path: root/site/datasets/verified/lfpw.csv

id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year
0,,LFPW,lfpw,0.0,0.0,,,,main,,Localizing Parts of Faces Using a Consensus of Exemplars,2011
1,China,LFPW,lfpw,28.2290209,112.99483204,"National University of Defense Technology, China",mil,ac51d9ddbd462d023ec60818bac6cdae83b66992,citation,http://downloads.hindawi.com/journals/cin/2015/709072.pdf,An Efficient Robust Eye Localization by Learning the Convolution Distribution Using Eye Template,2015
2,United Kingdom,LFPW,lfpw,52.9387428,-1.20029569,University of Nottingham,edu,529b1f33aed49dbe025a99ac1d211c777ad881ec,citation,http://eprints.eemcs.utwente.nl/26776/01/Pantic_Fast_and_Exact_Bi-Directional_Fitting.pdf,Fast and exact bi-directional fitting of active appearance models,2015
3,Netherlands,LFPW,lfpw,52.2380139,6.8566761,University of Twente,edu,529b1f33aed49dbe025a99ac1d211c777ad881ec,citation,http://eprints.eemcs.utwente.nl/26776/01/Pantic_Fast_and_Exact_Bi-Directional_Fitting.pdf,Fast and exact bi-directional fitting of active appearance models,2015
4,China,LFPW,lfpw,31.32235655,121.38400941,Shanghai University,edu,63fd7a159e58add133b9c71c4b1b37b899dd646f,citation,http://wei-shen.weebly.com/uploads/2/3/8/2/23825939/posecorrection.pdf,Exemplar-Based Human Action Pose Correction,2014
5,China,LFPW,lfpw,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,63fd7a159e58add133b9c71c4b1b37b899dd646f,citation,http://wei-shen.weebly.com/uploads/2/3/8/2/23825939/posecorrection.pdf,Exemplar-Based Human Action Pose Correction,2014
6,United States,LFPW,lfpw,47.6423318,-122.1369302,Microsoft,company,63fd7a159e58add133b9c71c4b1b37b899dd646f,citation,http://wei-shen.weebly.com/uploads/2/3/8/2/23825939/posecorrection.pdf,Exemplar-Based Human Action Pose Correction,2014
7,United States,LFPW,lfpw,42.3614256,-71.0812092,Microsoft Research Asia,company,63fd7a159e58add133b9c71c4b1b37b899dd646f,citation,http://wei-shen.weebly.com/uploads/2/3/8/2/23825939/posecorrection.pdf,Exemplar-Based Human Action Pose Correction,2014
8,China,LFPW,lfpw,22.4162632,114.2109318,Chinese University of Hong Kong,edu,57ebeff9273dea933e2a75c306849baf43081a8c,citation,http://mmlab.ie.cuhk.edu.hk/archive/CNN/data/CNN_FacePoint.pdf,Deep Convolutional Network Cascade for Facial Point Detection,2013
9,China,LFPW,lfpw,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,57ebeff9273dea933e2a75c306849baf43081a8c,citation,http://mmlab.ie.cuhk.edu.hk/archive/CNN/data/CNN_FacePoint.pdf,Deep Convolutional Network Cascade for Facial Point Detection,2013
10,Canada,LFPW,lfpw,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
11,Canada,LFPW,lfpw,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
12,United Kingdom,LFPW,lfpw,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
13,Canada,LFPW,lfpw,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
14,United States,LFPW,lfpw,37.3936717,-122.0807262,Facebook,company,dcd2ac544a8336d73e4d3d80b158477c783e1e50,citation,https://arxiv.org/pdf/1709.01591.pdf,Improving Landmark Localization with Semi-Supervised Learning,2018
15,United States,LFPW,lfpw,37.3706254,-121.9671894,NVIDIA,company,dcd2ac544a8336d73e4d3d80b158477c783e1e50,citation,https://arxiv.org/pdf/1709.01591.pdf,Improving Landmark Localization with Semi-Supervised Learning,2018
16,Canada,LFPW,lfpw,45.5010087,-73.6157778,University of Montreal,edu,dcd2ac544a8336d73e4d3d80b158477c783e1e50,citation,https://arxiv.org/pdf/1709.01591.pdf,Improving Landmark Localization with Semi-Supervised Learning,2018
17,United Kingdom,LFPW,lfpw,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
18,China,LFPW,lfpw,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
19,United States,LFPW,lfpw,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
20,United Kingdom,LFPW,lfpw,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
21,United Kingdom,LFPW,lfpw,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
22,China,LFPW,lfpw,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
23,China,LFPW,lfpw,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
24,Germany,LFPW,lfpw,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
25,United States,LFPW,lfpw,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
26,United States,LFPW,lfpw,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
27,United States,LFPW,lfpw,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
28,United States,LFPW,lfpw,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
29,China,LFPW,lfpw,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
30,United States,LFPW,lfpw,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
