://pdfs.semanticscholar.org/4f7b/92bd678772552b3c3edfc9a7c5c4a8c60a8e.pdf,Deep Density Clustering of Unconstrained Faces,0
519,LFW,lfw,35.97320905,-78.89755054,North Carolina Central University,edu,5bb53fb36a47b355e9a6962257dd465cd7ad6827,citation,http://pdfs.semanticscholar.org/5bb5/3fb36a47b355e9a6962257dd465cd7ad6827.pdf,Mask-off: Synthesizing Face Images in the Presence of Head-mounted Displays,2016
520,LFW,lfw,38.0333742,-84.5017758,University of Kentucky,edu,5bb53fb36a47b355e9a6962257dd465cd7ad6827,citation,http://pdfs.semanticscholar.org/5bb5/3fb36a47b355e9a6962257dd465cd7ad6827.pdf,Mask-off: Synthesizing Face Images in the Presence of Head-mounted Displays,2016
521,LFW,lfw,40.4319722,-86.92389368,Purdue University,edu,b18858ad6ec88d8b443dffd3e944e653178bc28b,citation,http://pdfs.semanticscholar.org/b188/58ad6ec88d8b443dffd3e944e653178bc28b.pdf,Trojaning Attack on Neural Networks,2017
522,LFW,lfw,1.2962018,103.77689944,National University of Singapore,edu,ee7093e91466b81d13f4d6933bcee48e4ee63a16,citation,http://pdfs.semanticscholar.org/ee70/93e91466b81d13f4d6933bcee48e4ee63a16.pdf,Discovering Person Identity via Large-Scale Observations,2014
523,LFW,lfw,47.6423318,-122.1369302,Microsoft,company,687e17db5043661f8921fb86f215e9ca2264d4d2,citation,http://www.ece.northwestern.edu/~ganghua/publication/ICCV09a.pdf,A robust elastic and partial matching metric for face recognition,2009
524,LFW,lfw,45.1929245,5.7661983,"GIPSA-Lab, Grenoble, France",edu,fffe5ab3351deab81f7562d06764551422dbd9c4,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7163114,Fully automated facial picture evaluation using high level attributes,2015
525,LFW,lfw,31.846918,117.29053367,Hefei University of Technology,edu,1ba9d12f24ac04f0309e8ff9b0162c6e18d97dc3,citation,http://doi.acm.org/10.1145/2964284.2984061,Robust Face Recognition with Deep Multi-View Representation Learning,2016
526,LFW,lfw,1.2962018,103.77689944,National University of Singapore,edu,1ba9d12f24ac04f0309e8ff9b0162c6e18d97dc3,citation,http://doi.acm.org/10.1145/2964284.2984061,Robust Face Recognition with Deep Multi-View Representation Learning,2016
527,LFW,lfw,32.77824165,34.99565673,Open University of Israel,edu,870433ba89d8cab1656e57ac78f1c26f4998edfb,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2017.163,Regressing Robust and Discriminative 3D Morphable Models with a Very Deep Neural Network,2017
528,LFW,lfw,30.284151,-97.73195598,University of Texas at Austin,edu,e3a6e5a573619a97bd6662b652ea7d088ec0b352,citation,https://arxiv.org/pdf/1804.00112.pdf,Compare and Contrast: Learning Prominent Visual Differences,2018
529,LFW,lfw,40.00229045,116.32098908,Tsinghua University,edu,60737db62fb5fab742371709485e4b2ddf64b7b2,citation,http://doi.acm.org/10.1145/3132847.3132891,Crowdsourced Selection on Multi-Attribute Data,2017
530,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,288964068cd87d97a98b8bc927d6e0d2349458a2,citation,https://pdfs.semanticscholar.org/2889/64068cd87d97a98b8bc927d6e0d2349458a2.pdf,Mean-Variance Loss for Deep Age Estimation from a Face,0
531,LFW,lfw,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,288964068cd87d97a98b8bc927d6e0d2349458a2,citation,https://pdfs.semanticscholar.org/2889/64068cd87d97a98b8bc927d6e0d2349458a2.pdf,Mean-Variance Loss for Deep Age Estimation from a Face,0
532,LFW,lfw,-27.47715625,153.02841004,Queensland University of Technology,edu,59d45281707b85a33d6f50c6ac6b148eedd71a25,citation,http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Cheng_Rank_Minimization_across_2013_ICCV_paper.pdf,Rank Minimization across Appearance and Shape for AAM Ensemble Fitting,2013
533,LFW,lfw,40.34829285,-74.66308325,Princeton University,edu,818ecc8c8d4dc398b01a852df90cb8d972530fa5,citation,https://arxiv.org/pdf/1806.06098.pdf,Unsupervised Training for 3D Morphable Model Regression,2018
534,LFW,lfw,42.3619407,-71.0904378,MIT CSAIL,edu,818ecc8c8d4dc398b01a852df90cb8d972530fa5,citation,https://arxiv.org/pdf/1806.06098.pdf,Unsupervised Training for 3D Morphable Model Regression,2018
535,LFW,lfw,31.83907195,117.26420748,University of Science and Technology of China,edu,d99b5ee3e2d7e3a016fbc5fd417304e15efbd1f8,citation,http://doi.org/10.1007/s11063-017-9578-6,A Novel Two-stage Learning Pipeline for Deep Neural Networks,2017
536,LFW,lfw,38.8920756,-104.79716389,"University of Colorado, Colorado Springs",edu,4b3f425274b0c2297d136f8833a31866db2f2aec,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2017.85,Toward Open-Set Face Recognition,2017
537,LFW,lfw,46.0501558,14.46907327,University of Ljubljana,edu,12003a7d65c4f98fb57587fd0e764b44d0d10125,citation,http://doi.ieeecomputersociety.org/10.1109/FG.2015.7284835,Face recognition in the wild with the Probabilistic Gabor-Fisher Classifier,2015
538,LFW,lfw,36.3697191,127.362537,Korea Advanced Institute of Science and Technology,edu,7a09e8f65bd85d4c79f0ae90d4e2685869a9894f,citation,https://doi.org/10.1109/TMM.2016.2551698,Face and Hair Region Labeling Using Semi-Supervised Spectral Clustering-Based Multiple Segmentations,2016
539,LFW,lfw,36.399184,127.394656,Korea Institute of Oriental Medicine,edu,7a09e8f65bd85d4c79f0ae90d4e2685869a9894f,citation,https://doi.org/10.1109/TMM.2016.2551698,Face and Hair Region Labeling Using Semi-Supervised Spectral Clustering-Based Multiple Segmentations,2016
540,LFW,lfw,41.21002475,-73.80407056,IBM Thomas J. Watson Research Center,company,2bcd9b2b78eb353ea57cf50387083900eae5384a,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5995329,Image ranking and retrieval based on multi-attribute queries,2011
541,LFW,lfw,39.2899685,-76.62196103,University of Maryland,edu,2bcd9b2b78eb353ea57cf50387083900eae5384a,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5995329,Image ranking and retrieval based on multi-attribute queries,2011
542,LFW,lfw,36.383765,127.36694,"Electronics and Telecommunications Research Institute, Daejeon, Korea",edu,77c5437107f8138d48cb7e10b2b286fa51473678,citation,https://doi.org/10.1109/URAI.2016.7734005,A pseudo ensemble convolutional neural networks,2016
543,LFW,lfw,36.3851395,127.3683413,"University of Science and Technology, Korea",edu,77c5437107f8138d48cb7e10b2b286fa51473678,citation,https://doi.org/10.1109/URAI.2016.7734005,A pseudo ensemble convolutional neural networks,2016
544,LFW,lfw,39.2899685,-76.62196103,University of Maryland,edu,872dfdeccf99bbbed7c8f1ea08afb2d713ebe085,citation,https://arxiv.org/pdf/1703.09507.pdf,L2-constrained Softmax Loss for Discriminative Face Verification,2017
545,LFW,lfw,23.09461185,113.28788994,Sun Yat-Sen University,edu,57ca530e9acb63487e8591cb6efb89473aa1e5b4,citation,https://doi.org/10.1109/TIP.2014.2356292,Multilayer Surface Albedo for Face Recognition With Reference Images in Bad Lighting Conditions,2014
546,LFW,lfw,39.2899685,-76.62196103,University of Maryland,edu,a8748a79e8d37e395354ba7a8b3038468cb37e1f,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2016.47,Seeing the Forest from the Trees: A Holistic Approach to Near-Infrared Heterogeneous Face Recognition,2016
