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

index,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year
0,COFW,cofw,0.0,0.0,,,2724ba85ec4a66de18da33925e537f3902f21249,main,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6751298,Robust Face Landmark Estimation under Occlusion,2013
1,COFW,cofw,23.04436505,113.36668458,Guangzhou University,edu,293d69d042fe9bc4fea256c61915978ddaf7cc92,citation,https://doi.org/10.1007/978-981-10-7302-1_6,Face Recognition by Coarse-to-Fine Landmark Regression with Application to ATM Surveillance,2017
2,COFW,cofw,23.09461185,113.28788994,Sun Yat-Sen University,edu,293d69d042fe9bc4fea256c61915978ddaf7cc92,citation,https://doi.org/10.1007/978-981-10-7302-1_6,Face Recognition by Coarse-to-Fine Landmark Regression with Application to ATM Surveillance,2017
3,COFW,cofw,32.87935255,-117.23110049,"University of California, San Diego",edu,d68dbb71b34dfe98dee0680198a23d3b53056394,citation,http://pdfs.semanticscholar.org/d68d/bb71b34dfe98dee0680198a23d3b53056394.pdf,VIVA Face-off Challenge: Dataset Creation and Balancing Privacy,2015
4,COFW,cofw,40.0044795,116.370238,Chinese Academy of Sciences,edu,c2474202d56bb80663e7bece5924245978425fc1,citation,https://doi.org/10.1109/ICIP.2016.7532771,Localize heavily occluded human faces via deep segmentation,2016
5,COFW,cofw,31.83907195,117.26420748,University of Science and Technology of China,edu,a7a3ec1128f920066c25cb86fbc33445ce613919,citation,https://doi.org/10.1109/VCIP.2017.8305115,Joint facial landmark detection and action estimation based on deep probabilistic random forest,2017
6,COFW,cofw,42.9336278,-78.88394479,SUNY Buffalo,edu,a7a3ec1128f920066c25cb86fbc33445ce613919,citation,https://doi.org/10.1109/VCIP.2017.8305115,Joint facial landmark detection and action estimation based on deep probabilistic random forest,2017
7,COFW,cofw,42.718568,-84.47791571,Michigan State University,edu,0141cb33c822e87e93b0c1bad0a09db49b3ad470,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7298876,Unconstrained 3D face reconstruction,2015
8,COFW,cofw,22.2081469,114.25964115,University of Hong Kong,edu,fb87045600da73b07f0757f345a937b1c8097463,citation,https://pdfs.semanticscholar.org/5c54/2fef80a35a4f930e5c82040b52c58e96ce87.pdf,Reflective Regression of 2D-3D Face Shape Across Large Pose,2016
9,COFW,cofw,1.2962018,103.77689944,National University of Singapore,edu,1fe59275142844ce3ade9e2aed900378dd025880,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w25/papers/Xiao_Facial_Landmark_Detection_ICCV_2015_paper.pdf,Facial Landmark Detection via Progressive Initialization,2015
10,COFW,cofw,42.7298459,-73.67950216,Rensselaer Polytechnic Institute,edu,171d8a39b9e3d21231004f7008397d5056ff23af,citation,http://arxiv.org/abs/1709.08130,"Simultaneous Facial Landmark Detection, Pose and Deformation Estimation Under Facial Occlusion",2017
11,COFW,cofw,52.17638955,0.14308882,University of Cambridge,edu,4ae291b070ad7940b3c9d3cb10e8c05955c9e269,citation,http://www.cl.cam.ac.uk/~pr10/publications/icmi14.pdf,Automatic Detection of Naturalistic Hand-over-Face Gesture Descriptors,2014
12,COFW,cofw,39.9041999,116.4073963,"360 AI Institute, Beijing, China",company,54f169ad7d1f6c9ce94381e9b5ccc1a07fd49cc6,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7911334,Towards Robust and Accurate Multi-View and Partially-Occluded Face Alignment,2018
13,COFW,cofw,51.2352438,7.1593132,Delphi Deutschland GMBH,company,54f169ad7d1f6c9ce94381e9b5ccc1a07fd49cc6,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7911334,Towards Robust and Accurate Multi-View and Partially-Occluded Face Alignment,2018