31,United Kingdom,LFPW,lfpw,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
32,United Kingdom,LFPW,lfpw,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
33,United Kingdom,LFPW,lfpw,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
34,United Kingdom,LFPW,lfpw,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
35,Netherlands,LFPW,lfpw,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
36,China,LFPW,lfpw,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
37,China,LFPW,lfpw,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
38,China,LFPW,lfpw,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
39,United Kingdom,LFPW,lfpw,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
40,United Kingdom,LFPW,lfpw,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
41,United Kingdom,LFPW,lfpw,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
42,Netherlands,LFPW,lfpw,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
43,United States,LFPW,lfpw,38.2167565,-85.75725023,University of Louisville,edu,9a4c45e5c6e4f616771a7325629d167a38508691,citation,http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W02/papers/Mostafa_A_Facial_Features_2015_CVPR_paper.pdf,A facial features detector integrating holistic facial information and part-based model,2015
44,Egypt,LFPW,lfpw,31.21051105,29.91314562,Alexandria University,edu,9a4c45e5c6e4f616771a7325629d167a38508691,citation,http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W02/papers/Mostafa_A_Facial_Features_2015_CVPR_paper.pdf,A facial features detector integrating holistic facial information and part-based model,2015
45,Egypt,LFPW,lfpw,27.18794105,31.17009498,Assiut University,edu,9a4c45e5c6e4f616771a7325629d167a38508691,citation,http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W02/papers/Mostafa_A_Facial_Features_2015_CVPR_paper.pdf,A facial features detector integrating holistic facial information and part-based model,2015
46,China,LFPW,lfpw,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
47,United Kingdom,LFPW,lfpw,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
48,China,LFPW,lfpw,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
49,China,LFPW,lfpw,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
50,China,LFPW,lfpw,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
51,United States,LFPW,lfpw,42.3614256,-71.0812092,Microsoft Research Asia,company,63d865c66faaba68018defee0daf201db8ca79ed,citation,https://arxiv.org/pdf/1409.5230.pdf,Deep Regression for Face Alignment,2014
52,United Kingdom,LFPW,lfpw,51.49887085,-0.17560797,Imperial College London,edu,5e9ec3b8daa95d45138e30c07321e386590f8ec7,citation,http://ibug.doc.ic.ac.uk/media/uploads/documents/eleftheriadis_tip.pdf,Discriminative Shared Gaussian Processes for Multiview and View-Invariant Facial Expression Recognition,2015
53,United States,LFPW,lfpw,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
54,China,LFPW,lfpw,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
55,United Kingdom,LFPW,lfpw,52.17638955,0.14308882,University of Cambridge,edu,023be757b1769ecb0db810c95c010310d7daf00b,citation,https://arxiv.org/pdf/1507.03148.pdf,Face Alignment Assisted by Head Pose Estimation,2015
56,United Kingdom,LFPW,lfpw,51.5247272,-0.03931035,Queen Mary University of London,edu,023be757b1769ecb0db810c95c010310d7daf00b,citation,https://arxiv.org/pdf/1507.03148.pdf,Face Alignment Assisted by Head Pose Estimation,2015
57,United States,LFPW,lfpw,42.36782045,-71.12666653,Harvard University,edu,023be757b1769ecb0db810c95c010310d7daf00b,citation,https://arxiv.org/pdf/1507.03148.pdf,Face Alignment Assisted by Head Pose Estimation,2015
58,United Kingdom,LFPW,lfpw,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
59,United Kingdom,LFPW,lfpw,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
60,Netherlands,LFPW,lfpw,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
61,United Kingdom,LFPW,lfpw,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
62,United Kingdom,LFPW,lfpw,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
63,United Kingdom,LFPW,lfpw,53.22853665,-0.54873472,University of Lincoln,edu,6a4ebd91c4d380e21da0efb2dee276897f56467a,citation,http://eprints.nottingham.ac.uk/31441/1/tzimiroICIP14b.pdf,HOG active appearance models,2014
64,United Kingdom,LFPW,lfpw,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
65,Netherlands,LFPW,lfpw,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
66,United Kingdom,LFPW,lfpw,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
67,United States,LFPW,lfpw,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