547,LFW,lfw,39.65404635,-79.96475355,West Virginia University,edu,a8748a79e8d37e395354ba7a8b3038468cb37e1f,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2016.47,Seeing the Forest from the Trees: A Holistic Approach to Near-Infrared Heterogeneous Face Recognition,2016
548,LFW,lfw,47.6543238,-122.30800894,University of Washington,edu,be28ed1be084385f5d389db25fd7f56cd2d7f7bf,citation,https://arxiv.org/pdf/1706.03864.pdf,Exploring computation-communication tradeoffs in camera systems,2017
549,LFW,lfw,42.3583961,-71.09567788,MIT,edu,0e652a99761d2664f28f8931fee5b1d6b78c2a82,citation,http://pdfs.semanticscholar.org/0e65/2a99761d2664f28f8931fee5b1d6b78c2a82.pdf,Making a Science of Model Search,2012
550,LFW,lfw,53.8338371,10.7035939,Institute of Systems and Robotics,edu,6604fd47f92ce66dd0c669dd66b347b80e17ebc9,citation,https://pdfs.semanticscholar.org/6604/fd47f92ce66dd0c669dd66b347b80e17ebc9.pdf,Simultaneous Cascaded Regression,2018
551,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,46e72046a9bb2d4982d60bcf5c63dbc622717f0f,citation,https://arxiv.org/pdf/1605.02424.pdf,Learning Discriminative Features with Class Encoder,2016
552,LFW,lfw,37.52914535,45.04886077,Urmia University,edu,2a92bda6dbd5cce5894f7d370d798c07fa8783f4,citation,https://doi.org/10.1109/TIFS.2014.2359587,Class-Specific Kernel Fusion of Multiple Descriptors for Face Verification Using Multiscale Binarised Statistical Image Features,2014
553,LFW,lfw,51.24303255,-0.59001382,University of Surrey,edu,2a92bda6dbd5cce5894f7d370d798c07fa8783f4,citation,https://doi.org/10.1109/TIFS.2014.2359587,Class-Specific Kernel Fusion of Multiple Descriptors for Face Verification Using Multiscale Binarised Statistical Image Features,2014
554,LFW,lfw,22.3386304,114.2620337,Hong Kong University of Science and Technology,edu,52bf00df3b970e017e4e2f8079202460f1c0e1bd,citation,http://pdfs.semanticscholar.org/52bf/00df3b970e017e4e2f8079202460f1c0e1bd.pdf,Learning High-level Prior with Convolutional Neural Networks for Semantic Segmentation,2015
555,LFW,lfw,40.00229045,116.32098908,Tsinghua University,edu,52bf00df3b970e017e4e2f8079202460f1c0e1bd,citation,http://pdfs.semanticscholar.org/52bf/00df3b970e017e4e2f8079202460f1c0e1bd.pdf,Learning High-level Prior with Convolutional Neural Networks for Semantic Segmentation,2015
556,LFW,lfw,31.83907195,117.26420748,University of Science and Technology of China,edu,52bf00df3b970e017e4e2f8079202460f1c0e1bd,citation,http://pdfs.semanticscholar.org/52bf/00df3b970e017e4e2f8079202460f1c0e1bd.pdf,Learning High-level Prior with Convolutional Neural Networks for Semantic Segmentation,2015
557,LFW,lfw,47.5612651,7.5752961,University of Basel,edu,0081e2188c8f34fcea3e23c49fb3e17883b33551,citation,http://pdfs.semanticscholar.org/0081/e2188c8f34fcea3e23c49fb3e17883b33551.pdf,Training Deep Face Recognition Systems with Synthetic Data,2018
558,LFW,lfw,30.642769,104.06751175,"Sichuan University, Chengdu",edu,2201f187a7483982c2e8e2585ad9907c5e66671d,citation,https://pdfs.semanticscholar.org/1cad/9aa5095733b56e998ad0cd396e89c2bc9928.pdf,Joint Face Alignment and 3D Face Reconstruction,2016
559,LFW,lfw,42.718568,-84.47791571,Michigan State University,edu,2201f187a7483982c2e8e2585ad9907c5e66671d,citation,https://pdfs.semanticscholar.org/1cad/9aa5095733b56e998ad0cd396e89c2bc9928.pdf,Joint Face Alignment and 3D Face Reconstruction,2016
560,LFW,lfw,23.0502042,113.39880323,South China University of Technology,edu,4bd3de97b256b96556d19a5db71dda519934fd53,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.529,Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition,2016
561,LFW,lfw,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,4bd3de97b256b96556d19a5db71dda519934fd53,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.529,Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition,2016
562,LFW,lfw,37.4102193,-122.05965487,Carnegie Mellon University,edu,831d661d657d97a07894da8639a048c430c5536d,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2016.19,Weakly Supervised Facial Analysis with Dense Hyper-Column Features,2016
563,LFW,lfw,40.51865195,-74.44099801,State University of New Jersey,edu,0d746111135c2e7f91443869003d05cde3044beb,citation,https://doi.org/10.1109/ICIP.2016.7532908,Partial face detection for continuous authentication,2016
564,LFW,lfw,39.2899685,-76.62196103,University of Maryland,edu,0d746111135c2e7f91443869003d05cde3044beb,citation,https://doi.org/10.1109/ICIP.2016.7532908,Partial face detection for continuous authentication,2016
565,LFW,lfw,47.6423318,-122.1369302,Microsoft,company,3bd10f7603c4f5a4737c5613722124787d0dd818,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7415949,An Efficient Joint Formulation for Bayesian Face Verification,2017
566,LFW,lfw,32.1119889,34.80459702,Tel Aviv University,edu,63a6c256ec2cf2e0e0c9a43a085f5bc94af84265,citation,https://doi.org/10.1109/ICPR.2016.7899662,Complexity of multiverse networks and their multilayer generalization,2016
567,LFW,lfw,40.5709358,-105.08655256,Colorado State University,edu,38a2661b6b995a3c4d69e7d5160b7596f89ce0e6,citation,http://www.cs.colostate.edu/~draper/papers/zhang_ijcb14.pdf,Randomized Intraclass-Distance Minimizing Binary Codes for face recognition,2014
568,LFW,lfw,39.1254938,-77.22293475,National Institute of Standards and Technology,edu,38a2661b6b995a3c4d69e7d5160b7596f89ce0e6,citation,http://www.cs.colostate.edu/~draper/papers/zhang_ijcb14.pdf,Randomized Intraclass-Distance Minimizing Binary Codes for face recognition,2014
569,LFW,lfw,29.5084174,106.57858552,Chongqing University,edu,aa1129780cc496918085cd0603a774345c353c54,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7779010,Evolutionary Cost-Sensitive Discriminative Learning With Application to Vision and Olfaction,2017
570,LFW,lfw,22.304572,114.17976285,Hong Kong Polytechnic University,edu,aa1129780cc496918085cd0603a774345c353c54,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7779010,Evolutionary Cost-Sensitive Discriminative Learning With Application to Vision and Olfaction,2017
571,LFW,lfw,51.49887085,-0.17560797,Imperial College London,edu,0de91641f37b0a81a892e4c914b46d05d33fd36e,citation,https://ibug.doc.ic.ac.uk/media/uploads/documents/raps.pdf,RAPS: Robust and Efficient Automatic Construction of Person-Specific Deformable Models,2014
572,LFW,lfw,52.2380139,6.8566761,University of Twente,edu,0de91641f37b0a81a892e4c914b46d05d33fd36e,citation,https://ibug.doc.ic.ac.uk/media/uploads/documents/raps.pdf,RAPS: Robust and Efficient Automatic Construction of Person-Specific Deformable Models,2014
573,LFW,lfw,43.07982815,-89.43066425,University of Wisconsin Madison,edu,2e091b311ac48c18aaedbb5117e94213f1dbb529,citation,http://pdfs.semanticscholar.org/b1a1/a049f1d78f6e3d072236237c467292ccd537.pdf,Collaborative Facial Landmark Localization for Transferring Annotations Across Datasets,2014
574,LFW,lfw,42.37289,-72.518814,Amherst College,edu,00075519a794ea546b2ca3ca105e2f65e2f5f471,citation,http://pdfs.semanticscholar.org/0007/5519a794ea546b2ca3ca105e2f65e2f5f471.pdf,"Generating a Large, Freely-Available Dataset for Face-Related Algorithms",2010
