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 id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year
 0,,Helen,helen,0.0,0.0,,,,main,,Interactive Facial Feature Localization,2012
+1,United States,Helen,helen,40.4441619,-79.94272826,Carnegie Mellon University,edu,bae86526b3b0197210b64cdd95cb5aca4209c98a,citation,https://arxiv.org/pdf/1802.01777.pdf,"Brute-Force Facial Landmark Analysis With a 140, 000-Way Classifier",2018
+2,China,Helen,helen,28.2290209,112.99483204,"National University of Defense Technology, China",mil,1b8541ec28564db66a08185510c8b300fa4dc793,citation,,Affine-Transformation Parameters Regression for Face Alignment,2016
+3,China,Helen,helen,31.83907195,117.26420748,University of Science and Technology of China,edu,084bd02d171e36458f108f07265386f22b34a1ae,citation,http://7xrqgw.com1.z0.glb.clouddn.com/3000fps.pdf,Face Alignment at 3000 FPS via Regressing Local Binary Features,2014
+4,United States,Helen,helen,47.6423318,-122.1369302,Microsoft,company,084bd02d171e36458f108f07265386f22b34a1ae,citation,http://7xrqgw.com1.z0.glb.clouddn.com/3000fps.pdf,Face Alignment at 3000 FPS via Regressing Local Binary Features,2014
+5,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,5bd3d08335bb4e444a86200c5e9f57fd9d719e14,citation,https://pdfs.semanticscholar.org/5bd3/d08335bb4e444a86200c5e9f57fd9d719e14.pdf,3 D Face Morphable Models “ Inthe-Wild ”,0
+6,United States,Helen,helen,38.7768106,-94.9442982,Amazon,company,5bd3d08335bb4e444a86200c5e9f57fd9d719e14,citation,https://pdfs.semanticscholar.org/5bd3/d08335bb4e444a86200c5e9f57fd9d719e14.pdf,3 D Face Morphable Models “ Inthe-Wild ”,0
+7,Finland,Helen,helen,65.0592157,25.46632601,University of Oulu,edu,5bd3d08335bb4e444a86200c5e9f57fd9d719e14,citation,https://pdfs.semanticscholar.org/5bd3/d08335bb4e444a86200c5e9f57fd9d719e14.pdf,3 D Face Morphable Models “ Inthe-Wild ”,0
+8,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,12095f9b35ee88272dd5abc2d942a4f55804b31e,citation,https://pdfs.semanticscholar.org/1209/5f9b35ee88272dd5abc2d942a4f55804b31e.pdf,DenseReg : Fully Convolutional Dense Shape Regression Inthe-Wild Rıza,0
+9,United States,Helen,helen,38.7768106,-94.9442982,Amazon,company,12095f9b35ee88272dd5abc2d942a4f55804b31e,citation,https://pdfs.semanticscholar.org/1209/5f9b35ee88272dd5abc2d942a4f55804b31e.pdf,DenseReg : Fully Convolutional Dense Shape Regression Inthe-Wild Rıza,0
+10,United Kingdom,Helen,helen,51.5231607,-0.1282037,University College London,edu,12095f9b35ee88272dd5abc2d942a4f55804b31e,citation,https://pdfs.semanticscholar.org/1209/5f9b35ee88272dd5abc2d942a4f55804b31e.pdf,DenseReg : Fully Convolutional Dense Shape Regression Inthe-Wild Rıza,0
+11,United Kingdom,Helen,helen,51.24303255,-0.59001382,University of Surrey,edu,2d2e1d1f50645fe20c051339e9a0fca7b176422a,citation,https://arxiv.org/pdf/1803.05536.pdf,Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild,2018
+12,United Kingdom,Helen,helen,56.1454119,-3.9205713,University of Stirling,edu,2d2e1d1f50645fe20c051339e9a0fca7b176422a,citation,https://arxiv.org/pdf/1803.05536.pdf,Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild,2018
+13,China,Helen,helen,31.4854255,120.2739581,Jiangnan University,edu,2d2e1d1f50645fe20c051339e9a0fca7b176422a,citation,https://arxiv.org/pdf/1803.05536.pdf,Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild,2018
+14,China,Helen,helen,30.642769,104.06751175,"Sichuan University, Chengdu",edu,2d2e1d1f50645fe20c051339e9a0fca7b176422a,citation,https://arxiv.org/pdf/1803.05536.pdf,Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild,2018
+15,Germany,Helen,helen,48.48187645,9.18682404,Reutlingen University,edu,2d2e1d1f50645fe20c051339e9a0fca7b176422a,citation,https://arxiv.org/pdf/1803.05536.pdf,Evaluation of Dense 3D Reconstruction from 2D Face Images in the Wild,2018
+16,United States,Helen,helen,45.57022705,-122.63709346,Concordia University,edu,266ed43dcea2e7db9f968b164ca08897539ca8dd,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/3B_037.pdf,Beyond Principal Components: Deep Boltzmann Machines for face modeling,2015
+17,United States,Helen,helen,40.4441619,-79.94272826,Carnegie Mellon University,edu,266ed43dcea2e7db9f968b164ca08897539ca8dd,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/3B_037.pdf,Beyond Principal Components: Deep Boltzmann Machines for face modeling,2015
+18,Germany,Helen,helen,52.5098686,13.3984513,"Amazon Research, Berlin",company,ba1c0600d3bdb8ed9d439e8aa736a96214156284,citation,http://www.eurasip.org/Proceedings/Eusipco/Eusipco2017/papers/1570347043.pdf,Complex representations for learning statistical shape priors,2017
+19,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,ba1c0600d3bdb8ed9d439e8aa736a96214156284,citation,http://www.eurasip.org/Proceedings/Eusipco/Eusipco2017/papers/1570347043.pdf,Complex representations for learning statistical shape priors,2017
+20,United States,Helen,helen,40.47913175,-74.43168868,Rutgers University,edu,3b470b76045745c0ef5321e0f1e0e6a4b1821339,citation,https://pdfs.semanticscholar.org/8e72/fa02f2d90ba31f31e0a7aa96a6d3e10a66fc.pdf,Consensus of Regression for Occlusion-Robust Facial Feature Localization,2014
+21,United States,Helen,helen,37.3309307,-121.8940485,"Adobe Research, San Jose, CA",company,3b470b76045745c0ef5321e0f1e0e6a4b1821339,citation,https://pdfs.semanticscholar.org/8e72/fa02f2d90ba31f31e0a7aa96a6d3e10a66fc.pdf,Consensus of Regression for Occlusion-Robust Facial Feature Localization,2014
+22,Spain,Helen,helen,41.40657415,2.1945341,Universitat Oberta de Catalunya,edu,cc4fc9a309f300e711e09712701b1509045a8e04,citation,https://pdfs.semanticscholar.org/cea6/9010a2f75f7a057d56770e776dec206ed705.pdf,Continuous Supervised Descent Method for Facial Landmark Localisation,2016
+23,Spain,Helen,helen,41.386608,2.16402,Universitat de Barcelona,edu,cc4fc9a309f300e711e09712701b1509045a8e04,citation,https://pdfs.semanticscholar.org/cea6/9010a2f75f7a057d56770e776dec206ed705.pdf,Continuous Supervised Descent Method for Facial Landmark Localisation,2016
+24,Thailand,Helen,helen,13.65450525,100.49423171,Robotics Institute,edu,cc4fc9a309f300e711e09712701b1509045a8e04,citation,https://pdfs.semanticscholar.org/cea6/9010a2f75f7a057d56770e776dec206ed705.pdf,Continuous Supervised Descent Method for Facial Landmark Localisation,2016
+25,United States,Helen,helen,40.44415295,-79.96243993,University of Pittsburgh,edu,cc4fc9a309f300e711e09712701b1509045a8e04,citation,https://pdfs.semanticscholar.org/cea6/9010a2f75f7a057d56770e776dec206ed705.pdf,Continuous Supervised Descent Method for Facial Landmark Localisation,2016
+26,Canada,Helen,helen,43.0095971,-81.2737336,University of Western Ontario,edu,f7ae38a073be7c9cd1b92359131b9c8374579b13,citation,http://www.digitalimaginggroup.ca/members/Shuo/07487053.pdf,Descriptor Learning via Supervised Manifold Regularization for Multioutput Regression,2017
+27,Canada,Helen,helen,42.960348,-81.226628,"London Healthcare Sciences Centre, Ontario, Canada",edu,f7ae38a073be7c9cd1b92359131b9c8374579b13,citation,http://www.digitalimaginggroup.ca/members/Shuo/07487053.pdf,Descriptor Learning via Supervised Manifold Regularization for Multioutput Regression,2017
+28,United Kingdom,Helen,helen,55.0030632,-1.57463231,Northumbria University,edu,f7ae38a073be7c9cd1b92359131b9c8374579b13,citation,http://www.digitalimaginggroup.ca/members/Shuo/07487053.pdf,Descriptor Learning via Supervised Manifold Regularization for Multioutput Regression,2017
+29,Canada,Helen,helen,43.0012953,-81.2550455,"St. Joseph's Health Care, Ontario, Canada",edu,f7ae38a073be7c9cd1b92359131b9c8374579b13,citation,http://www.digitalimaginggroup.ca/members/Shuo/07487053.pdf,Descriptor Learning via Supervised Manifold Regularization for Multioutput Regression,2017
+30,China,Helen,helen,40.0044795,116.370238,Chinese Academy of Sciences,edu,2a4153655ad1169d482e22c468d67f3bc2c49f12,citation,http://cseweb.ucsd.edu/~mkchandraker/classes/CSE291/Winter2018/Lectures/FaceAlignment.pdf,Face Alignment Across Large Poses: A 3D Solution,2016
+31,United States,Helen,helen,42.718568,-84.47791571,Michigan State University,edu,2a4153655ad1169d482e22c468d67f3bc2c49f12,citation,http://cseweb.ucsd.edu/~mkchandraker/classes/CSE291/Winter2018/Lectures/FaceAlignment.pdf,Face Alignment Across Large Poses: A 3D Solution,2016
+32,China,Helen,helen,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,655ad6ed99277b3bba1f2ea7e5da4709d6e6cf44,citation,https://arxiv.org/pdf/1803.06598.pdf,Facial Landmarks Detection by Self-Iterative Regression Based Landmarks-Attention Network,2018
+33,United States,Helen,helen,42.3614256,-71.0812092,Microsoft Research Asia,company,655ad6ed99277b3bba1f2ea7e5da4709d6e6cf44,citation,https://arxiv.org/pdf/1803.06598.pdf,Facial Landmarks Detection by Self-Iterative Regression Based Landmarks-Attention Network,2018
+34,United Kingdom,Helen,helen,53.22853665,-0.54873472,University of Lincoln,edu,232b6e2391c064d483546b9ee3aafe0ba48ca519,citation,http://doc.utwente.nl/89696/1/Pantic_Optimization_problems_for_fast_AAM_fitting.pdf,Optimization Problems for Fast AAM Fitting in-the-Wild,2013
+35,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,232b6e2391c064d483546b9ee3aafe0ba48ca519,citation,http://doc.utwente.nl/89696/1/Pantic_Optimization_problems_for_fast_AAM_fitting.pdf,Optimization Problems for Fast AAM Fitting in-the-Wild,2013
+36,United Kingdom,Helen,helen,52.9387428,-1.20029569,University of Nottingham,edu,75fd9acf5e5b7ed17c658cc84090c4659e5de01d,citation,http://eprints.nottingham.ac.uk/31442/1/tzimiro_CVPR15.pdf,Project-Out Cascaded Regression with an application to face alignment,2015
+37,Denmark,Helen,helen,57.01590275,9.97532827,Aalborg University,edu,087002ab569e35432cdeb8e63b2c94f1abc53ea9,citation,http://openaccess.thecvf.com/content_cvpr_workshops_2015/W09/papers/Irani_Spatiotemporal_Analysis_of_2015_CVPR_paper.pdf,Spatiotemporal analysis of RGB-D-T facial images for multimodal pain level recognition,2015
+38,Spain,Helen,helen,41.5008957,2.111553,"Computer Vision Center, UAB, Barcelona, Spain",edu,087002ab569e35432cdeb8e63b2c94f1abc53ea9,citation,http://openaccess.thecvf.com/content_cvpr_workshops_2015/W09/papers/Irani_Spatiotemporal_Analysis_of_2015_CVPR_paper.pdf,Spatiotemporal analysis of RGB-D-T facial images for multimodal pain level recognition,2015