68,Germany,LFPW,lfpw,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
69,China,LFPW,lfpw,40.0044795,116.370238,Chinese Academy of Sciences,edu,a820941eaf03077d68536732a4d5f28d94b5864a,citation,http://openaccess.thecvf.com/content_iccv_2015/papers/Zhang_Leveraging_Datasets_With_ICCV_2015_paper.pdf,Leveraging Datasets with Varying Annotations for Face Alignment via Deep Regression Network,2015
70,China,LFPW,lfpw,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,a820941eaf03077d68536732a4d5f28d94b5864a,citation,http://openaccess.thecvf.com/content_iccv_2015/papers/Zhang_Leveraging_Datasets_With_ICCV_2015_paper.pdf,Leveraging Datasets with Varying Annotations for Face Alignment via Deep Regression Network,2015
71,Sweden,LFPW,lfpw,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
72,United States,LFPW,lfpw,35.3070929,-80.735164,"North Carolina Univ., Charlotte, NC, USA",edu,3fb3c7dd12561e9443ac301f5527d539b1f4574e,citation,http://research.cs.rutgers.edu/~xiangyu/paper/iccv13_face_final.pdf,Pose-Free Facial Landmark Fitting via Optimized Part Mixtures and Cascaded Deformable Shape Model,2013
73,United States,LFPW,lfpw,40.47913175,-74.43168868,Rutgers University,edu,3fb3c7dd12561e9443ac301f5527d539b1f4574e,citation,http://research.cs.rutgers.edu/~xiangyu/paper/iccv13_face_final.pdf,Pose-Free Facial Landmark Fitting via Optimized Part Mixtures and Cascaded Deformable Shape Model,2013
74,United States,LFPW,lfpw,32.7283683,-97.11201835,University of Texas at Arlington,edu,3fb3c7dd12561e9443ac301f5527d539b1f4574e,citation,http://research.cs.rutgers.edu/~xiangyu/paper/iccv13_face_final.pdf,Pose-Free Facial Landmark Fitting via Optimized Part Mixtures and Cascaded Deformable Shape Model,2013
75,United States,LFPW,lfpw,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
76,United States,LFPW,lfpw,40.4441619,-79.94272826,Carnegie Mellon University,edu,03f98c175b4230960ac347b1100fbfc10c100d0c,citation,http://courses.cs.washington.edu/courses/cse590v/13au/intraface.pdf,Supervised Descent Method and Its Applications to Face Alignment,2013
77,United States,LFPW,lfpw,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
78,United Kingdom,LFPW,lfpw,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
79,United Kingdom,LFPW,lfpw,51.24303255,-0.59001382,University of Surrey,edu,7a0b78879a13bd42c63cd947f583129137b16830,citation,https://pdfs.semanticscholar.org/7a0b/78879a13bd42c63cd947f583129137b16830.pdf,A Multiresolution 3D Morphable Face Model and Fitting Framework,2016
80,Germany,LFPW,lfpw,48.48187645,9.18682404,Reutlingen University,edu,7a0b78879a13bd42c63cd947f583129137b16830,citation,https://pdfs.semanticscholar.org/7a0b/78879a13bd42c63cd947f583129137b16830.pdf,A Multiresolution 3D Morphable Face Model and Fitting Framework,2016
81,United Kingdom,LFPW,lfpw,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
82,United Kingdom,LFPW,lfpw,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
83,United Kingdom,LFPW,lfpw,51.49887085,-0.17560797,Imperial College London,edu,1a85956154c170daf7f15f32f29281269028ff69,citation,http://ibug.doc.ic.ac.uk/media/uploads/documents/active_pictorial_structures.pdf,Active Pictorial Structures,2015
84,United Kingdom,LFPW,lfpw,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
85,Finland,LFPW,lfpw,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
86,China,LFPW,lfpw,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
87,United Kingdom,LFPW,lfpw,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
88,United States,LFPW,lfpw,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
89,United Kingdom,LFPW,lfpw,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
90,United States,LFPW,lfpw,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
91,Italy,LFPW,lfpw,44.4056499,8.946256,"Istituto Italiano di Tecnologia, Genova, Italy",edu,14ff9c89f00dacc8e0c13c94f9fadcd90e4e604d,citation,http://www.hamedkiani.com/uploads/5/1/8/8/51882963/wacv_presentation.pdf,Correlation filter cascade for facial landmark localization,2016
92,Singapore,LFPW,lfpw,1.2962018,103.77689944,National University of Singapore,edu,14ff9c89f00dacc8e0c13c94f9fadcd90e4e604d,citation,http://www.hamedkiani.com/uploads/5/1/8/8/51882963/wacv_presentation.pdf,Correlation filter cascade for facial landmark localization,2016
93,United States,LFPW,lfpw,40.4441619,-79.94272826,Carnegie Mellon University,edu,5239001571bc64de3e61be0be8985860f08d7e7e,citation,https://arxiv.org/pdf/1607.06871.pdf,Deep Appearance Models: A Deep Boltzmann Machine Approach for Face Modeling,2016
94,United States,LFPW,lfpw,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