575,LFW,lfw,41.70456775,-86.23822026,University of Notre Dame,edu,d6791b98353aa113d79f6fb96335aa6c7ea3b759,citation,https://doi.org/10.1109/TNNLS.2017.2648122,Collaborative Random Faces-Guided Encoders for Pose-Invariant Face Representation Learning,2018
576,LFW,lfw,41.62772475,-71.00724501,University of Massachusetts Dartmouth,edu,d6791b98353aa113d79f6fb96335aa6c7ea3b759,citation,https://doi.org/10.1109/TNNLS.2017.2648122,Collaborative Random Faces-Guided Encoders for Pose-Invariant Face Representation Learning,2018
577,LFW,lfw,42.3383668,-71.08793524,Northeastern University,edu,d6791b98353aa113d79f6fb96335aa6c7ea3b759,citation,https://doi.org/10.1109/TNNLS.2017.2648122,Collaborative Random Faces-Guided Encoders for Pose-Invariant Face Representation Learning,2018
578,LFW,lfw,47.5612651,7.5752961,University of Basel,edu,7caa3a74313f9a7a2dd5b4c2cd7f825d895d3794,citation,http://doi.org/10.1007/s11263-016-0967-5,Markov Chain Monte Carlo for Automated Face Image Analysis,2016
579,LFW,lfw,40.00229045,116.32098908,Tsinghua University,edu,1860b8f63ce501bd0dfa9e6f2debc080e88d9baa,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7894195,Local Large-Margin Multi-Metric Learning for Face and Kinship Verification,2018
580,LFW,lfw,1.3484104,103.68297965,Nanyang Technological University,edu,1860b8f63ce501bd0dfa9e6f2debc080e88d9baa,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7894195,Local Large-Margin Multi-Metric Learning for Face and Kinship Verification,2018
581,LFW,lfw,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,7a131fafa7058fb75fdca32d0529bc7cb50429bd,citation,https://arxiv.org/pdf/1704.04086.pdf,Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis,2017
582,LFW,lfw,38.0333742,-84.5017758,University of Kentucky,edu,2cd7821fcf5fae53a185624f7eeda007434ae037,citation,http://cs.uky.edu/~jacobs/papers/islam2014faces.pdf,Exploring the geo-dependence of human face appearance,2014
583,LFW,lfw,40.72925325,-73.99625394,New York University,edu,270acff7916589a6cc9ca915b0012ffcb75d4899,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8425659,On the Applications of Robust PCA in Image and Video Processing,2018
584,LFW,lfw,52.3793131,-1.5604252,University of Warwick,edu,270acff7916589a6cc9ca915b0012ffcb75d4899,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8425659,On the Applications of Robust PCA in Image and Video Processing,2018
585,LFW,lfw,39.9922379,116.30393816,Peking University,edu,270acff7916589a6cc9ca915b0012ffcb75d4899,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8425659,On the Applications of Robust PCA in Image and Video Processing,2018
586,LFW,lfw,37.4102193,-122.05965487,Carnegie Mellon University,edu,270acff7916589a6cc9ca915b0012ffcb75d4899,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8425659,On the Applications of Robust PCA in Image and Video Processing,2018
587,LFW,lfw,22.304572,114.17976285,Hong Kong Polytechnic University,edu,588bed36b3cc9e2f26c39b5d99d6687f36ae1177,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5771389,Sparsely Encoded Local Descriptor for face recognition,2011
588,LFW,lfw,39.9041999,116.4073963,Chinese Academy of Science,edu,588bed36b3cc9e2f26c39b5d99d6687f36ae1177,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5771389,Sparsely Encoded Local Descriptor for face recognition,2011
589,LFW,lfw,42.4505507,-76.4783513,Cornell University,edu,8bdf6f03bde08c424c214188b35be8b2dec7cdea,citation,https://arxiv.org/pdf/1805.04049.pdf,Inference Attacks Against Collaborative Learning,2018
590,LFW,lfw,1.3484104,103.68297965,Nanyang Technological University,edu,09b43b59879d59493df2a93c216746f2cf50f4ac,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/1A_036_ext.pdf,Deep Transfer Metric Learning,2015
591,LFW,lfw,-27.47715625,153.02841004,Queensland University of Technology,edu,6342a4c54835c1e14159495373ab18b4233d2d9b,citation,http://pdfs.semanticscholar.org/6342/a4c54835c1e14159495373ab18b4233d2d9b.pdf,Towards Pose-robust Face Recognition on Video,2014
592,LFW,lfw,34.13710185,-118.12527487,California Institute of Technology,edu,34108098e1a378bc15a5824812bdf2229b938678,citation,http://pdfs.semanticscholar.org/3410/8098e1a378bc15a5824812bdf2229b938678.pdf,Reconstructive Sparse Code Transfer for Contour Detection and Semantic Labeling,2014
593,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,1fd6004345245daf101c98935387e6ef651cbb55,citation,http://pdfs.semanticscholar.org/1fd6/004345245daf101c98935387e6ef651cbb55.pdf,Learning Symmetry Features for Face Detection Based on Sparse Group Lasso,2013
594,LFW,lfw,65.0592157,25.46632601,University of Oulu,edu,5dc52c64991c655a12936867594326cf6352eb8e,citation,https://pdfs.semanticscholar.org/5dc5/2c64991c655a12936867594326cf6352eb8e.pdf,Constructing Local Binary Pattern Statistics by Soft Voting,2013
595,LFW,lfw,42.2942142,-83.71003894,University of Michigan,edu,2c424f21607ff6c92e640bfe3da9ff105c08fac4,citation,https://pdfs.semanticscholar.org/3f25/e17eb717e5894e0404ea634451332f85d287.pdf,Learning Structured Output Representation using Deep Conditional Generative Models,2015
596,LFW,lfw,34.13710185,-118.12527487,California Institute of Technology,edu,241d2c517dbc0e22d7b8698e06ace67de5f26fdf,citation,http://pdfs.semanticscholar.org/bfc3/546fa119443fdcbac3a5723647c2ba0007ac.pdf,"Online, Real-Time Tracking Using a Category-to-Individual Detector",2014
597,LFW,lfw,1.3037257,103.7737763,"Advanced Digital Sciences Center, Singapore",edu,13901473a12061f080b9d54219f16db7d406e769,citation,https://doi.org/10.1109/TIP.2012.2222895,High-Order Local Spatial Context Modeling by Spatialized Random Forest,2013
598,LFW,lfw,29.58333105,-98.61944505,University of Texas at San Antonio,edu,13901473a12061f080b9d54219f16db7d406e769,citation,https://doi.org/10.1109/TIP.2012.2222895,High-Order Local Spatial Context Modeling by Spatialized Random Forest,2013
599,LFW,lfw,1.2962018,103.77689944,National University of Singapore,edu,13901473a12061f080b9d54219f16db7d406e769,citation,https://doi.org/10.1109/TIP.2012.2222895,High-Order Local Spatial Context Modeling by Spatialized Random Forest,2013
600,LFW,lfw,31.846918,117.29053367,Hefei University of Technology,edu,13901473a12061f080b9d54219f16db7d406e769,citation,https://doi.org/10.1109/TIP.2012.2222895,High-Order Local Spatial Context Modeling by Spatialized Random Forest,2013
601,LFW,lfw,42.3583961,-71.09567788,MIT,edu,3f5e8f884e71310d7d5571bd98e5a049b8175075,citation,https://pdfs.semanticscholar.org/3f5e/8f884e71310d7d5571bd98e5a049b8175075.pdf,Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures,2013
602,LFW,lfw,37.4102193,-122.05965487,Carnegie Mellon University,edu,adaed4e92c93eb005198e41f87cf079e46050b5a,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Pal_Discriminative_Invariant_Kernel_CVPR_2016_paper.pdf,Discriminative Invariant Kernel Features: A Bells-and-Whistles-Free Approach to Unsupervised Face Recognition and Pose Estimation,2016
603,LFW,lfw,42.718568,-84.47791571,Michigan State University,edu,a2b4a6c6b32900a066d0257ae6d4526db872afe2,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8272466,Learning Face Image Quality From Human Assessments,2018