+39,China,Helen,helen,39.9041999,116.4073963,Key Lab of Intelligent Information Processing of Chinese Academy of Sciences,edu,090ff8f992dc71a1125636c1adffc0634155b450,citation,https://pdfs.semanticscholar.org/090f/f8f992dc71a1125636c1adffc0634155b450.pdf,Topic-Aware Deep Auto-Encoders (TDA) for Face Alignment,2014
+40,China,Helen,helen,40.0044795,116.370238,Chinese Academy of Sciences,edu,090ff8f992dc71a1125636c1adffc0634155b450,citation,https://pdfs.semanticscholar.org/090f/f8f992dc71a1125636c1adffc0634155b450.pdf,Topic-Aware Deep Auto-Encoders (TDA) for Face Alignment,2014
+41,China,Helen,helen,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,090ff8f992dc71a1125636c1adffc0634155b450,citation,https://pdfs.semanticscholar.org/090f/f8f992dc71a1125636c1adffc0634155b450.pdf,Topic-Aware Deep Auto-Encoders (TDA) for Face Alignment,2014
+42,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,090ff8f992dc71a1125636c1adffc0634155b450,citation,https://pdfs.semanticscholar.org/090f/f8f992dc71a1125636c1adffc0634155b450.pdf,Topic-Aware Deep Auto-Encoders (TDA) for Face Alignment,2014
+43,Israel,Helen,helen,32.77824165,34.99565673,Open University of Israel,edu,62e913431bcef5983955e9ca160b91bb19d9de42,citation,https://arxiv.org/pdf/1511.04031.pdf,Facial Landmark Detection with Tweaked Convolutional Neural Networks,2018
+44,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,034b3f3bac663fb814336a69a9fd3514ca0082b9,citation,http://ibug.doc.ic.ac.uk/media/uploads/documents/alabort_cvpr2015.pdf,Unifying holistic and Parts-Based Deformable Model fitting,2015
+45,China,Helen,helen,39.9808333,116.34101249,Beihang University,edu,86b6afc667bb14ff4d69e7a5e8bb2454a6bbd2cd,citation,https://pdfs.semanticscholar.org/86b6/afc667bb14ff4d69e7a5e8bb2454a6bbd2cd.pdf,Attentional Alignment Networks,2018
+46,United States,Helen,helen,32.7283683,-97.11201835,University of Texas at Arlington,edu,86b6afc667bb14ff4d69e7a5e8bb2454a6bbd2cd,citation,https://pdfs.semanticscholar.org/86b6/afc667bb14ff4d69e7a5e8bb2454a6bbd2cd.pdf,Attentional Alignment Networks,2018
+47,China,Helen,helen,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,86b6afc667bb14ff4d69e7a5e8bb2454a6bbd2cd,citation,https://pdfs.semanticscholar.org/86b6/afc667bb14ff4d69e7a5e8bb2454a6bbd2cd.pdf,Attentional Alignment Networks,2018
+48,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,4068574b8678a117d9a434360e9c12fe6232dae0,citation,http://ibug.doc.ic.ac.uk/media/uploads/documents/antonakos_automatic_2014.pdf,Automatic Construction of Deformable Models In-the-Wild,2014
+49,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,1d0128b9f96f4c11c034d41581f23eb4b4dd7780,citation,http://ibug.doc.ic.ac.uk/media/uploads/documents/robust_spherical_harmonics.pdf,Automatic construction Of robust spherical harmonic subspaces,2015
+50,China,Helen,helen,39.9041999,116.4073963,Key Lab of Intelligent Information Processing of Chinese Academy of Sciences,edu,22e2066acfb795ac4db3f97d2ac176d6ca41836c,citation,https://pdfs.semanticscholar.org/26f5/3a1abb47b1f0ea1f213dc7811257775dc6e6.pdf,Coarse-to-Fine Auto-Encoder Networks (CFAN) for Real-Time Face Alignment,2014
+51,China,Helen,helen,40.0044795,116.370238,Chinese Academy of Sciences,edu,22e2066acfb795ac4db3f97d2ac176d6ca41836c,citation,https://pdfs.semanticscholar.org/26f5/3a1abb47b1f0ea1f213dc7811257775dc6e6.pdf,Coarse-to-Fine Auto-Encoder Networks (CFAN) for Real-Time Face Alignment,2014
+52,China,Helen,helen,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,22e2066acfb795ac4db3f97d2ac176d6ca41836c,citation,https://pdfs.semanticscholar.org/26f5/3a1abb47b1f0ea1f213dc7811257775dc6e6.pdf,Coarse-to-Fine Auto-Encoder Networks (CFAN) for Real-Time Face Alignment,2014
+53,China,Helen,helen,22.4162632,114.2109318,Chinese University of Hong Kong,edu,ac6c3b3e92ff5fbcd8f7967696c7aae134bea209,citation,https://arxiv.org/pdf/1607.05046.pdf,Deep Cascaded Bi-Network for Face Hallucination,2016
+54,China,Helen,helen,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,ac6c3b3e92ff5fbcd8f7967696c7aae134bea209,citation,https://arxiv.org/pdf/1607.05046.pdf,Deep Cascaded Bi-Network for Face Hallucination,2016
+55,United States,Helen,helen,37.36566745,-120.42158888,"University of California, Merced",edu,ac6c3b3e92ff5fbcd8f7967696c7aae134bea209,citation,https://arxiv.org/pdf/1607.05046.pdf,Deep Cascaded Bi-Network for Face Hallucination,2016
+56,United States,Helen,helen,42.3614256,-71.0812092,Microsoft Research Asia,company,63d865c66faaba68018defee0daf201db8ca79ed,citation,https://arxiv.org/pdf/1409.5230.pdf,Deep Regression for Face Alignment,2014
+57,United States,Helen,helen,40.4441619,-79.94272826,Carnegie Mellon University,edu,35f921def890210dda4b72247849ad7ba7d35250,citation,http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Zhou_Exemplar-Based_Graph_Matching_2013_ICCV_paper.pdf,Exemplar-Based Graph Matching for Robust Facial Landmark Localization,2013
+58,United States,Helen,helen,42.3614256,-71.0812092,Microsoft Research Asia,company,898ff1bafee2a6fb3c848ad07f6f292416b5f07d,citation,,Face Alignment via Regressing Local Binary Features,2016
+59,China,Helen,helen,31.83907195,117.26420748,University of Science and Technology of China,edu,898ff1bafee2a6fb3c848ad07f6f292416b5f07d,citation,,Face Alignment via Regressing Local Binary Features,2016
+60,United States,Helen,helen,47.6423318,-122.1369302,Microsoft,company,898ff1bafee2a6fb3c848ad07f6f292416b5f07d,citation,,Face Alignment via Regressing Local Binary Features,2016
+61,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,71b07c537a9e188b850192131bfe31ef206a39a0,citation,https://pdfs.semanticscholar.org/71b0/7c537a9e188b850192131bfe31ef206a39a0.pdf,Faces InThe-Wild Challenge : database and results,2016
+62,United Kingdom,Helen,helen,52.9387428,-1.20029569,University of Nottingham,edu,71b07c537a9e188b850192131bfe31ef206a39a0,citation,https://pdfs.semanticscholar.org/71b0/7c537a9e188b850192131bfe31ef206a39a0.pdf,Faces InThe-Wild Challenge : database and results,2016
+63,Netherlands,Helen,helen,52.2380139,6.8566761,University of Twente,edu,71b07c537a9e188b850192131bfe31ef206a39a0,citation,https://pdfs.semanticscholar.org/71b0/7c537a9e188b850192131bfe31ef206a39a0.pdf,Faces InThe-Wild Challenge : database and results,2016
+64,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,f095b5770f0ff13ba9670e3d480743c5e9ad1036,citation,http://doc.utwente.nl/103789/1/Pantic_Fast_Algorithms_for_Fitting_Active_Appearance_Models.pdf,Fast Algorithms for Fitting Active Appearance Models to Unconstrained Images,2016
+65,Netherlands,Helen,helen,52.2380139,6.8566761,University of Twente,edu,f095b5770f0ff13ba9670e3d480743c5e9ad1036,citation,http://doc.utwente.nl/103789/1/Pantic_Fast_Algorithms_for_Fitting_Active_Appearance_Models.pdf,Fast Algorithms for Fitting Active Appearance Models to Unconstrained Images,2016
+66,United Kingdom,Helen,helen,52.9387428,-1.20029569,University of Nottingham,edu,f095b5770f0ff13ba9670e3d480743c5e9ad1036,citation,http://doc.utwente.nl/103789/1/Pantic_Fast_Algorithms_for_Fitting_Active_Appearance_Models.pdf,Fast Algorithms for Fitting Active Appearance Models to Unconstrained Images,2016
+67,United Kingdom,Helen,helen,53.22853665,-0.54873472,University of Lincoln,edu,624496296af19243d5f05e7505fd927db02fd0ce,citation,http://eprints.eemcs.utwente.nl/25815/01/Pantic_Gauss-Newton_Deformable_Part_Models.pdf,Gauss-Newton Deformable Part Models for Face Alignment In-the-Wild,2014
+68,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,624496296af19243d5f05e7505fd927db02fd0ce,citation,http://eprints.eemcs.utwente.nl/25815/01/Pantic_Gauss-Newton_Deformable_Part_Models.pdf,Gauss-Newton Deformable Part Models for Face Alignment In-the-Wild,2014
+69,United Kingdom,Helen,helen,53.22853665,-0.54873472,University of Lincoln,edu,6a4ebd91c4d380e21da0efb2dee276897f56467a,citation,http://eprints.nottingham.ac.uk/31441/1/tzimiroICIP14b.pdf,HOG active appearance models,2014
+70,China,Helen,helen,40.0044795,116.370238,Chinese Academy of Sciences,edu,696236fb6f986f6d5565abb01f402d09db68e5fa,citation,http://openaccess.thecvf.com/content_cvpr_2017/papers/Wei_Learning_Adaptive_Receptive_CVPR_2017_paper.pdf,Learning adaptive receptive fields for deep image parsing networks,2017
+71,China,Helen,helen,32.0565957,118.77408833,Nanjing University,edu,696236fb6f986f6d5565abb01f402d09db68e5fa,citation,http://openaccess.thecvf.com/content_cvpr_2017/papers/Wei_Learning_Adaptive_Receptive_CVPR_2017_paper.pdf,Learning adaptive receptive fields for deep image parsing networks,2017
+72,China,Helen,helen,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,696236fb6f986f6d5565abb01f402d09db68e5fa,citation,http://openaccess.thecvf.com/content_cvpr_2017/papers/Wei_Learning_Adaptive_Receptive_CVPR_2017_paper.pdf,Learning adaptive receptive fields for deep image parsing networks,2017
+73,United Kingdom,Helen,helen,52.17638955,0.14308882,University of Cambridge,edu,c17a332e59f03b77921942d487b4b102b1ee73b6,citation,https://pdfs.semanticscholar.org/c17a/332e59f03b77921942d487b4b102b1ee73b6.pdf,Learning an appearance-based gaze estimator from one million synthesised images,2016
+74,United States,Helen,helen,40.4441619,-79.94272826,Carnegie Mellon University,edu,c17a332e59f03b77921942d487b4b102b1ee73b6,citation,https://pdfs.semanticscholar.org/c17a/332e59f03b77921942d487b4b102b1ee73b6.pdf,Learning an appearance-based gaze estimator from one million synthesised images,2016
+75,Germany,Helen,helen,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,c17a332e59f03b77921942d487b4b102b1ee73b6,citation,https://pdfs.semanticscholar.org/c17a/332e59f03b77921942d487b4b102b1ee73b6.pdf,Learning an appearance-based gaze estimator from one million synthesised images,2016
+76,United States,Helen,helen,45.55236,-122.9142988,Intel Corporation,company,9ef2b2db11ed117521424c275c3ce1b5c696b9b3,citation,https://arxiv.org/pdf/1511.04404.pdf,Robust Face Alignment Using a Mixture of Invariant Experts,2016
+77,Germany,Helen,helen,48.7863462,9.2380718,Daimler AG,company,3a8846ca16df5dfb2daadc189ed40c13d2ddc0c5,citation,https://arxiv.org/pdf/1901.10143.pdf,Validation loss for landmark detection,2019