95,China,LFPW,lfpw,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
96,Singapore,LFPW,lfpw,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
97,United Kingdom,LFPW,lfpw,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
98,United Kingdom,LFPW,lfpw,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
99,United States,LFPW,lfpw,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
100,United Kingdom,LFPW,lfpw,51.49887085,-0.17560797,Imperial College London,edu,2fb8d7601fc3ad637781127620104aaab5122acd,citation,http://ibug.doc.ic.ac.uk/media/uploads/documents/zhou2016estimating.pdf,Estimating Correspondences of Deformable Objects “In-the-Wild”,2016
101,Finland,LFPW,lfpw,65.0592157,25.46632601,University of Oulu,edu,2fb8d7601fc3ad637781127620104aaab5122acd,citation,http://ibug.doc.ic.ac.uk/media/uploads/documents/zhou2016estimating.pdf,Estimating Correspondences of Deformable Objects “In-the-Wild”,2016
102,United States,LFPW,lfpw,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
103,United States,LFPW,lfpw,40.47913175,-74.43168868,Rutgers University,edu,ceeb67bf53ffab1395c36f1141b516f893bada27,citation,https://arxiv.org/pdf/1601.07950.pdf,Face Alignment by Local Deep Descriptor Regression,2016
104,United States,LFPW,lfpw,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
105,United States,LFPW,lfpw,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
106,South Korea,LFPW,lfpw,36.3697191,127.362537,Korea Advanced Institute of Science and Technology,edu,72e10a2a7a65db7ecdc7d9bd3b95a4160fab4114,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/2B_094.pdf,Face alignment using cascade Gaussian process regression trees,2015
107,United Kingdom,LFPW,lfpw,51.5247272,-0.03931035,Queen Mary University of London,edu,4b6387e608afa83ac8d855de2c9b0ae3b86f31cc,citation,http://www.researchgate.net/profile/Heng_Yang3/publication/263813517_Face_Sketch_Landmarks_Localization_in_the_Wild/links/53d3dd3b0cf220632f3ce8b3.pdf,Face Sketch Landmarks Localization in the Wild,2014
108,China,LFPW,lfpw,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,4b6387e608afa83ac8d855de2c9b0ae3b86f31cc,citation,http://www.researchgate.net/profile/Heng_Yang3/publication/263813517_Face_Sketch_Landmarks_Localization_in_the_Wild/links/53d3dd3b0cf220632f3ce8b3.pdf,Face Sketch Landmarks Localization in the Wild,2014
109,United Kingdom,LFPW,lfpw,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
110,Netherlands,LFPW,lfpw,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
111,United Kingdom,LFPW,lfpw,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
112,United Kingdom,LFPW,lfpw,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
113,United Kingdom,LFPW,lfpw,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
114,Netherlands,LFPW,lfpw,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
115,United Kingdom,LFPW,lfpw,51.49887085,-0.17560797,Imperial College London,edu,e42998bbebddeeb4b2bedf5da23fa5c4efc976fa,citation,https://pdfs.semanticscholar.org/e429/98bbebddeeb4b2bedf5da23fa5c4efc976fa.pdf,Generic Active Appearance Models Revisited,2012
116,United Kingdom,LFPW,lfpw,53.22853665,-0.54873472,University of Lincoln,edu,e42998bbebddeeb4b2bedf5da23fa5c4efc976fa,citation,https://pdfs.semanticscholar.org/e429/98bbebddeeb4b2bedf5da23fa5c4efc976fa.pdf,Generic Active Appearance Models Revisited,2012
117,United Kingdom,LFPW,lfpw,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
118,United States,LFPW,lfpw,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
119,United States,LFPW,lfpw,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
120,China,LFPW,lfpw,31.4854255,120.2739581,Jiangnan University,edu,9d57c4036a0e5f1349cd11bc342ac515307b6720,citation,https://arxiv.org/pdf/1808.05399.pdf,Landmark Weighting for 3DMM Shape Fitting,2018
121,United Kingdom,LFPW,lfpw,51.24303255,-0.59001382,University of Surrey,edu,9d57c4036a0e5f1349cd11bc342ac515307b6720,citation,https://arxiv.org/pdf/1808.05399.pdf,Landmark Weighting for 3DMM Shape Fitting,2018
122,China,LFPW,lfpw,40.0044795,116.370238,Chinese Academy of Sciences,edu,321c8ba38db118d8b02c0ba209be709e6792a2c7,citation,http://www.cbsr.ia.ac.cn/users/jjyan/ICCVW2013.pdf,Learn to Combine Multiple Hypotheses for Accurate Face Alignment,2013
123,China,LFPW,lfpw,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
124,China,LFPW,lfpw,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
125,United States,LFPW,lfpw,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
126,China,LFPW,lfpw,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