604,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,99facca6fc50cc30f13b7b6dd49ace24bc94f702,citation,https://arxiv.org/pdf/1609.03892.pdf,VIPLFaceNet: an open source deep face recognition SDK,2016
605,LFW,lfw,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,99facca6fc50cc30f13b7b6dd49ace24bc94f702,citation,https://arxiv.org/pdf/1609.03892.pdf,VIPLFaceNet: an open source deep face recognition SDK,2016
606,LFW,lfw,1.3484104,103.68297965,Nanyang Technological University,edu,d0dd1364411a130448517ba532728d5c2fe78ed9,citation,https://doi.org/10.1109/ISCAS.2016.7527183,On-line machine learning accelerator on digital RRAM-crossbar,2016
607,LFW,lfw,40.742252,-74.0270949,Stevens Institute of Technology,edu,8c66378df977606d332fc3b0047989e890a6ac76,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/ext/2B_078_ext.pdf,Hierarchical-PEP model for real-world face recognition,2015
608,LFW,lfw,52.4107358,-4.05295501,Aberystwyth University,edu,9264b390aa00521f9bd01095ba0ba4b42bf84d7e,citation,http://pdfs.semanticscholar.org/9264/b390aa00521f9bd01095ba0ba4b42bf84d7e.pdf,Displacement Template with Divide-&-Conquer Algorithm for Significantly Improving Descriptor Based Face Recognition Approaches,2012
609,LFW,lfw,53.8925662,-122.81471592,University of Northern British Columbia,edu,9264b390aa00521f9bd01095ba0ba4b42bf84d7e,citation,http://pdfs.semanticscholar.org/9264/b390aa00521f9bd01095ba0ba4b42bf84d7e.pdf,Displacement Template with Divide-&-Conquer Algorithm for Significantly Improving Descriptor Based Face Recognition Approaches,2012
610,LFW,lfw,36.20304395,117.05842113,Tianjin University,edu,353b6c1f431feac6edde12b2dde7e6e702455abd,citation,http://pdfs.semanticscholar.org/8835/c80f8ad8ebd05771a9bce5a8637efbc4c8e3.pdf,Multi-scale Patch Based Collaborative Representation for Face Recognition with Margin Distribution Optimization,2012
611,LFW,lfw,22.304572,114.17976285,Hong Kong Polytechnic University,edu,353b6c1f431feac6edde12b2dde7e6e702455abd,citation,http://pdfs.semanticscholar.org/8835/c80f8ad8ebd05771a9bce5a8637efbc4c8e3.pdf,Multi-scale Patch Based Collaborative Representation for Face Recognition with Margin Distribution Optimization,2012
612,LFW,lfw,39.2899685,-76.62196103,University of Maryland,edu,4377b03bbee1f2cf99950019a8d4111f8de9c34a,citation,http://www.umiacs.umd.edu/~morariu/publications/LiSelectiveEncoderICCV15.pdf,Selective Encoding for Recognizing Unreliably Localized Faces,2015
613,LFW,lfw,40.5709358,-105.08655256,Colorado State University,edu,120bcc9879d953de7b2ecfbcd301f72f3a96fb87,citation,http://www.cs.colostate.edu/~vision/pasc/docs/fg2015videoEvalPreprint.pdf,Report on the FG 2015 Video Person Recognition Evaluation,2015
614,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,120bcc9879d953de7b2ecfbcd301f72f3a96fb87,citation,http://www.cs.colostate.edu/~vision/pasc/docs/fg2015videoEvalPreprint.pdf,Report on the FG 2015 Video Person Recognition Evaluation,2015
615,LFW,lfw,39.1254938,-77.22293475,National Institute of Standards and Technology,edu,120bcc9879d953de7b2ecfbcd301f72f3a96fb87,citation,http://www.cs.colostate.edu/~vision/pasc/docs/fg2015videoEvalPreprint.pdf,Report on the FG 2015 Video Person Recognition Evaluation,2015
616,LFW,lfw,40.742252,-74.0270949,Stevens Institute of Technology,edu,120bcc9879d953de7b2ecfbcd301f72f3a96fb87,citation,http://www.cs.colostate.edu/~vision/pasc/docs/fg2015videoEvalPreprint.pdf,Report on the FG 2015 Video Person Recognition Evaluation,2015
617,LFW,lfw,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,120bcc9879d953de7b2ecfbcd301f72f3a96fb87,citation,http://www.cs.colostate.edu/~vision/pasc/docs/fg2015videoEvalPreprint.pdf,Report on the FG 2015 Video Person Recognition Evaluation,2015
618,LFW,lfw,46.0501558,14.46907327,University of Ljubljana,edu,120bcc9879d953de7b2ecfbcd301f72f3a96fb87,citation,http://www.cs.colostate.edu/~vision/pasc/docs/fg2015videoEvalPreprint.pdf,Report on the FG 2015 Video Person Recognition Evaluation,2015
619,LFW,lfw,41.70456775,-86.23822026,University of Notre Dame,edu,120bcc9879d953de7b2ecfbcd301f72f3a96fb87,citation,http://www.cs.colostate.edu/~vision/pasc/docs/fg2015videoEvalPreprint.pdf,Report on the FG 2015 Video Person Recognition Evaluation,2015
620,LFW,lfw,51.24303255,-0.59001382,University of Surrey,edu,120bcc9879d953de7b2ecfbcd301f72f3a96fb87,citation,http://www.cs.colostate.edu/~vision/pasc/docs/fg2015videoEvalPreprint.pdf,Report on the FG 2015 Video Person Recognition Evaluation,2015
621,LFW,lfw,-33.8809651,151.20107299,University of Technology Sydney,edu,120bcc9879d953de7b2ecfbcd301f72f3a96fb87,citation,http://www.cs.colostate.edu/~vision/pasc/docs/fg2015videoEvalPreprint.pdf,Report on the FG 2015 Video Person Recognition Evaluation,2015
622,LFW,lfw,42.3383668,-71.08793524,Northeastern University,edu,e00d4e4ba25fff3583b180db078ef962bf7d6824,citation,http://pdfs.semanticscholar.org/e00d/4e4ba25fff3583b180db078ef962bf7d6824.pdf,Face Verification with Multi-Task and Multi-Scale Features Fusion,2017
623,LFW,lfw,32.77824165,34.99565673,Open University of Israel,edu,5bde1718253ec28a753a892b0ba82d8e553b6bf3,citation,http://pdfs.semanticscholar.org/5bde/1718253ec28a753a892b0ba82d8e553b6bf3.pdf,Variational Relevance Vector Machine for Tabular Data,2010
624,LFW,lfw,55.70229715,37.53179777,Lomonosov Moscow State University,edu,5bde1718253ec28a753a892b0ba82d8e553b6bf3,citation,http://pdfs.semanticscholar.org/5bde/1718253ec28a753a892b0ba82d8e553b6bf3.pdf,Variational Relevance Vector Machine for Tabular Data,2010
625,LFW,lfw,32.1119889,34.80459702,Tel Aviv University,edu,5bde1718253ec28a753a892b0ba82d8e553b6bf3,citation,http://pdfs.semanticscholar.org/5bde/1718253ec28a753a892b0ba82d8e553b6bf3.pdf,Variational Relevance Vector Machine for Tabular Data,2010
626,LFW,lfw,34.68092465,50.05341352,Tafresh University,edu,4349f17ec319ac8b25c14c2ec8c35f374b958066,citation,https://doi.org/10.1109/THMS.2017.2681425,Dynamic Texture Comparison Using Derivative Sparse Representation: Application to Video-Based Face Recognition,2017
627,LFW,lfw,-33.8809651,151.20107299,University of Technology Sydney,edu,4349f17ec319ac8b25c14c2ec8c35f374b958066,citation,https://doi.org/10.1109/THMS.2017.2681425,Dynamic Texture Comparison Using Derivative Sparse Representation: Application to Video-Based Face Recognition,2017
628,LFW,lfw,-31.95040445,115.79790037,University of Western Australia,edu,4349f17ec319ac8b25c14c2ec8c35f374b958066,citation,https://doi.org/10.1109/THMS.2017.2681425,Dynamic Texture Comparison Using Derivative Sparse Representation: Application to Video-Based Face Recognition,2017
629,LFW,lfw,-27.5533975,153.05336234,Griffith University,edu,4349f17ec319ac8b25c14c2ec8c35f374b958066,citation,https://doi.org/10.1109/THMS.2017.2681425,Dynamic Texture Comparison Using Derivative Sparse Representation: Application to Video-Based Face Recognition,2017
630,LFW,lfw,28.54632595,77.27325504,Indian Institute of Technology Delhi,edu,b859d1fc1a7ad756815490527319d458fa9af3d2,citation,https://arxiv.org/pdf/1803.11405.pdf,Learning Structure and Strength of CNN Filters for Small Sample Size Training,2018