+78,South Africa,Helen,helen,-33.95828745,18.45997349,University of Cape Town,edu,3bc376f29bc169279105d33f59642568de36f17f,citation,http://www.dip.ee.uct.ac.za/~nicolls/publish/sm14-visapp.pdf,Active shape models with SIFT descriptors and MARS,2014
+79,United States,Helen,helen,33.9832526,-118.40417,USC Institute for Creative Technologies,edu,0a6d344112b5af7d1abbd712f83c0d70105211d0,citation,http://ict.usc.edu/pubs/Constrained%20local%20neural%20fields%20for%20robust%20facial%20landmark%20detection%20in%20the%20wild.pdf,Constrained Local Neural Fields for Robust Facial Landmark Detection in the Wild,2013
+80,China,Helen,helen,23.09461185,113.28788994,Sun Yat-Sen University,edu,3be8f1f7501978287af8d7ebfac5963216698249,citation,https://pdfs.semanticscholar.org/3be8/f1f7501978287af8d7ebfac5963216698249.pdf,Deep Cascaded Regression for Face Alignment,2015
+81,Singapore,Helen,helen,1.2962018,103.77689944,National University of Singapore,edu,3be8f1f7501978287af8d7ebfac5963216698249,citation,https://pdfs.semanticscholar.org/3be8/f1f7501978287af8d7ebfac5963216698249.pdf,Deep Cascaded Regression for Face Alignment,2015
+82,China,Helen,helen,40.00229045,116.32098908,Tsinghua University,edu,329d58e8fb30f1bf09acb2f556c9c2f3e768b15c,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Wu_Leveraging_Intra_and_CVPR_2017_paper.pdf,Leveraging Intra and Inter-Dataset Variations for Robust Face Alignment,2017
+83,China,Helen,helen,22.4162632,114.2109318,Chinese University of Hong Kong,edu,329d58e8fb30f1bf09acb2f556c9c2f3e768b15c,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Wu_Leveraging_Intra_and_CVPR_2017_paper.pdf,Leveraging Intra and Inter-Dataset Variations for Robust Face Alignment,2017
+84,France,Helen,helen,48.8407791,2.5873259,University of Paris-Est,edu,0293721d276856f0425d4417e22381de3350ac32,citation,https://hal-upec-upem.archives-ouvertes.fr/hal-01790317/file/RK_SSD_2018.pdf,Customer Satisfaction Measuring Based on the Most Significant Facial Emotion,2018
+85,Tunisia,Helen,helen,34.7361066,10.7427275,"University of Sfax, Tunisia",edu,0293721d276856f0425d4417e22381de3350ac32,citation,https://hal-upec-upem.archives-ouvertes.fr/hal-01790317/file/RK_SSD_2018.pdf,Customer Satisfaction Measuring Based on the Most Significant Facial Emotion,2018
+86,United States,Helen,helen,42.4505507,-76.4783513,Cornell University,edu,ce9e1dfa7705623bb67df3a91052062a0a0ca456,citation,https://arxiv.org/pdf/1611.05507.pdf,Deep Feature Interpolation for Image Content Changes,2017
+87,United States,Helen,helen,38.8997145,-77.0485992,George Washington University,edu,ce9e1dfa7705623bb67df3a91052062a0a0ca456,citation,https://arxiv.org/pdf/1611.05507.pdf,Deep Feature Interpolation for Image Content Changes,2017
+88,China,Helen,helen,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,2d294bde112b892068636f3a48300b3c033d98da,citation,https://arxiv.org/pdf/1808.01558.pdf,Deep Multi-Center Learning for Face Alignment,2018
+89,China,Helen,helen,31.2284923,121.40211389,East China Normal University,edu,2d294bde112b892068636f3a48300b3c033d98da,citation,https://arxiv.org/pdf/1808.01558.pdf,Deep Multi-Center Learning for Face Alignment,2018
+90,China,Helen,helen,23.09461185,113.28788994,Sun Yat-Sen University,edu,30cd39388b5c1aae7d8153c0ab9d54b61b474ffe,citation,https://arxiv.org/pdf/1510.09083.pdf,Deep Recurrent Regression for Facial Landmark Detection,2018
+91,Singapore,Helen,helen,1.2962018,103.77689944,National University of Singapore,edu,30cd39388b5c1aae7d8153c0ab9d54b61b474ffe,citation,https://arxiv.org/pdf/1510.09083.pdf,Deep Recurrent Regression for Facial Landmark Detection,2018
+92,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,0209389b8369aaa2a08830ac3b2036d4901ba1f1,citation,https://arxiv.org/pdf/1612.01202.pdf,DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild,2017
+93,United Kingdom,Helen,helen,51.5231607,-0.1282037,University College London,edu,0209389b8369aaa2a08830ac3b2036d4901ba1f1,citation,https://arxiv.org/pdf/1612.01202.pdf,DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild,2017
+94,United States,Helen,helen,42.7298459,-73.67950216,Rensselaer Polytechnic Institute,edu,191d30e7e7360d565b0c1e2814b5bcbd86a11d41,citation,http://homepages.rpi.edu/~wuy9/DiscriminativeDeepFaceShape/DiscriminativeDeepFaceShape_IJCV.pdf,Discriminative Deep Face Shape Model for Facial Point Detection,2014
+95,United States,Helen,helen,39.2899685,-76.62196103,University of Maryland,edu,ceeb67bf53ffab1395c36f1141b516f893bada27,citation,https://arxiv.org/pdf/1601.07950.pdf,Face Alignment by Local Deep Descriptor Regression,2016
+96,United States,Helen,helen,40.47913175,-74.43168868,Rutgers University,edu,ceeb67bf53ffab1395c36f1141b516f893bada27,citation,https://arxiv.org/pdf/1601.07950.pdf,Face Alignment by Local Deep Descriptor Regression,2016
+97,United States,Helen,helen,43.1576969,-77.58829158,University of Rochester,edu,beb8d7c128ccbdc6b63959a763ebc505a5313c06,citation,https://arxiv.org/pdf/1812.03252.pdf,Face Completion with Semantic Knowledge and Collaborative Adversarial Learning,2018
+98,China,Helen,helen,40.0044795,116.370238,Chinese Academy of Sciences,edu,beb8d7c128ccbdc6b63959a763ebc505a5313c06,citation,https://arxiv.org/pdf/1812.03252.pdf,Face Completion with Semantic Knowledge and Collaborative Adversarial Learning,2018
+99,United Kingdom,Helen,helen,51.24303255,-0.59001382,University of Surrey,edu,438e7999c937b94f0f6384dbeaa3febff6d283b6,citation,https://arxiv.org/pdf/1705.02402.pdf,"Face Detection, Bounding Box Aggregation and Pose Estimation for Robust Facial Landmark Localisation in the Wild",2017
+100,China,Helen,helen,31.4854255,120.2739581,Jiangnan University,edu,438e7999c937b94f0f6384dbeaa3febff6d283b6,citation,https://arxiv.org/pdf/1705.02402.pdf,"Face Detection, Bounding Box Aggregation and Pose Estimation for Robust Facial Landmark Localisation in the Wild",2017
+101,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,84e6669b47670f9f4f49c0085311dce0e178b685,citation,https://arxiv.org/pdf/1502.00852.pdf,Face frontalization for Alignment and Recognition,2015
+102,Netherlands,Helen,helen,52.2380139,6.8566761,University of Twente,edu,84e6669b47670f9f4f49c0085311dce0e178b685,citation,https://arxiv.org/pdf/1502.00852.pdf,Face frontalization for Alignment and Recognition,2015
+103,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,2f7aa942313b1eb12ebfab791af71d0a3830b24c,citation,http://ibug.doc.ic.ac.uk/media/uploads/documents/antonakos2015feature.pdf,Feature-Based Lucas–Kanade and Active Appearance Models,2015
+104,United Kingdom,Helen,helen,52.9387428,-1.20029569,University of Nottingham,edu,2f7aa942313b1eb12ebfab791af71d0a3830b24c,citation,http://ibug.doc.ic.ac.uk/media/uploads/documents/antonakos2015feature.pdf,Feature-Based Lucas–Kanade and Active Appearance Models,2015
+105,United Kingdom,Helen,helen,52.9387428,-1.20029569,University of Nottingham,edu,1c1a98df3d0d5e2034ea723994bdc85af45934db,citation,http://www.cs.nott.ac.uk/~pszmv/Documents/ICCV-300w_cameraready.pdf,Guided Unsupervised Learning of Mode Specific Models for Facial Point Detection in the Wild,2013
+106,China,Helen,helen,22.4162632,114.2109318,Chinese University of Hong Kong,edu,f070d739fb812d38571ec77490ccd8777e95ce7a,citation,https://zhzhanp.github.io/papers/PR2015.pdf,Hierarchical facial landmark localization via cascaded random binary patterns,2015
+107,China,Helen,helen,22.53521465,113.9315911,Shenzhen University,edu,f070d739fb812d38571ec77490ccd8777e95ce7a,citation,https://zhzhanp.github.io/papers/PR2015.pdf,Hierarchical facial landmark localization via cascaded random binary patterns,2015
+108,United States,Helen,helen,34.0224149,-118.28634407,University of Southern California,edu,87e6cb090aecfc6f03a3b00650a5c5f475dfebe1,citation,https://pdfs.semanticscholar.org/87e6/cb090aecfc6f03a3b00650a5c5f475dfebe1.pdf,Holistically Constrained Local Model: Going Beyond Frontal Poses for Facial Landmark Detection,2016
+109,United States,Helen,helen,40.4441619,-79.94272826,Carnegie Mellon University,edu,87e6cb090aecfc6f03a3b00650a5c5f475dfebe1,citation,https://pdfs.semanticscholar.org/87e6/cb090aecfc6f03a3b00650a5c5f475dfebe1.pdf,Holistically Constrained Local Model: Going Beyond Frontal Poses for Facial Landmark Detection,2016
+110,Singapore,Helen,helen,1.2962018,103.77689944,National University of Singapore,edu,0ea7b7fff090c707684fd4dc13e0a8f39b300a97,citation,https://arxiv.org/pdf/1711.06055.pdf,Integrated Face Analytics Networks through Cross-Dataset Hybrid Training,2017
+111,China,Helen,helen,39.9586652,116.30971281,Beijing Institute of Technology,edu,0ea7b7fff090c707684fd4dc13e0a8f39b300a97,citation,https://arxiv.org/pdf/1711.06055.pdf,Integrated Face Analytics Networks through Cross-Dataset Hybrid Training,2017
+112,China,Helen,helen,23.0490047,113.3971571,South China University of China,edu,7d7be6172fc2884e1da22d1e96d5899a29831ad2,citation,https://arxiv.org/pdf/1703.01605.pdf,L2GSCI: Local to Global Seam Cutting and Integrating for Accurate Face Contour Extraction,2017
+113,China,Helen,helen,22.46935655,114.19474194,Education University of Hong Kong,edu,7d7be6172fc2884e1da22d1e96d5899a29831ad2,citation,https://arxiv.org/pdf/1703.01605.pdf,L2GSCI: Local to Global Seam Cutting and Integrating for Accurate Face Contour Extraction,2017
+114,United States,Helen,helen,34.0224149,-118.28634407,University of Southern California,edu,d28d32af7ef9889ef9cb877345a90ea85e70f7f1,citation,http://multicomp.cs.cmu.edu/wp-content/uploads/2017/10/2017_FG_Kim_Local.pdf,Local-Global Landmark Confidences for Face Recognition,2017
+115,United States,Helen,helen,40.4441619,-79.94272826,Carnegie Mellon University,edu,d28d32af7ef9889ef9cb877345a90ea85e70f7f1,citation,http://multicomp.cs.cmu.edu/wp-content/uploads/2017/10/2017_FG_Kim_Local.pdf,Local-Global Landmark Confidences for Face Recognition,2017
+116,China,Helen,helen,40.0044795,116.370238,Chinese Academy of Sciences,edu,303a7099c01530fa0beb197eb1305b574168b653,citation,http://openaccess.thecvf.com/content_cvpr_2016/papers/Zhang_Occlusion-Free_Face_Alignment_CVPR_2016_paper.pdf,Occlusion-Free Face Alignment: Deep Regression Networks Coupled with De-Corrupt AutoEncoders,2016