127,China,LFPW,lfpw,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
128,United Kingdom,LFPW,lfpw,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
129,United States,LFPW,lfpw,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
130,United States,LFPW,lfpw,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
131,China,LFPW,lfpw,31.28473925,121.49694909,Tongji University,edu,7aafeb9aab48fb2c34bed4b86755ac71e3f00338,citation,https://pdfs.semanticscholar.org/7aaf/eb9aab48fb2c34bed4b86755ac71e3f00338.pdf,Real Time 3D Facial Movement Tracking Using a Monocular Camera,2016
132,Japan,LFPW,lfpw,32.8164178,130.72703969,Kumamoto University,edu,7aafeb9aab48fb2c34bed4b86755ac71e3f00338,citation,https://pdfs.semanticscholar.org/7aaf/eb9aab48fb2c34bed4b86755ac71e3f00338.pdf,Real Time 3D Facial Movement Tracking Using a Monocular Camera,2016
133,United States,LFPW,lfpw,45.57022705,-122.63709346,Concordia University,edu,6d0fe30444c6f4e4db3ad8b02fb2c87e2b33c58d,citation,https://arxiv.org/pdf/1607.00659.pdf,Robust Deep Appearance Models,2016
134,United States,LFPW,lfpw,40.4441619,-79.94272826,Carnegie Mellon University,edu,6d0fe30444c6f4e4db3ad8b02fb2c87e2b33c58d,citation,https://arxiv.org/pdf/1607.00659.pdf,Robust Deep Appearance Models,2016
135,China,LFPW,lfpw,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
136,China,LFPW,lfpw,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
137,United Kingdom,LFPW,lfpw,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
138,Netherlands,LFPW,lfpw,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
139,United States,LFPW,lfpw,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
140,United States,LFPW,lfpw,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
141,China,LFPW,lfpw,22.304572,114.17976285,Hong Kong Polytechnic University,edu,3c88ffb74573c87c994106b3ae164f316182fc2c,citation,https://opus.lib.uts.edu.au/bitstream/10453/43334/1/SAC-AAM_v10_Huiling_20151023_modifiedVersion.pdf,Shape-appearance-correlated active appearance model,2016
142,Australia,LFPW,lfpw,-33.8840504,151.1992254,University of Technology,edu,3c88ffb74573c87c994106b3ae164f316182fc2c,citation,https://opus.lib.uts.edu.au/bitstream/10453/43334/1/SAC-AAM_v10_Huiling_20151023_modifiedVersion.pdf,Shape-appearance-correlated active appearance model,2016
143,China,LFPW,lfpw,39.98177,116.330086,National Laboratory of Pattern Recognition,edu,4a1d640f5e25bb60bb2347d36009718249ce9230,citation,http://ir.ia.ac.cn/bitstream/173211/4555/1/CVPR14FaceAlignmentCameraReady.pdf,Towards Multi-view and Partially-Occluded Face Alignment,2014
144,Singapore,LFPW,lfpw,1.2962018,103.77689944,National University of Singapore,edu,4a1d640f5e25bb60bb2347d36009718249ce9230,citation,http://ir.ia.ac.cn/bitstream/173211/4555/1/CVPR14FaceAlignmentCameraReady.pdf,Towards Multi-view and Partially-Occluded Face Alignment,2014
145,China,LFPW,lfpw,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
146,China,LFPW,lfpw,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
147,United States,LFPW,lfpw,40.47913175,-74.43168868,Rutgers University,edu,3d78c144672c4ee76d92d21dad012bdf3c3aa1a0,citation,http://www.rci.rutgers.edu/~vmp93/Journal_pub/IJCV_20170517_v4.pdf,Unconstrained Still/Video-Based Face Verification with Deep Convolutional Neural Networks,2017
148,United States,LFPW,lfpw,39.2899685,-76.62196103,University of Maryland,edu,3d78c144672c4ee76d92d21dad012bdf3c3aa1a0,citation,http://www.rci.rutgers.edu/~vmp93/Journal_pub/IJCV_20170517_v4.pdf,Unconstrained Still/Video-Based Face Verification with Deep Convolutional Neural Networks,2017
149,United Kingdom,LFPW,lfpw,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
150,United Kingdom,LFPW,lfpw,51.49887085,-0.17560797,Imperial College London,edu,4c87aafa779747828054cffee3125fcea332364d,citation,https://pdfs.semanticscholar.org/4c87/aafa779747828054cffee3125fcea332364d.pdf,View-Constrained Latent Variable Model for Multi-view Facial Expression Classification,2014
151,Netherlands,LFPW,lfpw,52.2380139,6.8566761,University of Twente,edu,4c87aafa779747828054cffee3125fcea332364d,citation,https://pdfs.semanticscholar.org/4c87/aafa779747828054cffee3125fcea332364d.pdf,View-Constrained Latent Variable Model for Multi-view Facial Expression Classification,2014
152,United Kingdom,LFPW,lfpw,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
153,United States,LFPW,lfpw,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
154,China,LFPW,lfpw,30.672721,104.098806,University of Electronic Science and Technology of China,edu,88e2574af83db7281c2064e5194c7d5dfa649846,citation,http://downloads.hindawi.com/journals/cin/2017/4579398.pdf,A Robust Shape Reconstruction Method for Facial Feature Point Detection,2017