631,LFW,lfw,40.00229045,116.32098908,Tsinghua University,edu,122f52fadd4854cf6c9287013520eced3c91e71a,citation,https://doi.org/10.1109/TIP.2016.2515987,Robust Point Set Matching for Partial Face Recognition,2016
632,LFW,lfw,1.3484104,103.68297965,Nanyang Technological University,edu,122f52fadd4854cf6c9287013520eced3c91e71a,citation,https://doi.org/10.1109/TIP.2016.2515987,Robust Point Set Matching for Partial Face Recognition,2016
633,LFW,lfw,32.05765485,118.7550004,HoHai University,edu,771505abd38641454757de75fe751d41e87f89a4,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8401561,Learning structured sparse representation for single sample face recognition,2018
634,LFW,lfw,32.0565957,118.77408833,Nanjing University,edu,771505abd38641454757de75fe751d41e87f89a4,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8401561,Learning structured sparse representation for single sample face recognition,2018
635,LFW,lfw,31.9747463,120.90779264,Nantong University,edu,771505abd38641454757de75fe751d41e87f89a4,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8401561,Learning structured sparse representation for single sample face recognition,2018
636,LFW,lfw,30.2931534,120.1620458,Zhejiang University of Technology,edu,ac48ecbc7c3c1a7eab08820845d47d6ce197707c,citation,https://doi.org/10.1109/TIP.2017.2681841,Iterative Re-Constrained Group Sparse Face Recognition With Adaptive Weights Learning,2017
637,LFW,lfw,42.4505507,-76.4783513,Cornell University,edu,09f58353e48780c707cf24a0074e4d353da18934,citation,http://www.cse.msu.edu/rgroups/biometrics/Publications/Face/BestrowdenBishtKlontzJain_CrowdsourcingHumanPeformance_IJCB2014.pdf,Unconstrained face recognition: Establishing baseline human performance via crowdsourcing,2014
638,LFW,lfw,42.718568,-84.47791571,Michigan State University,edu,09f58353e48780c707cf24a0074e4d353da18934,citation,http://www.cse.msu.edu/rgroups/biometrics/Publications/Face/BestrowdenBishtKlontzJain_CrowdsourcingHumanPeformance_IJCB2014.pdf,Unconstrained face recognition: Establishing baseline human performance via crowdsourcing,2014
639,LFW,lfw,25.0410728,121.6147562,Institute of Information Science,edu,0106a2f6251dc9ffc90709c6f0d9b54c1e82326b,citation,http://www.iis.sinica.edu.tw/papers/song/14922-A.pdf,Applying scattering operators for face recognition: A comparative study,2012
640,LFW,lfw,25.01682835,121.53846924,National Taiwan University,edu,0106a2f6251dc9ffc90709c6f0d9b54c1e82326b,citation,http://www.iis.sinica.edu.tw/papers/song/14922-A.pdf,Applying scattering operators for face recognition: A comparative study,2012
641,LFW,lfw,35.0274996,135.78154513,University of Caen,edu,0ad8149318912b5449085187eb3521786a37bc78,citation,http://arxiv.org/abs/1604.02975,CP-mtML: Coupled Projection Multi-Task Metric Learning for Large Scale Face Retrieval,2016
642,LFW,lfw,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,bc27434e376db89fe0e6ef2d2fabc100d2575ec6,citation,https://arxiv.org/pdf/1607.08438.pdf,Faceless Person Recognition; Privacy Implications in Social Media,2016
643,LFW,lfw,31.2284923,121.40211389,East China Normal University,edu,83295bce2340cb87901499cff492ae6ff3365475,citation,https://arxiv.org/pdf/1808.01558.pdf,Deep Multi-Center Learning for Face Alignment,2018
644,LFW,lfw,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,83295bce2340cb87901499cff492ae6ff3365475,citation,https://arxiv.org/pdf/1808.01558.pdf,Deep Multi-Center Learning for Face Alignment,2018
645,LFW,lfw,45.7413921,126.62552755,Harbin Institute of Technology,edu,5c4f9260762a450892856b189df240f25b5ed333,citation,https://doi.org/10.1109/TIP.2017.2651396,Discriminative Elastic-Net Regularized Linear Regression,2017
646,LFW,lfw,22.53521465,113.9315911,Shenzhen University,edu,5c4f9260762a450892856b189df240f25b5ed333,citation,https://doi.org/10.1109/TIP.2017.2651396,Discriminative Elastic-Net Regularized Linear Regression,2017
647,LFW,lfw,52.6221571,1.2409136,University of East Anglia,edu,5c4f9260762a450892856b189df240f25b5ed333,citation,https://doi.org/10.1109/TIP.2017.2651396,Discriminative Elastic-Net Regularized Linear Regression,2017
648,LFW,lfw,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,57ebeff9273dea933e2a75c306849baf43081a8c,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Sun_Deep_Convolutional_Network_2013_CVPR_paper.pdf,Deep Convolutional Network Cascade for Facial Point Detection,2013
649,LFW,lfw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,57ebeff9273dea933e2a75c306849baf43081a8c,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Sun_Deep_Convolutional_Network_2013_CVPR_paper.pdf,Deep Convolutional Network Cascade for Facial Point Detection,2013
650,LFW,lfw,32.77824165,34.99565673,Open University of Israel,edu,9ce97efc1d520dadaa0d114192ca789f23442727,citation,http://doi.acm.org/10.1145/2597627,Teaching Computer Vision: Bringing Research Benchmarks to the Classroom,2014
651,LFW,lfw,42.9336278,-78.88394479,SUNY Buffalo,edu,4793f11fbca4a7dba898b9fff68f70d868e2497c,citation,http://pdfs.semanticscholar.org/4793/f11fbca4a7dba898b9fff68f70d868e2497c.pdf,Kinship Verification through Transfer Learning,2011
652,LFW,lfw,-33.88890695,151.18943366,University of Sydney,edu,08d55271589f989d90a7edce3345f78f2468a7e0,citation,https://arxiv.org/pdf/1704.03373v1.pdf,Quality Aware Network for Set to Set Recognition,2017
653,LFW,lfw,59.34986645,18.07063213,"KTH Royal Institute of Technology, Stockholm",edu,3c18fb8ff0f5003fefa8e9dc9bebaf88908d255c,citation,https://doi.org/10.1109/ICIP.2014.7025145,Is block matching an alternative tool to LBP for face recognition?,2014
654,LFW,lfw,1.340216,103.965089,Singapore University of Technology and Design,edu,651cafb2620ab60a0e4f550c080231f20ae6d26e,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6360717,4D unconstrained real-time face recognition using a commodity depth camera,2012
655,LFW,lfw,53.21967825,6.56251482,University of Groningen,edu,651cafb2620ab60a0e4f550c080231f20ae6d26e,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6360717,4D unconstrained real-time face recognition using a commodity depth camera,2012
656,LFW,lfw,50.0764296,14.41802312,Czech Technical University,edu,56e25358ebfaf8a8b3c7c33ed007e24f026065d0,citation,https://doi.org/10.1007/s10994-015-5541-9,V-shaped interval insensitive loss for ordinal classification,2015
657,LFW,lfw,32.0565957,118.77408833,Nanjing University,edu,ce37e11f4046a4b766b0e3228870ae4f26dddd67,citation,http://pdfs.semanticscholar.org/ce37/e11f4046a4b766b0e3228870ae4f26dddd67.pdf,Learning One-Shot Exemplar SVM from the Web for Face Verification,2014
658,LFW,lfw,49.10184375,8.4331256,Karlsruhe Institute of Technology,edu,ab0d227b63b702ba80f70fd053175cd1b2fd28cc,citation,https://pdfs.semanticscholar.org/0eed/cda8981740ae2c34ad5809dbdfcd817f2518.pdf,Boosting Pseudo Census Transform Features for Face Alignment,2011
659,LFW,lfw,40.8419836,-73.94368971,Columbia University,edu,4c170a0dcc8de75587dae21ca508dab2f9343974,citation,http://pdfs.semanticscholar.org/73a8/1d311eedac8dea3ca24dc15b6990fa4a725e.pdf,FaceTracer: A Search Engine for Large Collections of Images with Faces,2008