+117,China,Helen,helen,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,303a7099c01530fa0beb197eb1305b574168b653,citation,http://openaccess.thecvf.com/content_cvpr_2016/papers/Zhang_Occlusion-Free_Face_Alignment_CVPR_2016_paper.pdf,Occlusion-Free Face Alignment: Deep Regression Networks Coupled with De-Corrupt AutoEncoders,2016
+118,Sweden,Helen,helen,59.34986645,18.07063213,"KTH Royal Institute of Technology, Stockholm",edu,1824b1ccace464ba275ccc86619feaa89018c0ad,citation,http://www.csc.kth.se/~vahidk/face/KazemiCVPR14.pdf,One millisecond face alignment with an ensemble of regression trees,2014
+119,United States,Helen,helen,35.3103441,-80.73261617,University of North Carolina at Charlotte,edu,89002a64e96a82486220b1d5c3f060654b24ef2a,citation,http://research.rutgers.edu/~shaoting/paper/ICCV15_face.pdf,PIEFA: Personalized Incremental and Ensemble Face Alignment,2015
+120,United States,Helen,helen,45.57022705,-122.63709346,Concordia University,edu,6d0fe30444c6f4e4db3ad8b02fb2c87e2b33c58d,citation,https://arxiv.org/pdf/1607.00659.pdf,Robust Deep Appearance Models,2016
+121,United States,Helen,helen,40.4441619,-79.94272826,Carnegie Mellon University,edu,6d0fe30444c6f4e4db3ad8b02fb2c87e2b33c58d,citation,https://arxiv.org/pdf/1607.00659.pdf,Robust Deep Appearance Models,2016
+122,China,Helen,helen,40.0044795,116.370238,Chinese Academy of Sciences,edu,7fcfd72ba6bc14bbb90b31fe14c2c77a8b220ab2,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/He_Robust_FEC-CNN_A_CVPR_2017_paper.pdf,Robust FEC-CNN: A High Accuracy Facial Landmark Detection System,2017
+123,China,Helen,helen,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,7fcfd72ba6bc14bbb90b31fe14c2c77a8b220ab2,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/He_Robust_FEC-CNN_A_CVPR_2017_paper.pdf,Robust FEC-CNN: A High Accuracy Facial Landmark Detection System,2017
+124,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,788a7b59ea72e23ef4f86dc9abb4450efefeca41,citation,http://eprints.eemcs.utwente.nl/26840/01/Pantic_Robust_Statistical_Face_Frontalization.pdf,Robust Statistical Face Frontalization,2015
+125,Netherlands,Helen,helen,52.2380139,6.8566761,University of Twente,edu,788a7b59ea72e23ef4f86dc9abb4450efefeca41,citation,http://eprints.eemcs.utwente.nl/26840/01/Pantic_Robust_Statistical_Face_Frontalization.pdf,Robust Statistical Face Frontalization,2015
+126,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,7cdf3bc1de6c7948763c0c2dfa4384dcbd3677a0,citation,http://eprints.eemcs.utwente.nl/27129/01/sagonas2016robust.pdf,Robust Statistical Frontalization of Human and Animal Faces,2016
+127,Netherlands,Helen,helen,52.2380139,6.8566761,University of Twente,edu,7cdf3bc1de6c7948763c0c2dfa4384dcbd3677a0,citation,http://eprints.eemcs.utwente.nl/27129/01/sagonas2016robust.pdf,Robust Statistical Frontalization of Human and Animal Faces,2016
+128,United States,Helen,helen,40.47913175,-74.43168868,Rutgers University,edu,04ff69aa20da4eeccdabbe127e3641b8e6502ec0,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016_workshops/w28/papers/Peng_Sequential_Face_Alignment_CVPR_2016_paper.pdf,Sequential Face Alignment via Person-Specific Modeling in the Wild,2016
+129,United States,Helen,helen,32.7283683,-97.11201835,University of Texas at Arlington,edu,04ff69aa20da4eeccdabbe127e3641b8e6502ec0,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016_workshops/w28/papers/Peng_Sequential_Face_Alignment_CVPR_2016_paper.pdf,Sequential Face Alignment via Person-Specific Modeling in the Wild,2016
+130,United States,Helen,helen,40.47913175,-74.43168868,Rutgers University,edu,c8ca6a2dc41516c16ea0747e9b3b7b1db788dbdd,citation,https://arxiv.org/pdf/1609.02825.pdf,Track Facial Points in Unconstrained Videos,2016
+131,United States,Helen,helen,32.7298718,-97.1140116,The University of Texas at Arlington,edu,c8ca6a2dc41516c16ea0747e9b3b7b1db788dbdd,citation,https://arxiv.org/pdf/1609.02825.pdf,Track Facial Points in Unconstrained Videos,2016
+132,China,Helen,helen,22.4162632,114.2109318,Chinese University of Hong Kong,edu,433a6d6d2a3ed8a6502982dccc992f91d665b9b3,citation,https://arxiv.org/pdf/1409.0602.pdf,Transferring Landmark Annotations for Cross-Dataset Face Alignment.,2014
+133,China,Helen,helen,40.00229045,116.32098908,Tsinghua University,edu,433a6d6d2a3ed8a6502982dccc992f91d665b9b3,citation,https://arxiv.org/pdf/1409.0602.pdf,Transferring Landmark Annotations for Cross-Dataset Face Alignment.,2014
+134,Canada,Helen,helen,49.8091536,-97.13304179,University of Manitoba,edu,3bf249f716a384065443abc6172f4bdef88738d9,citation,https://arxiv.org/pdf/1812.01063.pdf,A Hybrid Instance-based Transfer Learning Method,2018
+135,United States,Helen,helen,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
+136,South Korea,Helen,helen,37.26728,126.9841151,Seoul National University,edu,b4362cd87ad219790800127ddd366cc465606a78,citation,https://pdfs.semanticscholar.org/b436/2cd87ad219790800127ddd366cc465606a78.pdf,A Smartphone-Based Automatic Diagnosis System for Facial Nerve Palsy,2015
+137,Canada,Helen,helen,43.66333345,-79.39769975,University of Toronto,edu,3a54b23cdbd159bb32c39c3adcba8229e3237e56,citation,https://arxiv.org/pdf/1805.12302.pdf,Adversarial Attacks on Face Detectors Using Neural Net Based Constrained Optimization,2018
+138,United States,Helen,helen,32.8800604,-117.2340135,University of California San Diego,edu,3ac0aefb379dedae4a6054e649e98698b3e5fb82,citation,https://arxiv.org/pdf/1802.02137.pdf,An Occluded Stacked Hourglass Approach to Facial Landmark Localization and Occlusion Estimation,2017
+139,United Kingdom,Helen,helen,53.8066815,-1.5550328,The University of Leeds,edu,c5ea084531212284ce3f1ca86a6209f0001de9d1,citation,https://pdfs.semanticscholar.org/c5ea/084531212284ce3f1ca86a6209f0001de9d1.pdf,Audio-visual speech processing for multimedia localisation,2016
+140,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,06c2dfe1568266ad99368fc75edf79585e29095f,citation,http://ibug.doc.ic.ac.uk/media/uploads/documents/joan_cvpr2014.pdf,Bayesian Active Appearance Models,2014
+141,United Kingdom,Helen,helen,52.9387428,-1.20029569,University of Nottingham,edu,ccf16bcf458e4d7a37643b8364594656287f5bfc,citation,https://pdfs.semanticscholar.org/ccf1/6bcf458e4d7a37643b8364594656287f5bfc.pdf,Cascade for Landmark Guided Semantic Part Segmentation,2016
+142,China,Helen,helen,31.4854255,120.2739581,Jiangnan University,edu,60824ee635777b4ee30fcc2485ef1e103b8e7af9,citation,http://epubs.surrey.ac.uk/808177/1/Feng-TIP-2015.pdf,Cascaded Collaborative Regression for Robust Facial Landmark Detection Trained Using a Mixture of Synthetic and Real Images With Dynamic Weighting,2015
+143,United Kingdom,Helen,helen,51.2421839,-0.5905421,University of Surrey Guildford,edu,60824ee635777b4ee30fcc2485ef1e103b8e7af9,citation,http://epubs.surrey.ac.uk/808177/1/Feng-TIP-2015.pdf,Cascaded Collaborative Regression for Robust Facial Landmark Detection Trained Using a Mixture of Synthetic and Real Images With Dynamic Weighting,2015
+144,China,Helen,helen,22.304572,114.17976285,Hong Kong Polytechnic University,edu,4836b084a583d2e794eb6a94982ea30d7990f663,citation,https://arxiv.org/pdf/1611.06642.pdf,Cascaded Face Alignment via Intimacy Definition Feature,2017
+145,United Kingdom,Helen,helen,52.9387428,-1.20029569,University of Nottingham,edu,72a1852c78b5e95a57efa21c92bdc54219975d8f,citation,http://eprints.nottingham.ac.uk/31303/1/prl_blockwise_SDM.pdf,Cascaded regression with sparsified feature covariance matrix for facial landmark detection,2016
+146,United States,Helen,helen,40.4441619,-79.94272826,Carnegie Mellon University,edu,4140498e96a5ff3ba816d13daf148fffb9a2be3f,citation,http://multicomp.cs.cmu.edu/wp-content/uploads/2017/10/2017_FG_Li_Constrained.pdf,Constrained Ensemble Initialization for Facial Landmark Tracking in Video,2017
+147,United States,Helen,helen,40.4441619,-79.94272826,Carnegie Mellon University,edu,963d0d40de8780161b70d28d2b125b5222e75596,citation,https://arxiv.org/pdf/1611.08657.pdf,Convolutional Experts Constrained Local Model for Facial Landmark Detection,2017
+148,United States,Helen,helen,32.87935255,-117.23110049,"University of California, San Diego",edu,ee418372b0038bd3b8ae82bd1518d5c01a33a7ec,citation,https://pdfs.semanticscholar.org/ee41/8372b0038bd3b8ae82bd1518d5c01a33a7ec.pdf,CSE 255 Winter 2015 Assignment 1 : Eye Detection using Histogram of Oriented Gradients and Adaboost Classifier,2015
+149,Poland,Helen,helen,52.22165395,21.00735776,Warsaw University of Technology,edu,f27b8b8f2059248f77258cf8595e9434cf0b0228,citation,https://arxiv.org/pdf/1706.01789.pdf,Deep Alignment Network: A Convolutional Neural Network for Robust Face Alignment,2017
+150,United States,Helen,helen,40.4441619,-79.94272826,Carnegie Mellon University,edu,a0b1990dd2b4cd87e4fd60912cc1552c34792770,citation,https://pdfs.semanticscholar.org/a0b1/990dd2b4cd87e4fd60912cc1552c34792770.pdf,Deep Constrained Local Models for Facial Landmark Detection,2016
+151,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,38cbb500823057613494bacd0078aa0e57b30af8,citation,https://arxiv.org/pdf/1704.08772.pdf,Deep Face Deblurring,2017
+152,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,9b8f7a6850d991586b7186f0bb7e424924a9fd74,citation,https://ibug.doc.ic.ac.uk/media/uploads/documents/disentangling-modes-variation.pdf,Disentangling the Modes of Variation in Unlabelled Data,2018
+153,China,Helen,helen,30.642769,104.06751175,"Sichuan University, Chengdu",edu,b29b42f7ab8d25d244bfc1413a8d608cbdc51855,citation,https://arxiv.org/pdf/1702.02719.pdf,Effective face landmark localization via single deep network,2017
+154,China,Helen,helen,22.304572,114.17976285,Hong Kong Polytechnic University,edu,4cfa8755fe23a8a0b19909fa4dec54ce6c1bd2f7,citation,https://arxiv.org/pdf/1611.09956.pdf,Efficient likelihood Bayesian constrained local model,2017
+155,China,Helen,helen,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,5c820e47981d21c9dddde8d2f8020146e600368f,citation,https://pdfs.semanticscholar.org/5c82/0e47981d21c9dddde8d2f8020146e600368f.pdf,Extended Supervised Descent Method for Robust Face Alignment,2014
+156,China,Helen,helen,32.0565957,118.77408833,Nanjing University,edu,f633d6dc02b2e55eb24b89f2b8c6df94a2de86dd,citation,http://parnec.nuaa.edu.cn/pubs/xiaoyang%20tan/journal/2016/JXPR-2016.pdf,Face alignment by robust discriminative Hough voting,2016