155,United Kingdom,LFPW,lfpw,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
156,France,LFPW,lfpw,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
157,China,LFPW,lfpw,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,7343f0b7bcdaf909c5e37937e295bf0ac7b69499,citation,http://wuyuebupt.github.io/files/csi.pdf,Adaptive Cascade Deep Convolutional Neural Networks for face alignment,2015
158,United States,LFPW,lfpw,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
159,United Kingdom,LFPW,lfpw,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
160,United Kingdom,LFPW,lfpw,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
161,United Kingdom,LFPW,lfpw,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
162,United States,LFPW,lfpw,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
163,United States,LFPW,lfpw,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
164,United States,LFPW,lfpw,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
165,Poland,LFPW,lfpw,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
166,United Kingdom,LFPW,lfpw,51.49887085,-0.17560797,Imperial College London,edu,38cbb500823057613494bacd0078aa0e57b30af8,citation,https://arxiv.org/pdf/1704.08772.pdf,Deep Face Deblurring,2017
167,France,LFPW,lfpw,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
168,United States,LFPW,lfpw,43.07982815,-89.43066425,University of Wisconsin Madison,edu,0eac652139f7ab44ff1051584b59f2dc1757f53b,citation,https://arxiv.org/pdf/1611.01584.pdf,Efficient Branching Cascaded Regression for Face Alignment under Significant Head Rotation,2016
169,China,LFPW,lfpw,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
170,China,LFPW,lfpw,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
171,Poland,LFPW,lfpw,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
172,United States,LFPW,lfpw,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
173,United Kingdom,LFPW,lfpw,51.5247272,-0.03931035,Queen Mary University of London,edu,1a140d9265df8cf50a3cd69074db7e20dc060d14,citation,https://pdfs.semanticscholar.org/1a14/0d9265df8cf50a3cd69074db7e20dc060d14.pdf,Face Parts Localization Using Structured-Output Regression Forests,2012
174,United States,LFPW,lfpw,35.9542493,-83.9307395,University of Tennessee,edu,5e97a1095f2811e0bc188f52380ea7c9c460c896,citation,http://web.eecs.utk.edu/~rguo1/FacialParsing.pdf,Facial feature parsing and landmark detection via low-rank matrix decomposition,2015
175,China,LFPW,lfpw,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
176,United Kingdom,LFPW,lfpw,52.9387428,-1.20029569,University of Nottingham,edu,e5533c70706109ee8d0b2a4360fbe73fd3b0f35d,citation,https://arxiv.org/pdf/1703.07332.pdf,"How Far are We from Solving the 2D & 3D Face Alignment Problem? (and a Dataset of 230,000 3D Facial Landmarks)",2017
177,United Kingdom,LFPW,lfpw,52.17638955,0.14308882,University of Cambridge,edu,9901f473aeea177a55e58bac8fd4f1b086e575a4,citation,https://arxiv.org/pdf/1509.04954.pdf,Human and sheep facial landmarks localisation by triplet interpolated features,2016
178,United Kingdom,LFPW,lfpw,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
179,United States,LFPW,lfpw,29.7207902,-95.34406271,University of Houston,edu,466f80b066215e85da63e6f30e276f1a9d7c843b,citation,http://cbl.uh.edu/pub_files/07961802.pdf,Joint Head Pose Estimation and Face Alignment Framework Using Global and Local CNN Features,2017
180,United Kingdom,LFPW,lfpw,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
181,China,LFPW,lfpw,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
182,China,LFPW,lfpw,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
183,China,LFPW,lfpw,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
184,United Kingdom,LFPW,lfpw,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
185,United Kingdom,LFPW,lfpw,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
186,United Kingdom,LFPW,lfpw,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
187,South Africa,LFPW,lfpw,-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
188,United States,LFPW,lfpw,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
189,United States,LFPW,lfpw,42.718568,-84.47791571,Michigan State University,edu,b53485dbdd2dc5e4f3c7cff26bd8707964bb0503,citation,http://cvlab.cse.msu.edu/pdfs/Jourabloo_Liu_IJCV_2017.pdf,Pose-Invariant Face Alignment via CNN-Based Dense 3D Model Fitting,2017
190,Canada,LFPW,lfpw,45.5010087,-73.6157778,University of Montreal,edu,3176ee88d1bb137d0b561ee63edf10876f805cf0,citation,https://arxiv.org/pdf/1511.07356.pdf,Recombinator Networks: Learning Coarse-to-Fine Feature Aggregation,2016