14,COFW,cofw,40.0044795,116.370238,Chinese Academy of Sciences,edu,54f169ad7d1f6c9ce94381e9b5ccc1a07fd49cc6,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7911334,Towards Robust and Accurate Multi-View and Partially-Occluded Face Alignment,2018
15,COFW,cofw,40.0044795,116.370238,Chinese Academy of Sciences,edu,bc910ca355277359130da841a589a36446616262,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Huang_Conditional_High-Order_Boltzmann_ICCV_2015_paper.pdf,Conditional High-Order Boltzmann Machine: A Supervised Learning Model for Relation Learning,2015
16,COFW,cofw,29.7207902,-95.34406271,University of Houston,edu,466f80b066215e85da63e6f30e276f1a9d7c843b,citation,http://doi.ieeecomputersociety.org/10.1109/FG.2017.81,Joint Head Pose Estimation and Face Alignment Framework Using Global and Local CNN Features,2017
17,COFW,cofw,37.4102193,-122.05965487,Carnegie Mellon University,edu,3146fabd5631a7d1387327918b184103d06c2211,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016_workshops/w18/papers/Jeni_Person-Independent_3D_Gaze_CVPR_2016_paper.pdf,Person-Independent 3D Gaze Estimation Using Face Frontalization,2016
18,COFW,cofw,40.44415295,-79.96243993,University of Pittsburgh,edu,3146fabd5631a7d1387327918b184103d06c2211,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016_workshops/w18/papers/Jeni_Person-Independent_3D_Gaze_CVPR_2016_paper.pdf,Person-Independent 3D Gaze Estimation Using Face Frontalization,2016
19,COFW,cofw,42.718568,-84.47791571,Michigan State University,edu,b53485dbdd2dc5e4f3c7cff26bd8707964bb0503,citation,http://doi.org/10.1007/s11263-017-1012-z,Pose-Invariant Face Alignment via CNN-Based Dense 3D Model Fitting,2017
20,COFW,cofw,38.83133325,-77.30798839,George Mason University,edu,a9426cb98c8aedf79ea19839643a7cf1e435aeaa,citation,https://doi.org/10.1109/GlobalSIP.2016.7905998,Cascaded regression for 3D pose estimation for mouse in fisheye lens distorted monocular images,2016
21,COFW,cofw,39.00041165,-77.10327775,National Institutes of Health,edu,a9426cb98c8aedf79ea19839643a7cf1e435aeaa,citation,https://doi.org/10.1109/GlobalSIP.2016.7905998,Cascaded regression for 3D pose estimation for mouse in fisheye lens distorted monocular images,2016
22,COFW,cofw,41.3861759,2.1248717,"Transmural Biotech, Barcelona, Spain",edu,a9426cb98c8aedf79ea19839643a7cf1e435aeaa,citation,https://doi.org/10.1109/GlobalSIP.2016.7905998,Cascaded regression for 3D pose estimation for mouse in fisheye lens distorted monocular images,2016
23,COFW,cofw,26.88111275,112.62850666,Hunan University,edu,1fe1a78c941e03abe942498249c041b2703fd3d2,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8393355,Face alignment based on improved shape searching,2017
24,COFW,cofw,32.8164178,130.72703969,Kumamoto University,edu,7aafeb9aab48fb2c34bed4b86755ac71e3f00338,citation,http://pdfs.semanticscholar.org/7aaf/eb9aab48fb2c34bed4b86755ac71e3f00338.pdf,Real Time 3D Facial Movement Tracking Using a Monocular Camera,2016
25,COFW,cofw,31.28473925,121.49694909,Tongji University,edu,7aafeb9aab48fb2c34bed4b86755ac71e3f00338,citation,http://pdfs.semanticscholar.org/7aaf/eb9aab48fb2c34bed4b86755ac71e3f00338.pdf,Real Time 3D Facial Movement Tracking Using a Monocular Camera,2016
26,COFW,cofw,32.8164178,130.72703969,Kumamoto University,edu,6fdf2f4f7ae589af6016305a17d460617d9ef345,citation,https://doi.org/10.1109/ICIP.2015.7350767,Robust facial landmark localization using multi partial features,2015