+157,Romania,Helen,helen,46.7723581,23.5852075,Technical University,edu,f0ae807627f81acb63eb5837c75a1e895a92c376,citation,https://pdfs.semanticscholar.org/f0ae/807627f81acb63eb5837c75a1e895a92c376.pdf,Facial Landmark Detection using Ensemble of Cascaded Regressions,2016
+158,Czech Republic,Helen,helen,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
+159,China,Helen,helen,32.0565957,118.77408833,Nanjing University,edu,5b0bf1063b694e4b1575bb428edb4f3451d9bf04,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w25/papers/Yang_Facial_Shape_Tracking_ICCV_2015_paper.pdf,Facial Shape Tracking via Spatio-Temporal Cascade Shape Regression,2015
+160,Switzerland,Helen,helen,47.376313,8.5476699,ETH Zurich,edu,a66d89357ada66d98d242c124e1e8d96ac9b37a0,citation,https://arxiv.org/pdf/1608.06451.pdf,Failure Detection for Facial Landmark Detectors,2016
+161,United States,Helen,helen,40.4441619,-79.94272826,Carnegie Mellon University,edu,f1b4583c576d6d8c661b4b2c82bdebf3ba3d7e53,citation,https://arxiv.org/pdf/1707.05653.pdf,Faster than Real-Time Facial Alignment: A 3D Spatial Transformer Network Approach in Unconstrained Poses,2017
+162,United Kingdom,Helen,helen,51.24303255,-0.59001382,University of Surrey,edu,70a69569ba61f3585cd90c70ca5832e838fa1584,citation,https://pdfs.semanticscholar.org/70a6/9569ba61f3585cd90c70ca5832e838fa1584.pdf,Friendly Faces: Weakly Supervised Character Identification,2014
+163,United States,Helen,helen,37.36566745,-120.42158888,"University of California, Merced",edu,f0a4a3fb6997334511d7b8fc090f9ce894679faf,citation,https://arxiv.org/pdf/1704.05838.pdf,Generative Face Completion,2017
+164,United States,Helen,helen,28.0599999,-82.41383619,University of South Florida,edu,ba21fd28003994480f713b0a1276160fea2e89b5,citation,https://pdfs.semanticscholar.org/ba21/fd28003994480f713b0a1276160fea2e89b5.pdf,Identification of Individuals from Ears in Real World Conditions,2018
+165,United States,Helen,helen,28.59899755,-81.19712501,University of Central Florida,edu,a40edf6eb979d1ddfe5894fac7f2cf199519669f,citation,https://arxiv.org/pdf/1704.08740.pdf,Improving Facial Attribute Prediction Using Semantic Segmentation,2017
+166,Germany,Helen,helen,48.263011,11.666857,Technical University of Munich,edu,e6178de1ef15a6a973aad2791ce5fbabc2cb8ae5,citation,https://pdfs.semanticscholar.org/e617/8de1ef15a6a973aad2791ce5fbabc2cb8ae5.pdf,Improving Facial Landmark Detection via a Super-Resolution Inception Network,2017
+167,United Kingdom,Helen,helen,52.9387428,-1.20029569,University of Nottingham,edu,9ca0626366e136dac6bfd628cec158e26ed959c7,citation,https://arxiv.org/pdf/1811.02194.pdf,In-the-wild Facial Expression Recognition in Extreme Poses,2017
+168,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,500b92578e4deff98ce20e6017124e6d2053b451,citation,http://eprints.eemcs.utwente.nl/25818/01/Pantic_Incremental_Face_Alignment_in_the_Wild.pdf,Incremental Face Alignment in the Wild,2014
+169,Netherlands,Helen,helen,52.2380139,6.8566761,University of Twente,edu,500b92578e4deff98ce20e6017124e6d2053b451,citation,http://eprints.eemcs.utwente.nl/25818/01/Pantic_Incremental_Face_Alignment_in_the_Wild.pdf,Incremental Face Alignment in the Wild,2014
+170,China,Helen,helen,40.00229045,116.32098908,Tsinghua University,edu,8dd162c9419d29564e9777dd523382a20c683d89,citation,https://arxiv.org/pdf/1806.02479.pdf,Interlinked Convolutional Neural Networks for Face Parsing,2015
+171,United Kingdom,Helen,helen,52.9387428,-1.20029569,University of Nottingham,edu,2c14c3bb46275da5706c466f9f51f4424ffda914,citation,http://braismartinez.com/media/documents/2015ivc_-_l21-based_regression_and_prediction_accumulation_across_views_for_robust_facial_landmark_detection.pdf,"L2, 1-based regression and prediction accumulation across views for robust facial landmark detection",2016
+172,China,Helen,helen,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,c00f402b9cfc3f8dd2c74d6b3552acbd1f358301,citation,https://arxiv.org/pdf/1608.00207.pdf,Learning deep representation from coarse to fine for face alignment,2016
+173,China,Helen,helen,31.83907195,117.26420748,University of Science and Technology of China,edu,b5f79df712ad535d88ae784a617a30c02e0551ca,citation,http://staff.ustc.edu.cn/~juyong/Papers/FaceAlignment-2015.pdf,Locating Facial Landmarks Using Probabilistic Random Forest,2015
+174,United Kingdom,Helen,helen,52.3793131,-1.5604252,University of Warwick,edu,0bc53b338c52fc635687b7a6c1e7c2b7191f42e5,citation,https://pdfs.semanticscholar.org/a32a/8d6d4c3b4d69544763be48ffa7cb0d7f2f23.pdf,Loglet SIFT for Part Description in Deformable Part Models: Application to Face Alignment,2016
+175,United Kingdom,Helen,helen,53.4717306,-2.2399239,Manchester Metropolitan University,edu,6fd4048bfe3123e94c2648e53a56bc6bf8ff4cdd,citation,https://pdfs.semanticscholar.org/6fd4/048bfe3123e94c2648e53a56bc6bf8ff4cdd.pdf,Micro-facial movement detection using spatio-temporal features,2016
+176,United Kingdom,Helen,helen,51.5247272,-0.03931035,Queen Mary University of London,edu,0f81b0fa8df5bf3fcfa10f20120540342a0c92e5,citation,https://arxiv.org/pdf/1501.05152.pdf,"Mirror, mirror on the wall, tell me, is the error small?",2015
+177,South Africa,Helen,helen,-33.95828745,18.45997349,University of Cape Town,edu,36e8ef2e5d52a78dddf0002e03918b101dcdb326,citation,http://www.milbo.org/stasm-files/multiview-active-shape-models-with-sift-for-300w.pdf,Multiview Active Shape Models with SIFT Descriptors for the 300-W Face Landmark Challenge,2013
+178,United States,Helen,helen,40.51865195,-74.44099801,State University of New Jersey,edu,bbc5f4052674278c96abe7ff9dc2d75071b6e3f3,citation,https://pdfs.semanticscholar.org/287b/7baff99d6995fd5852002488eb44659be6c1.pdf,Nonlinear Hierarchical Part-Based Regression for Unconstrained Face Alignment,2016
+179,United States,Helen,helen,33.6404952,-117.8442962,University of California at Irvine,edu,bd13f50b8997d0733169ceba39b6eb1bda3eb1aa,citation,https://arxiv.org/pdf/1506.08347.pdf,Occlusion Coherence: Detecting and Localizing Occluded Faces,2015
+180,United States,Helen,helen,33.6404952,-117.8442962,University of California Irvine,edu,65126e0b1161fc8212643b8ff39c1d71d262fbc1,citation,http://vision.ics.uci.edu/papers/GhiasiF_CVPR_2014/GhiasiF_CVPR_2014.pdf,Occlusion Coherence: Localizing Occluded Faces with a Hierarchical Deformable Part Model,2014
+181,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,4a8480d58c30dc484bda08969e754cd13a64faa1,citation,http://ibug.doc.ic.ac.uk/media/uploads/documents/paper_offline.pdf,Offline Deformable Face Tracking in Arbitrary Videos,2015
+182,Germany,Helen,helen,52.14005065,11.64471248,Otto von Guericke University,edu,7d1688ce0b48096e05a66ead80e9270260cb8082,citation,http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w44/Saxen_Real_vs._Fake_ICCV_2017_paper.pdf,Real vs. Fake Emotion Challenge: Learning to Rank Authenticity from Facial Activity Descriptors,2017
+183,United Kingdom,Helen,helen,51.24303255,-0.59001382,University of Surrey,edu,3c6cac7ecf546556d7c6050f7b693a99cc8a57b3,citation,https://pdfs.semanticscholar.org/3c6c/ac7ecf546556d7c6050f7b693a99cc8a57b3.pdf,Robust facial landmark detection in the wild,2016
+184,Germany,Helen,helen,53.8338371,10.7035939,Institute of Systems and Robotics,edu,4a04d4176f231683fd68ccf0c76fcc0c44d05281,citation,http://home.isr.uc.pt/~pedromartins/Publications/pmartins_icip2018.pdf,Simultaneous Cascaded Regression,2018
+185,United States,Helen,helen,34.0224149,-118.28634407,University of Southern California,edu,11fc332bdcc843aad7475bb4566e73a957dffda5,citation,https://arxiv.org/pdf/1805.03356.pdf,SPG-Net: Segmentation Prediction and Guidance Network for Image Inpainting,2018
+186,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,d140c5add2cddd4a572f07358d666fe00e8f4fe1,citation,https://pdfs.semanticscholar.org/d140/c5add2cddd4a572f07358d666fe00e8f4fe1.pdf,Statistically Learned Deformable Eye Models,2014
+187,Australia,Helen,helen,-33.8809651,151.20107299,University of Technology Sydney,edu,77875d6e4d8c7ed3baeb259fd5696e921f59d7ad,citation,https://arxiv.org/pdf/1803.04108.pdf,Style Aggregated Network for Facial Landmark Detection,2018
+188,Germany,Helen,helen,50.7791703,6.06728733,RWTH Aachen University,edu,d32b155138dafd0a9099980eceec6081ab51b861,citation,https://arxiv.org/pdf/1902.03459.pdf,Super-realtime facial landmark detection and shape fitting by deep regression of shape model parameters,2019
+189,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,59d8fa6fd91cdb72cd0fa74c04016d79ef5a752b,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Zafeiriou_The_Menpo_Facial_CVPR_2017_paper.pdf,The Menpo Facial Landmark Localisation Challenge: A Step Towards the Solution,2017
+190,Sweden,Helen,helen,55.7039571,13.1902011,Lund University,edu,995d55fdf5b6fe7fb630c93a424700d4bc566104,citation,http://openaccess.thecvf.com/content_iccv_2015/papers/Nilsson_The_One_Triangle_ICCV_2015_paper.pdf,The One Triangle Three Parallelograms Sampling Strategy and Its Application in Shape Regression,2015
+191,United Kingdom,Helen,helen,52.9387428,-1.20029569,University of Nottingham,edu,671bfefb22d2044ab3e4402703bb88a10a7da78a,citation,https://arxiv.org/pdf/1811.03492.pdf,Triple consistency loss for pairing distributions in GAN-based face synthesis.,2018
+192,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,5c124b57699be19cd4eb4e1da285b4a8c84fc80d,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Zhao_Unified_Face_Analysis_2014_CVPR_paper.pdf,Unified Face Analysis by Iterative Multi-output Random Forests,2014
+193,France,Helen,helen,49.3849757,1.0683257,"INSA Rouen, France",edu,891b10c4b3b92ca30c9b93170ec9abd71f6099c4,citation,https://pdfs.semanticscholar.org/891b/10c4b3b92ca30c9b93170ec9abd71f6099c4.pdf,2 New Statement for Structured Output Regression Problems,2015
+194,France,Helen,helen,49.4583047,1.0688892,Rouen University,edu,891b10c4b3b92ca30c9b93170ec9abd71f6099c4,citation,https://pdfs.semanticscholar.org/891b/10c4b3b92ca30c9b93170ec9abd71f6099c4.pdf,2 New Statement for Structured Output Regression Problems,2015