660,LFW,lfw,40.4319722,-86.92389368,Purdue University,edu,65f0b05052c3145a58c2653821e5429ca62555ce,citation,https://arxiv.org/pdf/1810.11580.pdf,Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples,2018
661,LFW,lfw,37.3936717,-122.0807262,Facebook,company,edfce091688bc88389dd4877950bd58e00ff1253,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6553700,A talking profile to distinguish identical twins,2013
662,LFW,lfw,1.2962018,103.77689944,National University of Singapore,edu,edfce091688bc88389dd4877950bd58e00ff1253,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6553700,A talking profile to distinguish identical twins,2013
663,LFW,lfw,51.5247272,-0.03931035,Queen Mary University of London,edu,1e3068886b138304ec5a7296702879cc8788143d,citation,http://doi.org/10.1007/s11263-013-0630-3,Active Rare Class Discovery and Classification Using Dirichlet Processes,2013
664,LFW,lfw,32.77824165,34.99565673,Open University of Israel,edu,566a39d753c494f57b4464d6bde61bf3593f7ceb,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2013.43,A Critical Review of Action Recognition Benchmarks,2013
665,LFW,lfw,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,16fadde3e68bba301f9829b3f99157191106bd0f,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4562953,Utility data annotation with Amazon Mechanical Turk,2008
666,LFW,lfw,42.4505507,-76.4783513,Cornell University,edu,bd379f8e08f88729a9214260e05967f4ca66cd65,citation,https://arxiv.org/pdf/1711.06148.pdf,Learning Compositional Visual Concepts with Mutual Consistency,2017
667,LFW,lfw,37.8687126,-122.25586815,"University of California, Berkeley",edu,2ff9ffedfc59422a8c7dac418a02d1415eec92f1,citation,http://pdfs.semanticscholar.org/6e3b/778ad384101f792284b42844518f620143aa.pdf,Face Verification Using Boosted Cross-Image Features,2013
668,LFW,lfw,28.59899755,-81.19712501,University of Central Florida,edu,2ff9ffedfc59422a8c7dac418a02d1415eec92f1,citation,http://pdfs.semanticscholar.org/6e3b/778ad384101f792284b42844518f620143aa.pdf,Face Verification Using Boosted Cross-Image Features,2013
669,LFW,lfw,42.718568,-84.47791571,Michigan State University,edu,e22adcd2a6a7544f017ec875ce8f89d5c59e09c8,citation,https://arxiv.org/pdf/1807.11936.pdf,Gender Privacy: An Ensemble of Semi Adversarial Networks for Confounding Arbitrary Gender Classifiers,2018
670,LFW,lfw,38.6480445,-90.3099667,Washington University,edu,22e678d3e915218a7c09af0d1602e73080658bb7,citation,http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/IEEE_CVPR_2009_WS/data/papers/04/13.pdf,Adventures in archiving and using three years of webcam images,2009
671,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,c3c463a9ee464bb610423b7203300a83a166b500,citation,https://doi.org/10.1109/ICIP.2014.7025069,Transform-invariant dictionary learning for face recognition,2014
672,LFW,lfw,33.776033,-84.39884086,Georgia Institute of Technology,edu,b75eecc879da38138bf3ace9195ae1613fb6e3cc,citation,https://doi.org/10.1007/s10278-015-9808-2,Improvement in Detection of Wrong-Patient Errors When Radiologists Include Patient Photographs in Their Interpretation of Portable Chest Radiographs,2015
673,LFW,lfw,-27.49741805,153.01316956,University of Queensland,edu,2af19b5ff2ca428fa42ef4b85ddbb576b5d9a5cc,citation,http://pdfs.semanticscholar.org/2af1/9b5ff2ca428fa42ef4b85ddbb576b5d9a5cc.pdf,Multi-Region Probabilistic Histograms for Robust and Scalable Identity Inference,2009
674,LFW,lfw,49.10184375,8.4331256,Karlsruhe Institute of Technology,edu,919bdc161485615d5ee571b1585c1eb0539822c8,citation,http://ieeexplore.ieee.org/document/6460332/,A ranking model for face alignment with Pseudo Census Transform,2012
675,LFW,lfw,40.00229045,116.32098908,Tsinghua University,edu,a3a2f3803bf403262b56ce88d130af15e984fff0,citation,http://pdfs.semanticscholar.org/e538/e1f6557d2920b449249606f909b665fbb924.pdf,Building a Compact Relevant Sample Coverage for Relevance Feedback in Content-Based Image Retrieval,2008
676,LFW,lfw,24.96841805,121.19139696,National Central University,edu,a192845a7695bdb372cccf008e6590a14ed82761,citation,https://doi.org/10.1109/TIP.2014.2321495,A Novel Local Pattern Descriptor—Local Vector Pattern in High-Order Derivative Space for Face Recognition,2014
677,LFW,lfw,22.1240187,113.54510901,University of Macau,edu,8db9188e5137e167bffb3ee974732c1fe5f7a7dc,citation,https://doi.org/10.1109/TIP.2016.2612885,Tree-Structured Nuclear Norm Approximation With Applications to Robust Face Recognition,2016
678,LFW,lfw,32.0565957,118.77408833,Nanjing University,edu,8db9188e5137e167bffb3ee974732c1fe5f7a7dc,citation,https://doi.org/10.1109/TIP.2016.2612885,Tree-Structured Nuclear Norm Approximation With Applications to Robust Face Recognition,2016
679,LFW,lfw,40.00229045,116.32098908,Tsinghua University,edu,21e158bcda4e10da88ee8da3799a6144b60d791f,citation,https://pdfs.semanticscholar.org/21e1/58bcda4e10da88ee8da3799a6144b60d791f.pdf,Population Matching Discrepancy and Applications in Deep Learning,2017
680,LFW,lfw,37.43131385,-122.16936535,Stanford University,edu,2b7ef95822a4d577021df16607bf7b4a4514eb4b,citation,http://pdfs.semanticscholar.org/b596/9178f843bfaecd0026d04c41e79bcb9edab5.pdf,Emergence of Object-Selective Features in Unsupervised Feature Learning,2012
681,LFW,lfw,41.3868913,2.16352385,University of Barcelona,edu,3fe4109ded039ac9d58eb9f5baa5327af30ad8b6,citation,http://www.cvc.uab.cat/~ahernandez/files/CVPR2010STGRABCUT.pdf,Spatio-Temporal GrabCut human segmentation for face and pose recovery,2010
682,LFW,lfw,1.3484104,103.68297965,Nanyang Technological University,edu,28be652db01273289499bc6e56379ca0237506c0,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/3B_018_ext.pdf,FaLRR: A fast low rank representation solver,2015
683,LFW,lfw,50.8142701,8.771435,Philipps-Universität Marburg,edu,5981c309bd0ffd849c51b1d8a2ccc481a8ec2f5c,citation,https://doi.org/10.1109/ICT.2017.7998256,SmartFace: Efficient face detection on smartphones for wireless on-demand emergency networks,2017
684,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,1a40c2a2d17c52c8b9d20648647d0886e30a60fa,citation,https://doi.org/10.1109/ICPR.2016.7900283,Hybrid hypergraph construction for facial expression recognition,2016
685,LFW,lfw,24.8186587,67.0316585,Shaheed Zulfikar Ali Bhutto Institute of Science and Technology,edu,70580ed8bc482cad66e059e838e4a779081d1648,citation,http://pdfs.semanticscholar.org/7058/0ed8bc482cad66e059e838e4a779081d1648.pdf,Gender Classification using Multi-Level Wavelets on Real World Face Images,2013
686,LFW,lfw,37.4102193,-122.05965487,Carnegie Mellon University,edu,9fc04a13eef99851136eadff52e98eb9caac919d,citation,http://pdfs.semanticscholar.org/9fc0/4a13eef99851136eadff52e98eb9caac919d.pdf,Rethinking the Camera Pipeline for Computer Vision,2017
687,LFW,lfw,42.4505507,-76.4783513,Cornell University,edu,9fc04a13eef99851136eadff52e98eb9caac919d,citation,http://pdfs.semanticscholar.org/9fc0/4a13eef99851136eadff52e98eb9caac919d.pdf,Rethinking the Camera Pipeline for Computer Vision,2017
688,LFW,lfw,40.51865195,-74.44099801,State University of New Jersey,edu,0ca66283f4fb7dbc682f789fcf6d6732006befd5,citation,http://pdfs.semanticscholar.org/0ca6/6283f4fb7dbc682f789fcf6d6732006befd5.pdf,Active Dictionary Learning for Image Representation,2015