191,Taiwan,LFPW,lfpw,25.01353105,121.54173736,National Taiwan University of Science and Technology,edu,27c6cd568d0623d549439edc98f6b92528d39bfe,citation,http://openaccess.thecvf.com/content_iccv_2015/papers/Hsu_Regressive_Tree_Structured_ICCV_2015_paper.pdf,Regressive Tree Structured Model for Facial Landmark Localization,2015
192,United States,LFPW,lfpw,38.2167565,-85.75725023,University of Louisville,edu,84bc3ca61fc63b47ec3a1a6566ab8dcefb3d0015,citation,http://www.cvip.louisville.edu/wwwcvip/research/publications/Pub_Pdf/2012/BTAS%20144.pdf,Rejecting pseudo-faces using the likelihood of facial features and skin,2012
193,Australia,LFPW,lfpw,-35.28121335,149.11665331,"Australian National University, Canberra",edu,24e099e77ae7bae3df2bebdc0ee4e00acca71250,citation,http://users.cecs.anu.edu.au/~hexm/papers/heng_tip.pdf,Robust Face Alignment Under Occlusion via Regional Predictive Power Estimation,2015
194,China,LFPW,lfpw,22.4162632,114.2109318,Chinese University of Hong Kong,edu,24e099e77ae7bae3df2bebdc0ee4e00acca71250,citation,http://users.cecs.anu.edu.au/~hexm/papers/heng_tip.pdf,Robust Face Alignment Under Occlusion via Regional Predictive Power Estimation,2015
195,United Kingdom,LFPW,lfpw,51.5247272,-0.03931035,Queen Mary University of London,edu,24e099e77ae7bae3df2bebdc0ee4e00acca71250,citation,http://users.cecs.anu.edu.au/~hexm/papers/heng_tip.pdf,Robust Face Alignment Under Occlusion via Regional Predictive Power Estimation,2015
196,United States,LFPW,lfpw,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
197,United States,LFPW,lfpw,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
198,Australia,LFPW,lfpw,-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
199,China,LFPW,lfpw,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
200,United States,LFPW,lfpw,40.4441619,-79.94272826,Carnegie Mellon University,edu,7cfbf90368553333b47731729e0e358479c25340,citation,http://www.andrew.cmu.edu/user/kseshadr/TPAMI_2016_Paper_Final_Submission.pdf,"Towards a Unified Framework for Pose, Expression, and Occlusion Tolerant Automatic Facial Alignment",2016
201,Poland,LFPW,lfpw,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
202,United Kingdom,LFPW,lfpw,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
203,United Kingdom,LFPW,lfpw,50.7944026,-1.0971748,Cambridge University,edu,cc96eab1e55e771e417b758119ce5d7ef1722b43,citation,https://arxiv.org/pdf/1511.05049.pdf,An Empirical Study of Recent Face Alignment Methods,2015
204,China,LFPW,lfpw,22.4162632,114.2109318,Chinese University of Hong Kong,edu,cc96eab1e55e771e417b758119ce5d7ef1722b43,citation,https://arxiv.org/pdf/1511.05049.pdf,An Empirical Study of Recent Face Alignment Methods,2015
205,China,LFPW,lfpw,35.86166,104.195397,"Megvii Inc. (Face++), China",company,064b797aa1da2000640e437cacb97256444dee82,citation,https://arxiv.org/pdf/1511.04901.pdf,Coarse-to-fine Face Alignment with Multi-Scale Local Patch Regression,2015
206,Germany,LFPW,lfpw,49.10184375,8.4331256,Karlsruhe Institute of Technology,edu,9b9ccd4954cf9dd605d49e9c3504224d06725ab7,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w13/papers/Schwarz_DriveAHead_-_A_CVPR_2017_paper.pdf,DriveAHead — A Large-Scale Driver Head Pose Dataset,2017
207,China,LFPW,lfpw,32.0565957,118.77408833,Nanjing University,edu,91883dabc11245e393786d85941fb99a6248c1fb,citation,https://arxiv.org/pdf/1608.04188.pdf,Face Alignment In-the-Wild: A Survey,2017
208,United Kingdom,LFPW,lfpw,51.521975,-0.130462,"Birkbeck College, London, UK",edu,38192a0f9261d9727b119e294a65f2e25f72d7e6,citation,https://arxiv.org/pdf/1410.1037.pdf,Facial feature point detection: A comprehensive survey,2018
209,Australia,LFPW,lfpw,-33.8809651,151.20107299,University of Technology Sydney,edu,38192a0f9261d9727b119e294a65f2e25f72d7e6,citation,https://arxiv.org/pdf/1410.1037.pdf,Facial feature point detection: A comprehensive survey,2018
210,China,LFPW,lfpw,34.1235825,108.83546,Xidian University,edu,38192a0f9261d9727b119e294a65f2e25f72d7e6,citation,https://arxiv.org/pdf/1410.1037.pdf,Facial feature point detection: A comprehensive survey,2018
211,China,LFPW,lfpw,30.19331415,120.11930822,Zhejiang University,edu,bd8e2d27987be9e13af2aef378754f89ab20ce10,citation,http://bksy.zju.edu.cn/attachments/tlxjxj/2016-10/99999-1477633998-1097578.pdf,Facial feature points detecting based on Gaussian Mixture Models,2015