27,COFW,cofw,31.28473925,121.49694909,Tongji University,edu,6fdf2f4f7ae589af6016305a17d460617d9ef345,citation,https://doi.org/10.1109/ICIP.2015.7350767,Robust facial landmark localization using multi partial features,2015
28,COFW,cofw,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
29,COFW,cofw,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
30,COFW,cofw,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
31,COFW,cofw,37.5901411,127.0362318,Korea University,edu,5957936195c10521dadc9b90ca9b159eb1fc4871,citation,https://doi.org/10.1109/TCE.2016.7838098,LBP-ferns-based feature extraction for robust facial recognition,2016
32,COFW,cofw,33.5866784,-101.87539204,Electrical and Computer Engineering,edu,ebb3d5c70bedf2287f9b26ac0031004f8f617b97,citation,https://doi.org/10.1109/MSP.2017.2764116,"Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans",2018
33,COFW,cofw,39.2899685,-76.62196103,University of Maryland,edu,ebb3d5c70bedf2287f9b26ac0031004f8f617b97,citation,https://doi.org/10.1109/MSP.2017.2764116,"Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans",2018
34,COFW,cofw,40.0141905,-83.0309143,University of Electronic Science and Technology of China,edu,21a2f67b21905ff6e0afa762937427e92dc5aa0b,citation,http://pdfs.semanticscholar.org/21a2/f67b21905ff6e0afa762937427e92dc5aa0b.pdf,Extra Facial Landmark Localization via Global Shape Reconstruction,2017
35,COFW,cofw,40.0141905,-83.0309143,University of Electronic Science and Technology of China,edu,88e2574af83db7281c2064e5194c7d5dfa649846,citation,http://pdfs.semanticscholar.org/88e2/574af83db7281c2064e5194c7d5dfa649846.pdf,A Robust Shape Reconstruction Method for Facial Feature Point Detection,2017
36,COFW,cofw,29.7207902,-95.34406271,University of Houston,edu,607aebe7568407421e8ffc7b23a5fda52650ad93,citation,https://doi.org/10.1109/ISBA.2016.7477237,Face alignment via an ensemble of random ferns,2016
37,COFW,cofw,-27.49741805,153.01316956,University of Queensland,edu,710c3aaffef29730ffd909a63798e9185f488327,citation,https://doi.org/10.1109/ICPR.2016.7900095,The GIST of aligning faces,2016
38,COFW,cofw,32.7283683,-97.11201835,University of Texas at Arlington,edu,411dc8874fd7b3a9a4c1fd86bb5b583788027776,citation,https://pdfs.semanticscholar.org/701f/56f0eac9f88387de1f556acef78016b05d52.pdf,Direct Shape Regression Networks for End-to-End Face Alignment,2018
39,COFW,cofw,34.1235825,108.83546,Xidian University,edu,411dc8874fd7b3a9a4c1fd86bb5b583788027776,citation,https://pdfs.semanticscholar.org/701f/56f0eac9f88387de1f556acef78016b05d52.pdf,Direct Shape Regression Networks for End-to-End Face Alignment,2018
40,COFW,cofw,30.44235995,-84.29747867,Florida State University,edu,1ed6c7e02b4b3ef76f74dd04b2b6050faa6e2177,citation,http://pdfs.semanticscholar.org/6433/c412149382418ccd8aa966aa92973af41671.pdf,Face Detection with a 3D Model,2014
41,COFW,cofw,39.00041165,-77.10327775,National Institutes of Health,edu,1ed6c7e02b4b3ef76f74dd04b2b6050faa6e2177,citation,http://pdfs.semanticscholar.org/6433/c412149382418ccd8aa966aa92973af41671.pdf,Face Detection with a 3D Model,2014
42,COFW,cofw,32.87935255,-117.23110049,"University of California, San Diego",edu,43776d1bfa531e66d5e9826ff5529345b792def7,citation,http://cvrr.ucsd.edu/scmartin/presentation/DriveAnalysisByLookingIn-ITSC2015-NDS.pdf,Automatic Critical Event Extraction and Semantic Interpretation by Looking-Inside,2015
43,COFW,cofw,38.99203005,-76.9461029,University of Maryland College Park,edu,f7824758800a7b1a386db5bd35f84c81454d017a,citation,https://arxiv.org/pdf/1702.05085.pdf,KEPLER: Keypoint and Pose Estimation of Unconstrained Faces by Learning Efficient H-CNN Regressors,2017