689,LFW,lfw,65.0592157,25.46632601,University of Oulu,edu,761304bbd259a9e419a2518193e1ff1face9fd2d,citation,https://doi.org/10.1007/978-3-642-33885-4_57,Robust and Computationally Efficient Face Detection Using Gaussian Derivative Features of Higher Orders,2012
690,LFW,lfw,13.0222347,77.56718325,Indian Institute of Science Bangalore,edu,f5af3c28b290dc797c499283e2d0662570f9ed02,citation,https://pdfs.semanticscholar.org/f5af/3c28b290dc797c499283e2d0662570f9ed02.pdf,GenLR-Net : Deep framework for very low resolution face and object recognition with generalization to unseen categories,2018
691,LFW,lfw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,a0d6390dd28d802152f207940c7716fe5fae8760,citation,http://pdfs.semanticscholar.org/a0d6/390dd28d802152f207940c7716fe5fae8760.pdf,Bayesian Face Revisited: A Joint Formulation,2012
692,LFW,lfw,31.83907195,117.26420748,University of Science and Technology of China,edu,a0d6390dd28d802152f207940c7716fe5fae8760,citation,http://pdfs.semanticscholar.org/a0d6/390dd28d802152f207940c7716fe5fae8760.pdf,Bayesian Face Revisited: A Joint Formulation,2012
693,LFW,lfw,45.7833244,4.8781984,University of Lyon,edu,54ba18952fe36c9be9f2ab11faecd43d123b389b,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7163085,Triangular similarity metric learning for face verification,2015
694,LFW,lfw,-27.5953995,-48.6154218,University of Campinas,edu,a2bd81be79edfa8dcfde79173b0a895682d62329,citation,http://pdfs.semanticscholar.org/a2bd/81be79edfa8dcfde79173b0a895682d62329.pdf,Multi-Objective Vehicle Routing Problem Applied to Large Scale Post Office Deliveries,2017
695,LFW,lfw,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,4cfd770ccecae1c0b4248bc800d7fd35c817bbbd,citation,https://pdfs.semanticscholar.org/8774/e206564df3bf9050f8c2be6b434cc2469c5b.pdf,A Discriminative Feature Learning Approach for Deep Face Recognition,2016
696,LFW,lfw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,4cfd770ccecae1c0b4248bc800d7fd35c817bbbd,citation,https://pdfs.semanticscholar.org/8774/e206564df3bf9050f8c2be6b434cc2469c5b.pdf,A Discriminative Feature Learning Approach for Deep Face Recognition,2016
697,LFW,lfw,43.08250655,-77.67121663,Rochester Institute of Technology,edu,69b2a7533e38c2c8c9a0891a728abb423ad2c7e7,citation,https://doi.org/10.1016/j.imavis.2013.03.003,Manifold based sparse representation for facial understanding in natural images,2013
698,LFW,lfw,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,0df0d1adea39a5bef318b74faa37de7f3e00b452,citation,https://scalable.mpi-inf.mpg.de/files/2015/09/zhang_CVPR15.pdf,Appearance-based gaze estimation in the wild,2015
699,LFW,lfw,59.34986645,18.07063213,"KTH Royal Institute of Technology, Stockholm",edu,633101e794d7b80f55f466fd2941ea24595e10e6,citation,https://pdfs.semanticscholar.org/6331/01e794d7b80f55f466fd2941ea24595e10e6.pdf,Face Attribute Prediction with classification CNN,2016
700,LFW,lfw,39.1254938,-77.22293475,National Institute of Standards and Technology,edu,089b5e8eb549723020b908e8eb19479ba39812f5,citation,http://www.face-recognition-challenge.com/RobustnessOfDCNN-preprint.pdf,A Cross Benchmark Assessment of a Deep Convolutional Neural Network for Face Recognition,2017
701,LFW,lfw,-27.49741805,153.01316956,University of Queensland,edu,f27fd2a1bc229c773238f1912db94991b8bf389a,citation,https://doi.org/10.1109/IVCNZ.2016.7804414,How do you develop a face detector for the unconstrained environment?,2016
702,LFW,lfw,40.47913175,-74.43168868,Rutgers University,edu,afdf9a3464c3b015f040982750f6b41c048706f5,citation,https://arxiv.org/pdf/1608.05477.pdf,A Recurrent Encoder-Decoder Network for Sequential Face Alignment,2016
703,LFW,lfw,40.00229045,116.32098908,Tsinghua University,edu,a52a69bf304d49fba6eac6a73c5169834c77042d,citation,https://doi.org/10.1109/LSP.2017.2789251,Margin Loss: Making Faces More Separable,2018
704,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,bc910ca355277359130da841a589a36446616262,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Huang_Conditional_High-Order_Boltzmann_ICCV_2015_paper.pdf,Conditional High-Order Boltzmann Machine: A Supervised Learning Model for Relation Learning,2015
705,LFW,lfw,40.00229045,116.32098908,Tsinghua University,edu,93eb3963bc20e28af26c53ef3bce1e76b15e3209,citation,https://doi.org/10.1109/ICIP.2017.8296992,Occlusion robust face recognition based on mask learning,2017
706,LFW,lfw,51.49887085,-0.17560797,Imperial College London,edu,a06b6d30e2b31dc600f622ab15afe5e2929581a7,citation,https://ibug.doc.ic.ac.uk/media/uploads/documents/2209.pdf,Robust Joint and Individual Variance Explained,2017
707,LFW,lfw,51.59029705,-0.22963221,Middlesex University,edu,a06b6d30e2b31dc600f622ab15afe5e2929581a7,citation,https://ibug.doc.ic.ac.uk/media/uploads/documents/2209.pdf,Robust Joint and Individual Variance Explained,2017
708,LFW,lfw,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,565f7c767e6b150ebda491e04e6b1de759fda2d4,citation,https://doi.org/10.1016/j.patcog.2016.11.023,"Fine-grained face verification: FGLFW database, baselines, and human-DCMN partnership",2017
709,LFW,lfw,40.0141905,-83.0309143,University of Electronic Science and Technology of China,edu,16b9d258547f1eccdb32111c9f45e2e4bbee79af,citation,https://arxiv.org/pdf/1704.06369.pdf,NormFace: L2 Hypersphere Embedding for Face Verification,2017
710,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,a73405038fdc0d8bf986539ef755a80ebd341e97,citation,https://doi.org/10.1109/TIP.2017.2698918,Conditional High-Order Boltzmann Machines for Supervised Relation Learning,2017
711,LFW,lfw,-32.00686365,115.89691775,Curtin University,edu,e9a5a38e7da3f0aa5d21499149536199f2e0e1f7,citation,https://pdfs.semanticscholar.org/e9a5/a38e7da3f0aa5d21499149536199f2e0e1f7.pdf,A Bayesian Scene-Prior-Based Deep Network Model for Face Verification,2018
712,LFW,lfw,65.0592157,25.46632601,University of Oulu,edu,1fe121925668743762ce9f6e157081e087171f4c,citation,https://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W02/papers/Ylioinas_Unsupervised_Learning_of_2015_CVPR_paper.pdf,Unsupervised learning of overcomplete face descriptors,2015
713,LFW,lfw,-35.23656905,149.08446994,University of Canberra,edu,6f2b36cadf3dd1648b709e9b4f4c19ffa1939ed1,citation,https://arxiv.org/pdf/1901.07590.pdf,Striking the Right Balance with Uncertainty,2019
714,LFW,lfw,-35.2776999,149.118527,Australian National University,edu,84c7d3b1d407e0d435a08574a3f82ecacf7841b6,citation,https://arxiv.org/pdf/1901.07711.pdf,Max-margin Class Imbalanced Learning with Gaussian Affinity,2019
715,LFW,lfw,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,0be418e63d111e3b94813875f75909e4dc27d13a,citation,https://doi.org/10.1109/ICB.2016.7550057,Fine-grained LFW database,2016
716,LFW,lfw,-27.49741805,153.01316956,University of Queensland,edu,3c563542db664321aa77a9567c1601f425500f94,citation,https://arxiv.org/pdf/1712.02514.pdf,TV-GAN: Generative Adversarial Network Based Thermal to Visible Face Recognition,2018
717,LFW,lfw,36.20304395,117.05842113,Tianjin University,edu,1bf570bd40b3adced1e47dbcceffe50573f81845,citation,https://arxiv.org/pdf/1803.02504.pdf,Exponential Discriminative Metric Embedding in Deep Learning,2018