+195,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,e4754afaa15b1b53e70743880484b8d0736990ff,citation,http://ibug.doc.ic.ac.uk/media/uploads/documents/1-s2.0-s0262885616000147-main.pdf,300 Faces In-The-Wild Challenge: database and results,2016
+196,United Kingdom,Helen,helen,52.9387428,-1.20029569,University of Nottingham,edu,e4754afaa15b1b53e70743880484b8d0736990ff,citation,http://ibug.doc.ic.ac.uk/media/uploads/documents/1-s2.0-s0262885616000147-main.pdf,300 Faces In-The-Wild Challenge: database and results,2016
+197,Netherlands,Helen,helen,52.2380139,6.8566761,University of Twente,edu,e4754afaa15b1b53e70743880484b8d0736990ff,citation,http://ibug.doc.ic.ac.uk/media/uploads/documents/1-s2.0-s0262885616000147-main.pdf,300 Faces In-The-Wild Challenge: database and results,2016
+198,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,303065c44cf847849d04da16b8b1d9a120cef73a,citation,https://arxiv.org/pdf/1701.05360.pdf,"3D Face Morphable Models ""In-the-Wild""",2017
+199,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,2e3d081c8f0e10f138314c4d2c11064a981c1327,citation,https://arxiv.org/pdf/1603.06015.pdf,A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”,2017
+200,Italy,Helen,helen,40.3515155,18.1750161,"National Research Council of Italy, Institute of Applied Sciences and Intelligent Systems, Lecce, Italy",edu,6e38011e38a1c893b90a48e8f8eae0e22d2008e8,citation,http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w22/Del_Coco_A_Computer_Vision_ICCV_2017_paper.pdf,A Computer Vision Based Approach for Understanding Emotional Involvements in Children with Autism Spectrum Disorders,2017
+201,United States,Helen,helen,40.4441619,-79.94272826,Carnegie Mellon University,edu,131e395c94999c55c53afead65d81be61cd349a4,citation,https://arxiv.org/pdf/1612.02203.pdf,A Functional Regression Approach to Facial Landmark Tracking,2018
+202,United Kingdom,Helen,helen,52.9387428,-1.20029569,University of Nottingham,edu,131e395c94999c55c53afead65d81be61cd349a4,citation,https://arxiv.org/pdf/1612.02203.pdf,A Functional Regression Approach to Facial Landmark Tracking,2018
+203,France,Helen,helen,49.4583047,1.0688892,Normandie University,edu,2df4d05119fe3fbf1f8112b3ad901c33728b498a,citation,https://pdfs.semanticscholar.org/2df4/d05119fe3fbf1f8112b3ad901c33728b498a.pdf,A regularization scheme for structured output problems : an application to facial landmark detection,2016
+204,United States,Helen,helen,40.00471095,-83.02859368,Ohio State University,edu,9993f1a7cfb5b0078f339b9a6bfa341da76a3168,citation,https://arxiv.org/pdf/1609.09058.pdf,"A Simple, Fast and Highly-Accurate Algorithm to Recover 3D Shape from 2D Landmarks on a Single Image",2018
+205,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,5f5906168235613c81ad2129e2431a0e5ef2b6e4,citation,https://arxiv.org/pdf/1601.00199.pdf,A Unified Framework for Compositional Fitting of Active Appearance Models,2016
+206,France,Helen,helen,49.4583047,1.0688892,Rouen University,edu,0b0958493e43ca9c131315bcfb9a171d52ecbb8a,citation,https://pdfs.semanticscholar.org/0b09/58493e43ca9c131315bcfb9a171d52ecbb8a.pdf,A Unified Neural Based Model for Structured Output Problems,2015
+207,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,b730908bc1f80b711c031f3ea459e4de09a3d324,citation,http://ibug.doc.ic.ac.uk/media/uploads/documents/tifs_aoms.pdf,Active Orientation Models for Face Alignment In-the-Wild,2014
+208,United Kingdom,Helen,helen,53.22853665,-0.54873472,University of Lincoln,edu,b730908bc1f80b711c031f3ea459e4de09a3d324,citation,http://ibug.doc.ic.ac.uk/media/uploads/documents/tifs_aoms.pdf,Active Orientation Models for Face Alignment In-the-Wild,2014
+209,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,293ade202109c7f23637589a637bdaed06dc37c9,citation,http://ibug.doc.ic.ac.uk/media/uploads/documents/antonakos2016adaptive.pdf,Adaptive cascaded regression,2016
+210,Finland,Helen,helen,65.0592157,25.46632601,University of Oulu,edu,293ade202109c7f23637589a637bdaed06dc37c9,citation,http://ibug.doc.ic.ac.uk/media/uploads/documents/antonakos2016adaptive.pdf,Adaptive cascaded regression,2016
+211,Australia,Helen,helen,-34.920603,138.6062277,Adelaide University,edu,45e7ddd5248977ba8ec61be111db912a4387d62f,citation,https://arxiv.org/pdf/1711.00253.pdf,Adversarial Learning of Structure-Aware Fully Convolutional Networks for Landmark Localization,2017
+212,China,Helen,helen,32.0565957,118.77408833,Nanjing University,edu,45e7ddd5248977ba8ec61be111db912a4387d62f,citation,https://arxiv.org/pdf/1711.00253.pdf,Adversarial Learning of Structure-Aware Fully Convolutional Networks for Landmark Localization,2017
+213,China,Helen,helen,32.035225,118.855317,Nanjing University of Science & Technology,edu,45e7ddd5248977ba8ec61be111db912a4387d62f,citation,https://arxiv.org/pdf/1711.00253.pdf,Adversarial Learning of Structure-Aware Fully Convolutional Networks for Landmark Localization,2017
+214,United States,Helen,helen,38.99203005,-76.9461029,University of Maryland College Park,edu,3504907a2e3c81d78e9dfe71c93ac145b1318f9c,citation,https://arxiv.org/pdf/1605.02686.pdf,An End-to-End System for Unconstrained Face Verification with Deep Convolutional Neural Networks,2015
+215,United States,Helen,helen,39.738444,-84.17918747,University of Dayton,edu,1f9ae272bb4151817866511bd970bffb22981a49,citation,https://arxiv.org/pdf/1709.03170.pdf,An Iterative Regression Approach for Face Pose Estimation from RGB Images,2017
+216,China,Helen,helen,40.0044795,116.370238,Chinese Academy of Sciences,edu,86c053c162c08bc3fe093cc10398b9e64367a100,citation,https://pdfs.semanticscholar.org/86c0/53c162c08bc3fe093cc10398b9e64367a100.pdf,Cascade of forests for face alignment,2015
+217,United Kingdom,Helen,helen,51.5247272,-0.03931035,Queen Mary University of London,edu,86c053c162c08bc3fe093cc10398b9e64367a100,citation,https://pdfs.semanticscholar.org/86c0/53c162c08bc3fe093cc10398b9e64367a100.pdf,Cascade of forests for face alignment,2015
+218,United Kingdom,Helen,helen,52.9387428,-1.20029569,University of Nottingham,edu,056ba488898a1a1b32daec7a45e0d550e0c51ae4,citation,https://arxiv.org/pdf/1608.01137.pdf,Cascaded Continuous Regression for Real-Time Incremental Face Tracking,2016
+219,United States,Helen,helen,43.07982815,-89.43066425,University of Wisconsin Madison,edu,2e091b311ac48c18aaedbb5117e94213f1dbb529,citation,http://pages.cs.wisc.edu/~lizhang/projects/collab-face-landmarks/SmithECCV2014.pdf,Collaborative Facial Landmark Localization for Transferring Annotations Across Datasets,2014
+220,China,Helen,helen,40.0044795,116.370238,Chinese Academy of Sciences,edu,faead8f2eb54c7bc33bc7d0569adc7a4c2ec4c3b,citation,https://arxiv.org/pdf/1611.10152.pdf,Combining Data-Driven and Model-Driven Methods for Robust Facial Landmark Detection,2018
+221,Canada,Helen,helen,45.3290959,-75.6619858,"National Research Council, Italy",edu,08ecc281cdf954e405524287ee5920e7c4fb597e,citation,https://pdfs.semanticscholar.org/08ec/c281cdf954e405524287ee5920e7c4fb597e.pdf,Computational Assessment of Facial Expression Production in ASD Children,2018
+222,United Kingdom,Helen,helen,51.5247272,-0.03931035,Queen Mary University of London,edu,dee406a7aaa0f4c9d64b7550e633d81bc66ff451,citation,https://arxiv.org/pdf/1710.01453.pdf,Content-Adaptive Sketch Portrait Generation by Decompositional Representation Learning,2017
+223,China,Helen,helen,23.09461185,113.28788994,Sun Yat-Sen University,edu,dee406a7aaa0f4c9d64b7550e633d81bc66ff451,citation,https://arxiv.org/pdf/1710.01453.pdf,Content-Adaptive Sketch Portrait Generation by Decompositional Representation Learning,2017
+224,United Kingdom,Helen,helen,52.17638955,0.14308882,University of Cambridge,edu,029b53f32079063047097fa59cfc788b2b550c4b,citation,https://pdfs.semanticscholar.org/f4e3/c42df13aeed9196647d4e3fe0f84fa725252.pdf,Continuous Conditional Neural Fields for Structured Regression,2014
+225,United States,Helen,helen,34.0224149,-118.28634407,University of Southern California,edu,029b53f32079063047097fa59cfc788b2b550c4b,citation,https://pdfs.semanticscholar.org/f4e3/c42df13aeed9196647d4e3fe0f84fa725252.pdf,Continuous Conditional Neural Fields for Structured Regression,2014
+226,United States,Helen,helen,40.4441619,-79.94272826,Carnegie Mellon University,edu,88e2efab01e883e037a416c63a03075d66625c26,citation,http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w36/Zadeh_Convolutional_Experts_Constrained_ICCV_2017_paper.pdf,Convolutional Experts Constrained Local Model for 3D Facial Landmark Detection,2017
+227,Sweden,Helen,helen,59.34986645,18.07063213,"KTH Royal Institute of Technology, Stockholm",edu,656a59954de3c9fcf82ffcef926af6ade2f3fdb5,citation,https://pdfs.semanticscholar.org/656a/59954de3c9fcf82ffcef926af6ade2f3fdb5.pdf,Convolutional Network Representation for Visual Recognition,2017
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+231,United States,Helen,helen,45.57022705,-122.63709346,Concordia University,edu,5239001571bc64de3e61be0be8985860f08d7e7e,citation,https://arxiv.org/pdf/1607.06871.pdf,Deep Appearance Models: A Deep Boltzmann Machine Approach for Face Modeling,2016
+232,United States,Helen,helen,37.3239177,-122.0129693,"NEC Labs, Cupertino, CA",company,61f04606528ecf4a42b49e8ac2add2e9f92c0def,citation,https://arxiv.org/pdf/1605.01014.pdf,Deep Deformation Network for Object Landmark Localization,2016
+233,France,Helen,helen,49.4583047,1.0688892,Normandie University,edu,9ca7899338129f4ba6744f801e722d53a44e4622,citation,https://arxiv.org/pdf/1504.07550.pdf,Deep neural networks regularization for structured output prediction,2018
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+239,United States,Helen,helen,28.0599999,-82.41383619,University of South Florida,edu,b07582d1a59a9c6f029d0d8328414c7bef64dca0,citation,https://arxiv.org/pdf/1710.07662.pdf,Employing Fusion of Learned and Handcrafted Features for Unconstrained Ear Recognition,2018
+240,Spain,Helen,helen,41.5008957,2.111553,Autonomous University of Barcelona,edu,a40f8881a36bc01f3ae356b3e57eac84e989eef0,citation,https://arxiv.org/pdf/1703.03305.pdf,"End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks",2017
+241,Netherlands,Helen,helen,51.816701,5.865272,Radboud University Nijmegen,edu,a40f8881a36bc01f3ae356b3e57eac84e989eef0,citation,https://arxiv.org/pdf/1703.03305.pdf,"End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks",2017