212,Japan,LFPW,lfpw,35.2742655,137.01327841,Chubu University,edu,62f0d8446adee6a5e8102053a63a61af07ac4098,citation,http://www.vision.cs.chubu.ac.jp/MPRG/C_group/C072_yamashita2015.pdf,Facial point detection using convolutional neural network transferred from a heterogeneous task,2015
213,Sweden,LFPW,lfpw,58.3978364,15.5760072,Linköping University,edu,ebd5df2b4105ba04cef4ca334fcb9bfd6ea0430c,citation,https://arxiv.org/pdf/1403.6888.pdf,Fast Localization of Facial Landmark Points,2014
214,Croatia,LFPW,lfpw,45.801121,15.9708409,University of Zagreb,edu,ebd5df2b4105ba04cef4ca334fcb9bfd6ea0430c,citation,https://arxiv.org/pdf/1403.6888.pdf,Fast Localization of Facial Landmark Points,2014
215,United States,LFPW,lfpw,29.736724,-95.3931825,Houston University,edu,5b2cfee6e81ef36507ebf3c305e84e9e0473575a,citation,https://arxiv.org/pdf/1704.02402.pdf,GoDP: Globally Optimized Dual Pathway deep network architecture for facial landmark localization in-the-wild,2018
216,United States,LFPW,lfpw,43.07982815,-89.43066425,University of Wisconsin Madison,edu,fd615118fb290a8e3883e1f75390de8a6c68bfde,citation,https://pdfs.semanticscholar.org/fd61/5118fb290a8e3883e1f75390de8a6c68bfde.pdf,Joint Face Alignment with Non-parametric Shape Models,2012
217,United Kingdom,LFPW,lfpw,51.49887085,-0.17560797,Imperial College London,edu,47471105d9ee2276e14ab4a3a4d66ef58612188f,citation,https://arxiv.org/pdf/1708.06023.pdf,Joint Multi-view Face Alignment in the Wild,2019
218,United Kingdom,LFPW,lfpw,51.5247272,-0.03931035,Queen Mary University of London,edu,d511e903a882658c9f6f930d6dd183007f508eda,citation,https://www.computer.org/csdl/proceedings/fg/2013/5545/00/06553766.pdf,Privileged information-based conditional regression forest for facial feature detection,2013
219,China,LFPW,lfpw,31.4854255,120.2739581,Jiangnan University,edu,2d072cd43de8d17ce3198fae4469c498f97c6277,citation,http://www.ee.surrey.ac.uk/CVSSP/Publications/papers/Feng-IEEE-SPL-2015.pdf,Random Cascaded-Regression Copse for Robust Facial Landmark Detection,2015
220,United Kingdom,LFPW,lfpw,51.24303255,-0.59001382,University of Surrey,edu,2d072cd43de8d17ce3198fae4469c498f97c6277,citation,http://www.ee.surrey.ac.uk/CVSSP/Publications/papers/Feng-IEEE-SPL-2015.pdf,Random Cascaded-Regression Copse for Robust Facial Landmark Detection,2015
221,Italy,LFPW,lfpw,46.0658836,11.1159894,University of Trento,edu,b48d3694a8342b6efc18c9c9124c62406e6bf3b3,citation,,Recurrent Convolutional Shape Regression,2018
222,United States,LFPW,lfpw,33.9850469,-118.4694832,"Snapchat Research, Venice, CA",company,b48d3694a8342b6efc18c9c9124c62406e6bf3b3,citation,,Recurrent Convolutional Shape Regression,2018
223,United States,LFPW,lfpw,34.13710185,-118.12527487,California Institute of Technology,edu,2724ba85ec4a66de18da33925e537f3902f21249,citation,,Robust Face Landmark Estimation under Occlusion,2013
224,United States,LFPW,lfpw,47.6423318,-122.1369302,Microsoft,company,2724ba85ec4a66de18da33925e537f3902f21249,citation,,Robust Face Landmark Estimation under Occlusion,2013
225,United States,LFPW,lfpw,40.4441619,-79.94272826,Carnegie Mellon University,edu,1035b073455165a31de875390977c8c09a672f2d,citation,https://pdfs.semanticscholar.org/1035/b073455165a31de875390977c8c09a672f2d.pdf,Robust Facial Landmark Localization Under Simultaneous Real-World Degradations,2015
226,China,LFPW,lfpw,22.4162632,114.2109318,Chinese University of Hong Kong,edu,2f489bd9bfb61a7d7165a2f05c03377a00072477,citation,https://pdfs.semanticscholar.org/2f48/9bd9bfb61a7d7165a2f05c03377a00072477.pdf,Structured Semi-supervised Forest for Facial Landmarks Localization with Face Mask Reasoning,2014
227,United Kingdom,LFPW,lfpw,51.5247272,-0.03931035,Queen Mary University of London,edu,2f489bd9bfb61a7d7165a2f05c03377a00072477,citation,https://pdfs.semanticscholar.org/2f48/9bd9bfb61a7d7165a2f05c03377a00072477.pdf,Structured Semi-supervised Forest for Facial Landmarks Localization with Face Mask Reasoning,2014
228,United States,LFPW,lfpw,40.4441619,-79.94272826,Carnegie Mellon University,edu,fd4ac1da699885f71970588f84316589b7d8317b,citation,https://arxiv.org/pdf/1405.0601.pdf,Supervised Descent Method for Solving Nonlinear Least Squares Problems in Computer Vision,2014
229,China,LFPW,lfpw,40.0044795,116.370238,Chinese Academy of Sciences,edu,e0162dea3746d58083dd1d061fb276015d875b2e,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Shao_Unconstrained_Face_Alignment_CVPR_2017_paper.pdf,Unconstrained Face Alignment Without Face Detection,2017
230,United Kingdom,LFPW,lfpw,51.7534538,-1.25400997,University of Oxford,edu,73c9cbbf3f9cea1bc7dce98fce429bf0616a1a8c,citation,https://arxiv.org/pdf/1705.02193.pdf,Unsupervised Learning of Object Landmarks by Factorized Spatial Embeddings,2017