718,LFW,lfw,25.01682835,121.53846924,National Taiwan University,edu,81884e1de00e59f24bc20254584d73a1a1806933,citation,https://arxiv.org/pdf/1811.02328.pdf,Super-Identity Convolutional Neural Network for Face Hallucination,2018
719,LFW,lfw,39.993008,116.329882,SenseTime,company,81884e1de00e59f24bc20254584d73a1a1806933,citation,https://arxiv.org/pdf/1811.02328.pdf,Super-Identity Convolutional Neural Network for Face Hallucination,2018
720,LFW,lfw,30.284151,-97.73195598,University of Texas at Austin,edu,81884e1de00e59f24bc20254584d73a1a1806933,citation,https://arxiv.org/pdf/1811.02328.pdf,Super-Identity Convolutional Neural Network for Face Hallucination,2018
721,LFW,lfw,32.77824165,34.99565673,Open University of Israel,edu,1e6ed6ca8209340573a5e907a6e2e546a3bf2d28,citation,http://arxiv.org/pdf/1607.01450v1.pdf,Pooling Faces: Template Based Face Recognition with Pooled Face Images,2016
722,LFW,lfw,37.5600406,126.9369248,Yonsei University,edu,06c956d4aac65752672ce4bd5a379f10a7fd6148,citation,https://doi.org/10.1109/LSP.2017.2749763,Stacking PCANet +: An Overly Simplified ConvNets Baseline for Face Recognition,2017
723,LFW,lfw,52.2380139,6.8566761,University of Twente,edu,740e095a65524d569244947f6eea3aefa3cca526,citation,http://pdfs.semanticscholar.org/740e/095a65524d569244947f6eea3aefa3cca526.pdf,Towards Human-like Performance Face Detection: A Convolutional Neural Network Approach,2016
724,LFW,lfw,32.1119889,34.80459702,Tel Aviv University,edu,2f16baddac6af536451b3216b02d3480fc361ef4,citation,http://cs.nyu.edu/~fergus/teaching/vision/10_facerec.pdf,Web-scale training for face identification,2015
725,LFW,lfw,42.4505507,-76.4783513,Cornell University,edu,32d8e555441c47fc27249940991f80502cb70bd5,citation,https://arxiv.org/pdf/1709.07886v1.pdf,Machine Learning Models that Remember Too Much,2017
726,LFW,lfw,32.1119889,34.80459702,Tel Aviv University,edu,7859667ed6c05a467dfc8a322ecd0f5e2337db56,citation,http://pdfs.semanticscholar.org/7859/667ed6c05a467dfc8a322ecd0f5e2337db56.pdf,Web-Scale Transfer Learning for Unconstrained 1:N Face Identification,2015
727,LFW,lfw,29.58333105,-98.61944505,University of Texas at San Antonio,edu,7788fa76f1488b1597ee2bebc462f628e659f61e,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8063888,A Privacy-Aware Architecture at the Edge for Autonomous Real-Time Identity Reidentification in Crowds,2018
728,LFW,lfw,40.8419836,-73.94368971,Columbia University,edu,f467eb5e7f9d49ab318401f961109882c00f2720,citation,http://www.nsl.cs.columbia.edu/papers/2015/faceoff.ccs15.pdf,Face/Off: Preventing Privacy Leakage From Photos in Social Networks,2015
729,LFW,lfw,40.2773077,-7.5095801,University of Beira Interior,edu,84ae55603bffda40c225fe93029d39f04793e01f,citation,https://doi.org/10.1109/ICB.2016.7550066,ICB-RW 2016: International challenge on biometric recognition in the wild,2016
730,LFW,lfw,-33.8809651,151.20107299,University of Technology Sydney,edu,3b64efa817fd609d525c7244a0e00f98feacc8b4,citation,http://doi.acm.org/10.1145/2845089,A Comprehensive Survey on Pose-Invariant Face Recognition,2016
731,LFW,lfw,32.0575279,118.78682252,Southeast University,edu,b2e308649c7a502456a8e3c95ac7fbe6f8216e51,citation,http://pdfs.semanticscholar.org/b2e3/08649c7a502456a8e3c95ac7fbe6f8216e51.pdf,Recurrent Regression for Face Recognition,2016
732,LFW,lfw,3.12267405,101.65356103,University of Malaya,edu,5e48958c1c9ab9ccb5c9e1a62b81532700d38d83,citation,https://arxiv.org/pdf/1702.03410.pdf,ArtGAN: Artwork synthesis with conditional categorical GANs,2017
733,LFW,lfw,39.47787665,-0.34257711,Universitat de València,edu,5922e26c9eaaee92d1d70eae36275bb226ecdb2e,citation,http://pdfs.semanticscholar.org/5922/e26c9eaaee92d1d70eae36275bb226ecdb2e.pdf,Boosting Classification Based Similarity Learning by using Standard Distances,2015
734,LFW,lfw,42.4505507,-76.4783513,Cornell University,edu,345cc31c85e19cea9f8b8521be6a37937efd41c2,citation,https://arxiv.org/pdf/1511.06421.pdf,Deep Manifold Traversal: Changing Labels with Convolutional Features,2015
735,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,72a7eb68f0955564e1ceafa75aeeb6b5bbb14e7e,citation,https://pdfs.semanticscholar.org/72a7/eb68f0955564e1ceafa75aeeb6b5bbb14e7e.pdf,Face Recognition with Contrastive Convolution,2018
736,LFW,lfw,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,72a7eb68f0955564e1ceafa75aeeb6b5bbb14e7e,citation,https://pdfs.semanticscholar.org/72a7/eb68f0955564e1ceafa75aeeb6b5bbb14e7e.pdf,Face Recognition with Contrastive Convolution,2018
737,LFW,lfw,31.83907195,117.26420748,University of Science and Technology of China,edu,47cd161546c59ab1e05f8841b82e985f72e5ddcb,citation,https://doi.org/10.1109/ICIP.2017.8296552,Gender classification in live videos,2017
738,LFW,lfw,37.4102193,-122.05965487,Carnegie Mellon University,edu,bd8f77b7d3b9d272f7a68defc1412f73e5ac3135,citation,https://arxiv.org/pdf/1704.08063.pdf,SphereFace: Deep Hypersphere Embedding for Face Recognition,2017
739,LFW,lfw,33.776033,-84.39884086,Georgia Institute of Technology,edu,bd8f77b7d3b9d272f7a68defc1412f73e5ac3135,citation,https://arxiv.org/pdf/1704.08063.pdf,SphereFace: Deep Hypersphere Embedding for Face Recognition,2017
740,LFW,lfw,23.09461185,113.28788994,Sun Yat-Sen University,edu,bd8f77b7d3b9d272f7a68defc1412f73e5ac3135,citation,https://arxiv.org/pdf/1704.08063.pdf,SphereFace: Deep Hypersphere Embedding for Face Recognition,2017
741,LFW,lfw,42.2942142,-83.71003894,University of Michigan,edu,9ace71283834d4dd72509db6f1b859536f801d1c,citation,https://arxiv.org/pdf/1701.00299.pdf,Dynamic Deep Neural Networks: Optimizing Accuracy-Efficiency Trade-Offs by Selective Execution,2018
742,LFW,lfw,22.2081469,114.25964115,University of Hong Kong,edu,e1630014a5ae3d2fb7ff6618f1470a567f4d90f5,citation,https://arxiv.org/pdf/1602.04364.pdf,"Look, Listen and Learn - A Multimodal LSTM for Speaker Identification",2016
743,LFW,lfw,24.18005755,120.64836072,Feng Chia University,edu,344a5802999dddd0a6d1c4d511910af2eb922231,citation,http://pdfs.semanticscholar.org/f0ba/552418698d1b881c6f9f02e2c84f969e66f3.pdf,DroneFace: An Open Dataset for Drone Research,2017
744,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,600f164c81dbaa0327e7bd659fd9eb7f511f9e9a,citation,https://doi.org/10.1109/BTAS.2014.6996301,A benchmark study of large-scale unconstrained face recognition,2014
745,LFW,lfw,36.20304395,117.05842113,Tianjin University,edu,5180df9d5eb26283fb737f491623395304d57497,citation,https://arxiv.org/pdf/1804.10899.pdf,Scalable Angular Discriminative Deep Metric Learning for Face Recognition,2018
746,LFW,lfw,32.0565957,118.77408833,Nanjing University,edu,55089f9bc858ae7e9addf30502ac11be4347c05a,citation,https://pdfs.semanticscholar.org/5508/9f9bc858ae7e9addf30502ac11be4347c05a.pdf,A Privacy-Preserving Deep Learning Approach for Face Recognition with Edge Computing,2018
747,LFW,lfw,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,37af037923d0d7a4480a9c1f2e7d002f122bfebb,citation,https://arxiv.org/pdf/1706.04717.pdf,Recent Progress of Face Image Synthesis,2017