+242,Spain,Helen,helen,41.40657415,2.1945341,Universitat Oberta de Catalunya,edu,a40f8881a36bc01f3ae356b3e57eac84e989eef0,citation,https://arxiv.org/pdf/1703.03305.pdf,"End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks",2017
+243,United States,Helen,helen,34.0224149,-118.28634407,University of Southern California,edu,49258cc3979103681848284470056956b77caf80,citation,https://5443dcab-a-62cb3a1a-s-sites.googlegroups.com/site/tuftsyuewu/epat-euclidean-perturbation.pdf?attachauth=ANoY7crlk9caZscfn0KRjed81DVoV-Ec6ZHI7txQrJiM_NBic36WKIg-ODwefcBtfgfKdS1iX28MlSXNyB7pE0D7opPjlGqxBVVa1UuIiydhFOgkXlXGfrYqSPS6749JeYWDkfvwWraRfB_CK8bu77jAEA2sIVNgaVRa_7zvmzwnstLwSUowbYC1LRc5yDt8ieT_jdEb_TuhMgR2j03BdHgyUkVjl0TXRukYHWglDOxzHAKwj0vsb4U%3D&attredirects=0,EPAT: Euclidean Perturbation Analysis and Transform - An Agnostic Data Adaptation Framework for Improving Facial Landmark Detectors,2017
+244,United States,Helen,helen,37.3307703,-121.8940951,Adobe,company,992ebd81eb448d1eef846bfc416fc929beb7d28b,citation,https://pdfs.semanticscholar.org/992e/bd81eb448d1eef846bfc416fc929beb7d28b.pdf,Exemplar-Based Face Parsing Supplementary Material,2013
+245,United States,Helen,helen,43.07982815,-89.43066425,University of Wisconsin Madison,edu,992ebd81eb448d1eef846bfc416fc929beb7d28b,citation,https://pdfs.semanticscholar.org/992e/bd81eb448d1eef846bfc416fc929beb7d28b.pdf,Exemplar-Based Face Parsing Supplementary Material,2013
+246,China,Helen,helen,35.86166,104.195397,"Megvii Inc. (Face++), China",company,1a8ccc23ed73db64748e31c61c69fe23c48a2bb1,citation,http://www.cv-foundation.org/openaccess/content_iccv_workshops_2013/W11/papers/Zhou_Extensive_Facial_Landmark_2013_ICCV_paper.pdf,Extensive Facial Landmark Localization with Coarse-to-Fine Convolutional Network Cascade,2013
+247,United States,Helen,helen,40.4441619,-79.94272826,Carnegie Mellon University,edu,6d8c9a1759e7204eacb4eeb06567ad0ef4229f93,citation,https://arxiv.org/pdf/1707.05938.pdf,"Face Alignment Robust to Pose, Expressions and Occlusions",2016
+248,United States,Helen,helen,42.718568,-84.47791571,Michigan State University,edu,6d8c9a1759e7204eacb4eeb06567ad0ef4229f93,citation,https://arxiv.org/pdf/1707.05938.pdf,"Face Alignment Robust to Pose, Expressions and Occlusions",2016
+249,Poland,Helen,helen,52.22165395,21.00735776,Warsaw University of Technology,edu,eb48a58b873295d719827e746d51b110f5716d6c,citation,https://arxiv.org/pdf/1706.01820.pdf,Face Alignment Using K-Cluster Regression Forests With Weighted Splitting,2016
+250,France,Helen,helen,48.8507603,2.3412757,"Sorbonne Universités, Paris, France",edu,31e57fa83ac60c03d884774d2b515813493977b9,citation,https://arxiv.org/pdf/1703.01597.pdf,Face Alignment with Cascaded Semi-Parametric Deep Greedy Neural Forests,2018
+251,United States,Helen,helen,30.44235995,-84.29747867,Florida State University,edu,9207671d9e2b668c065e06d9f58f597601039e5e,citation,https://pdfs.semanticscholar.org/9207/671d9e2b668c065e06d9f58f597601039e5e.pdf,Face Detection Using a 3D Model on Face Keypoints,2014
+252,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,bc704680b5032eadf78c4e49f548ba14040965bf,citation,http://openaccess.thecvf.com/content_cvpr_2017/papers/Trigeorgis_Face_Normals_In-The-Wild_CVPR_2017_paper.pdf,"Face Normals ""In-the-Wild"" Using Fully Convolutional Networks",2017
+253,United Kingdom,Helen,helen,51.5231607,-0.1282037,University College London,edu,bc704680b5032eadf78c4e49f548ba14040965bf,citation,http://openaccess.thecvf.com/content_cvpr_2017/papers/Trigeorgis_Face_Normals_In-The-Wild_CVPR_2017_paper.pdf,"Face Normals ""In-the-Wild"" Using Fully Convolutional Networks",2017
+254,China,Helen,helen,23.09461185,113.28788994,Sun Yat-Sen University,edu,a4ce0f8cfa7d9aa343cb30b0792bb379e20ef41b,citation,https://arxiv.org/pdf/1812.03887.pdf,Facial Landmark Machines: A Backbone-Branches Architecture with Progressive Representation Learning,2018
+255,China,Helen,helen,22.2081469,114.25964115,University of Hong Kong,edu,a4ce0f8cfa7d9aa343cb30b0792bb379e20ef41b,citation,https://arxiv.org/pdf/1812.03887.pdf,Facial Landmark Machines: A Backbone-Branches Architecture with Progressive Representation Learning,2018
+256,Israel,Helen,helen,32.06932925,34.84334339,Bar-Ilan University,edu,e4f032ee301d4a4b3d598e6fa6cffbcdb9cdfdd1,citation,https://arxiv.org/pdf/1805.01760.pdf,Facial Landmark Point Localization using Coarse-to-Fine Deep Recurrent Neural Network,2018
+257,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,ebedc841a2c1b3a9ab7357de833101648281ff0e,citation,http://ibug.doc.ic.ac.uk/media/uploads/documents/1-s2.0-s0262885615000116-main.pdf,Facial landmarking for in-the-wild images with local inference based on global appearance,2015
+258,Netherlands,Helen,helen,52.2380139,6.8566761,University of Twente,edu,ebedc841a2c1b3a9ab7357de833101648281ff0e,citation,http://ibug.doc.ic.ac.uk/media/uploads/documents/1-s2.0-s0262885615000116-main.pdf,Facial landmarking for in-the-wild images with local inference based on global appearance,2015
+259,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,375435fb0da220a65ac9e82275a880e1b9f0a557,citation,http://eprints.lincoln.ac.uk/17528/7/__ddat02_staffhome_jpartridge_tzimiroTPAMI15.pdf,From Pixels to Response Maps: Discriminative Image Filtering for Face Alignment in the Wild,2015
+260,Netherlands,Helen,helen,52.2380139,6.8566761,University of Twente,edu,375435fb0da220a65ac9e82275a880e1b9f0a557,citation,http://eprints.lincoln.ac.uk/17528/7/__ddat02_staffhome_jpartridge_tzimiroTPAMI15.pdf,From Pixels to Response Maps: Discriminative Image Filtering for Face Alignment in the Wild,2015
+261,China,Helen,helen,31.30104395,121.50045497,Fudan University,edu,37381718559f767fc496cc34ceb98ff18bc7d3e1,citation,https://pdfs.semanticscholar.org/3738/1718559f767fc496cc34ceb98ff18bc7d3e1.pdf,Harnessing Synthesized Abstraction Images to Improve Facial Attribute Recognition,2018
+262,China,Helen,helen,31.19884,121.432567,Jiaotong University,edu,37381718559f767fc496cc34ceb98ff18bc7d3e1,citation,https://pdfs.semanticscholar.org/3738/1718559f767fc496cc34ceb98ff18bc7d3e1.pdf,Harnessing Synthesized Abstraction Images to Improve Facial Attribute Recognition,2018
+263,Spain,Helen,helen,42.797263,-1.6321518,Public University of Navarra,edu,8c0a47c61143ceb5bbabef403923e4bf92fb854d,citation,http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w22/Larumbe_Improved_Strategies_for_ICCV_2017_paper.pdf,Improved Strategies for HPE Employing Learning-by-Synthesis Approaches,2017
+264,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,3352426a67eabe3516812cb66a77aeb8b4df4d1b,citation,https://arxiv.org/pdf/1708.06023.pdf,Joint Multi-view Face Alignment in the Wild,2017
+265,China,Helen,helen,22.4162632,114.2109318,Chinese University of Hong Kong,edu,390f3d7cdf1ce127ecca65afa2e24c563e9db93b,citation,https://pdfs.semanticscholar.org/6e80/a3558f9170f97c103137ea2e18ddd782e8d7.pdf,Learning and Transferring Multi-task Deep Representation for Face Alignment,2014
+266,China,Helen,helen,40.00229045,116.32098908,Tsinghua University,edu,df80fed59ffdf751a20af317f265848fe6bfb9c9,citation,http://ivg.au.tsinghua.edu.cn/paper/2017_Learning%20deep%20sharable%20and%20structural%20detectors%20for%20face%20alignment.pdf,Learning Deep Sharable and Structural Detectors for Face Alignment,2017
+267,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,d9deafd9d9e60657a7f34df5f494edff546c4fb8,citation,http://openaccess.thecvf.com/content_cvpr_2017/papers/Wang_Learning_the_Multilinear_CVPR_2017_paper.pdf,Learning the Multilinear Structure of Visual Data,2017
+268,Canada,Helen,helen,45.504384,-73.6128829,Polytechnique Montréal,edu,4f77a37753c03886ca9c9349723ec3bbfe4ee967,citation,http://www.cv-foundation.org/openaccess/content_iccv_workshops_2013/W11/papers/Hasan_Localizing_Facial_Keypoints_2013_ICCV_paper.pdf,"Localizing Facial Keypoints with Global Descriptor Search, Neighbour Alignment and Locally Linear Models",2013
+269,Canada,Helen,helen,43.66333345,-79.39769975,University of Toronto,edu,4f77a37753c03886ca9c9349723ec3bbfe4ee967,citation,http://www.cv-foundation.org/openaccess/content_iccv_workshops_2013/W11/papers/Hasan_Localizing_Facial_Keypoints_2013_ICCV_paper.pdf,"Localizing Facial Keypoints with Global Descriptor Search, Neighbour Alignment and Locally Linear Models",2013
+270,United States,Helen,helen,38.7768106,-94.9442982,Amazon,company,e7265c560b3f10013bf70aacbbf0eb4631b7e2aa,citation,https://arxiv.org/pdf/1805.10483.pdf,Look at Boundary: A Boundary-Aware Face Alignment Algorithm,2018
+271,China,Helen,helen,39.993008,116.329882,SenseTime,company,e7265c560b3f10013bf70aacbbf0eb4631b7e2aa,citation,https://arxiv.org/pdf/1805.10483.pdf,Look at Boundary: A Boundary-Aware Face Alignment Algorithm,2018
+272,China,Helen,helen,40.00229045,116.32098908,Tsinghua University,edu,e7265c560b3f10013bf70aacbbf0eb4631b7e2aa,citation,https://arxiv.org/pdf/1805.10483.pdf,Look at Boundary: A Boundary-Aware Face Alignment Algorithm,2018
+273,United States,Helen,helen,32.87935255,-117.23110049,"University of California, San Diego",edu,1b0a071450c419138432c033f722027ec88846ea,citation,http://cvrr.ucsd.edu/publications/2016/YuenMartinTrivediITSC2016.pdf,Looking at faces in a vehicle: A deep CNN based approach and evaluation,2016
+274,Iran,Helen,helen,35.704514,51.40972058,Amirkabir University of Technology,edu,6f5ce5570dc2960b8b0e4a0a50eab84b7f6af5cb,citation,https://arxiv.org/pdf/1706.06247.pdf,Low Resolution Face Recognition Using a Two-Branch Deep Convolutional Neural Network Architecture,2017
+275,United States,Helen,helen,42.3583961,-71.09567788,MIT,edu,6f5ce5570dc2960b8b0e4a0a50eab84b7f6af5cb,citation,https://arxiv.org/pdf/1706.06247.pdf,Low Resolution Face Recognition Using a Two-Branch Deep Convolutional Neural Network Architecture,2017
+276,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,47e8db3d9adb79a87c8c02b88f432f911eb45dc5,citation,https://arxiv.org/pdf/1509.05715.pdf,MAGMA: Multilevel Accelerated Gradient Mirror Descent Algorithm for Large-Scale Convex Composite Minimization,2016
