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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 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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 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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 . 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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 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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 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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 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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 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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 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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 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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 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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 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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, 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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 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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. 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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 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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 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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 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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 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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 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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 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