+277,United Kingdom,Helen,helen,53.46600455,-2.23300881,University of Manchester,edu,daa4cfde41d37b2ab497458e331556d13dd14d0b,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w25/papers/Rajamanoharan_Multi-View_Constrained_Local_ICCV_2015_paper.pdf,Multi-view Constrained Local Models for Large Head Angle Facial Tracking,2015
+278,China,Helen,helen,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,d03265ea9200a993af857b473c6bf12a095ca178,citation,https://pdfs.semanticscholar.org/d032/65ea9200a993af857b473c6bf12a095ca178.pdf,Multiple deep convolutional neural networks averaging for face alignment,2015
+279,France,Helen,helen,49.3849757,1.0683257,"INSA Rouen, France",edu,0a6a25ee84fc0bf7284f41eaa6fefaa58b5b329a,citation,https://arxiv.org/pdf/1807.05292.pdf,Neural Networks Regularization Through Representation Learning,2018
+280,France,Helen,helen,49.4583047,1.0688892,"LITIS, Université de Rouen, Rouen, France",edu,0a6a25ee84fc0bf7284f41eaa6fefaa58b5b329a,citation,https://arxiv.org/pdf/1807.05292.pdf,Neural Networks Regularization Through Representation Learning,2018
+281,United Kingdom,Helen,helen,53.7641378,-2.7092453,University of Central Lancashire,edu,ef52f1e2b52fd84a7e22226ed67132c6ce47b829,citation,https://pdfs.semanticscholar.org/ef52/f1e2b52fd84a7e22226ed67132c6ce47b829.pdf,Online Eye Status Detection in the Wild with Convolutional Neural Networks,2017
+282,United Kingdom,Helen,helen,50.7944026,-1.0971748,Cambridge University,edu,2fda461869f84a9298a0e93ef280f79b9fb76f94,citation,http://multicomp.cs.cmu.edu/wp-content/uploads/2017/09/2016_WACV_Baltrusaitis_OpenFace.pdf,OpenFace: An open source facial behavior analysis toolkit,2016
+283,United States,Helen,helen,40.4441619,-79.94272826,Carnegie Mellon University,edu,2fda461869f84a9298a0e93ef280f79b9fb76f94,citation,http://multicomp.cs.cmu.edu/wp-content/uploads/2017/09/2016_WACV_Baltrusaitis_OpenFace.pdf,OpenFace: An open source facial behavior analysis toolkit,2016
+284,Sweden,Helen,helen,59.34986645,18.07063213,"KTH Royal Institute of Technology, Stockholm",edu,12d8730da5aab242795bdff17b30b6e0bac82998,citation,https://arxiv.org/pdf/1411.6509.pdf,Persistent Evidence of Local Image Properties in Generic ConvNets,2015
+285,United States,Helen,helen,33.6404952,-117.8442962,UC Irvine,edu,5711400c59a162112c57e9f899147d457537f701,citation,https://pdfs.semanticscholar.org/5711/400c59a162112c57e9f899147d457537f701.pdf,Recognizing and Segmenting Objects in the Presence of Occlusion and Clutter,2016
+286,United States,Helen,helen,41.2097516,-73.8026467,IBM Research T. J. Watson Center,company,ac5d0705a9ddba29151fd539c668ba2c0d16deb6,citation,https://arxiv.org/pdf/1801.06066.pdf,RED-Net: A Recurrent Encoder–Decoder Network for Video-Based Face Alignment,2018
+287,United States,Helen,helen,40.47913175,-74.43168868,Rutgers University,edu,ac5d0705a9ddba29151fd539c668ba2c0d16deb6,citation,https://arxiv.org/pdf/1801.06066.pdf,RED-Net: A Recurrent Encoder–Decoder Network for Video-Based Face Alignment,2018
+288,Singapore,Helen,helen,1.3484104,103.68297965,Nanyang Technological University,edu,2bfccbf6f4e88a92a7b1f2b5c588b68c5fa45a92,citation,https://arxiv.org/pdf/1807.11079.pdf,ReenactGAN: Learning to Reenact Faces via Boundary Transfer,2018
+289,China,Helen,helen,39.993008,116.329882,SenseTime,company,2bfccbf6f4e88a92a7b1f2b5c588b68c5fa45a92,citation,https://arxiv.org/pdf/1807.11079.pdf,ReenactGAN: Learning to Reenact Faces via Boundary Transfer,2018
+290,Italy,Helen,helen,46.0658836,11.1159894,University of Trento,edu,f61829274cfe64b94361e54351f01a0376cd1253,citation,http://openaccess.thecvf.com/content_iccv_2015/papers/Tulyakov_Regressing_a_3D_ICCV_2015_paper.pdf,Regressing a 3D Face Shape from a Single Image,2015
+291,Singapore,Helen,helen,1.3484104,103.68297965,Nanyang Technological University,edu,4d23bb65c6772cb374fc05b1f10dedf9b43e63cf,citation,https://pdfs.semanticscholar.org/4d23/bb65c6772cb374fc05b1f10dedf9b43e63cf.pdf,Robust face alignment and partial face recognition,2016
+292,United States,Helen,helen,34.13710185,-118.12527487,California Institute of Technology,edu,2724ba85ec4a66de18da33925e537f3902f21249,citation,,Robust Face Landmark Estimation under Occlusion,2013
+293,United States,Helen,helen,47.6423318,-122.1369302,Microsoft,company,2724ba85ec4a66de18da33925e537f3902f21249,citation,,Robust Face Landmark Estimation under Occlusion,2013
+294,United States,Helen,helen,42.7298459,-73.67950216,Rensselaer Polytechnic Institute,edu,1c1f957d85b59d23163583c421755869f248ceef,citation,https://arxiv.org/pdf/1709.08127.pdf,Robust Facial Landmark Detection Under Significant Head Poses and Occlusion,2015
+295,Germany,Helen,helen,48.263011,11.666857,Technical University of Munich,edu,1121873326ab0c9f324b004aa0970a31d4f83eb8,citation,http://openaccess.thecvf.com/content_cvpr_2018/papers/Merget_Robust_Facial_Landmark_CVPR_2018_paper.pdf,Robust Facial Landmark Detection via a Fully-Convolutional Local-Global Context Network,2018
+296,United States,Helen,helen,42.7298459,-73.67950216,Rensselaer Polytechnic Institute,edu,c3d3d2229500c555c7a7150a8b126ef874cbee1c,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w25/papers/Wu_Shape_Augmented_Regression_ICCV_2015_paper.pdf,Shape Augmented Regression Method for Face Alignment,2015
+297,Canada,Helen,helen,43.66333345,-79.39769975,University of Toronto,edu,33ae696546eed070717192d393f75a1583cd8e2c,citation,https://arxiv.org/pdf/1708.08508.pdf,Subspace selection to suppress confounding source domain information in AAM transfer learning,2017
+298,Finland,Helen,helen,65.0592157,25.46632601,University of Oulu,edu,f3745aa4a723d791d3a04ddf7a5546e411226459,citation,,The Menpo Benchmark for Multi-pose 2D and 3D Facial Landmark Localisation and Tracking,2018
+299,United Kingdom,Helen,helen,51.59029705,-0.22963221,Middlesex University,edu,f3745aa4a723d791d3a04ddf7a5546e411226459,citation,,The Menpo Benchmark for Multi-pose 2D and 3D Facial Landmark Localisation and Tracking,2018
+300,United Kingdom,Helen,helen,50.7369302,-3.53647672,University of Exeter,edu,f3745aa4a723d791d3a04ddf7a5546e411226459,citation,,The Menpo Benchmark for Multi-pose 2D and 3D Facial Landmark Localisation and Tracking,2018
+301,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,f3745aa4a723d791d3a04ddf7a5546e411226459,citation,,The Menpo Benchmark for Multi-pose 2D and 3D Facial Landmark Localisation and Tracking,2018
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+303,Germany,Helen,helen,49.10184375,8.4331256,Karlsruhe Institute of Technology,edu,50ccc98d9ce06160cdf92aaf470b8f4edbd8b899,citation,http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W08/papers/Qu_Towards_Robust_Cascaded_2015_CVPR_paper.pdf,Towards robust cascaded regression for face alignment in the wild,2015
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+305,Poland,Helen,helen,52.22165395,21.00735776,Warsaw University of Technology,edu,e52272f92fa553687f1ac068605f1de929efafc2,citation,https://repo.pw.edu.pl/docstore/download/WUT8aeb20bbb6964b7da1cfefbf2e370139/1-s2.0-S0952197617301227-main.pdf,Using a Probabilistic Neural Network for lip-based biometric verification,2017
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+308,China,Helen,helen,40.0044795,116.370238,Chinese Academy of Sciences,edu,708f4787bec9d7563f4bb8b33834de445147133b,citation,http://openaccess.thecvf.com/content_ICCV_2017/papers/Huang_Wavelet-SRNet_A_Wavelet-Based_ICCV_2017_paper.pdf,Wavelet-SRNet: A Wavelet-Based CNN for Multi-scale Face Super Resolution,2017
+309,United Kingdom,Helen,helen,51.49887085,-0.17560797,Imperial College London,edu,044d9a8c61383312cdafbcc44b9d00d650b21c70,citation,,300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge,2013
+310,United Kingdom,Helen,helen,53.22853665,-0.54873472,University of Lincoln,edu,044d9a8c61383312cdafbcc44b9d00d650b21c70,citation,,300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge,2013
+311,Netherlands,Helen,helen,52.2380139,6.8566761,University of Twente,edu,044d9a8c61383312cdafbcc44b9d00d650b21c70,citation,,300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge,2013
+312,Italy,Helen,helen,46.0658836,11.1159894,University of Trento,edu,b48d3694a8342b6efc18c9c9124c62406e6bf3b3,citation,,Recurrent Convolutional Shape Regression,2018
+313,United States,Helen,helen,33.9850469,-118.4694832,"Snapchat Research, Venice, CA",company,b48d3694a8342b6efc18c9c9124c62406e6bf3b3,citation,,Recurrent Convolutional Shape Regression,2018
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+315,Italy,Helen,helen,38.1937335,15.5542057,"National Research Council of Italy, Institute of Applied Sciences and Intelligent Systems, Messina, Italy",edu,523db6dee0e60a2d513759fa04aa96f2fed40ff4,citation,,Study of Mechanisms of Social Interaction Stimulation in Autism Spectrum Disorder by Assisted Humanoid Robot,2018
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+317,United States,Helen,helen,37.3936717,-122.0807262,Facebook,company,95f12d27c3b4914e0668a268360948bce92f7db3,citation,https://pdfs.semanticscholar.org/95f1/2d27c3b4914e0668a268360948bce92f7db3.pdf,Interactive Facial Feature Localization,2012
+318,United States,Helen,helen,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,95f12d27c3b4914e0668a268360948bce92f7db3,citation,https://pdfs.semanticscholar.org/95f1/2d27c3b4914e0668a268360948bce92f7db3.pdf,Interactive Facial Feature Localization,2012
+319,United States,Helen,helen,33.9832526,-118.40417,USC,edu,0a34fe39e9938ae8c813a81ae6d2d3a325600e5c,citation,https://arxiv.org/pdf/1708.07517.pdf,FacePoseNet: Making a Case for Landmark-Free Face Alignment,2017
+320,Israel,Helen,helen,32.77824165,34.99565673,Open University of Israel,edu,0a34fe39e9938ae8c813a81ae6d2d3a325600e5c,citation,https://arxiv.org/pdf/1708.07517.pdf,FacePoseNet: Making a Case for Landmark-Free Face Alignment,2017
+321,United Kingdom,Helen,helen,52.9387428,-1.20029569,University of Nottingham,edu,c46a4db7247d26aceafed3e4f38ce52d54361817,citation,https://arxiv.org/pdf/1609.09642.pdf,A CNN Cascade for Landmark Guided Semantic Part Segmentation,2016
+322,United States,Helen,helen,38.9869183,-76.9425543,"Maryland Univ., College Park, MD, USA",edu,59b6e9320a4e1de9216c6fc49b4b0309211b17e8,citation,https://pdfs.semanticscholar.org/59b6/e9320a4e1de9216c6fc49b4b0309211b17e8.pdf,Robust Representations for unconstrained Face Recognition and its Applications,2016