44,COFW,cofw,38.99203005,-76.9461029,University of Maryland College Park,edu,ceeb67bf53ffab1395c36f1141b516f893bada27,citation,http://pdfs.semanticscholar.org/ceeb/67bf53ffab1395c36f1141b516f893bada27.pdf,Face Alignment by Local Deep Descriptor Regression,2016
45,COFW,cofw,40.47913175,-74.43168868,Rutgers University,edu,ceeb67bf53ffab1395c36f1141b516f893bada27,citation,http://pdfs.semanticscholar.org/ceeb/67bf53ffab1395c36f1141b516f893bada27.pdf,Face Alignment by Local Deep Descriptor Regression,2016
46,COFW,cofw,39.2899685,-76.62196103,University of Maryland,edu,ceeb67bf53ffab1395c36f1141b516f893bada27,citation,http://pdfs.semanticscholar.org/ceeb/67bf53ffab1395c36f1141b516f893bada27.pdf,Face Alignment by Local Deep Descriptor Regression,2016
47,COFW,cofw,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
48,COFW,cofw,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
49,COFW,cofw,51.7534538,-1.25400997,University of Oxford,edu,a3d0ebb50d49116289fb176d28ea98a92badada6,citation,https://pdfs.semanticscholar.org/a3d0/ebb50d49116289fb176d28ea98a92badada6.pdf,Unsupervised Learning of Object Landmarks through Conditional Image Generation,2018
50,COFW,cofw,55.94951105,-3.19534913,University of Edinburgh,edu,a3d0ebb50d49116289fb176d28ea98a92badada6,citation,https://pdfs.semanticscholar.org/a3d0/ebb50d49116289fb176d28ea98a92badada6.pdf,Unsupervised Learning of Object Landmarks through Conditional Image Generation,2018
51,COFW,cofw,30.642769,104.06751175,"Sichuan University, Chengdu",edu,a0aa32bb7f406693217fba6dcd4aeb6c4d5a479b,citation,https://pdfs.semanticscholar.org/a0aa/32bb7f406693217fba6dcd4aeb6c4d5a479b.pdf,Cascaded Regressor based 3D Face Reconstruction from a Single Arbitrary View Image,2015
52,COFW,cofw,25.01353105,121.54173736,National Taiwan University of Science and Technology,edu,deb89950939ae9847f0a1a4bb198e6dbfed62778,citation,https://doi.org/10.1109/LSP.2016.2543019,Accurate Facial Landmark Extraction,2016
53,COFW,cofw,3.12267405,101.65356103,University of Malaya,edu,deb89950939ae9847f0a1a4bb198e6dbfed62778,citation,https://doi.org/10.1109/LSP.2016.2543019,Accurate Facial Landmark Extraction,2016
54,COFW,cofw,37.4102193,-122.05965487,Carnegie Mellon University,edu,78598e7005f7c96d64cc47ff47e6f13ae52245b8,citation,https://arxiv.org/pdf/1708.00370.pdf,Hand2Face: Automatic synthesis and recognition of hand over face occlusions,2017
55,COFW,cofw,28.59899755,-81.19712501,University of Central Florida,edu,78598e7005f7c96d64cc47ff47e6f13ae52245b8,citation,https://arxiv.org/pdf/1708.00370.pdf,Hand2Face: Automatic synthesis and recognition of hand over face occlusions,2017
56,COFW,cofw,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
57,COFW,cofw,40.00229045,116.32098908,Tsinghua University,edu,e4fa062bff299a0bcef9f6b2e593c85be116c9f1,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7407641,Cascaded Elastically Progressive Model for Accurate Face Alignment,2017
58,COFW,cofw,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,5c820e47981d21c9dddde8d2f8020146e600368f,citation,http://pdfs.semanticscholar.org/5c82/0e47981d21c9dddde8d2f8020146e600368f.pdf,Extended Supervised Descent Method for Robust Face Alignment,2014
59,COFW,cofw,51.49887085,-0.17560797,Imperial College London,edu,29c340c83b3bbef9c43b0c50b4d571d5ed037cbd,citation,https://pdfs.semanticscholar.org/29c3/40c83b3bbef9c43b0c50b4d571d5ed037cbd.pdf,Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment,2018
60,COFW,cofw,30.19331415,120.11930822,Zhejiang University,edu,5213549200bccec57232fc3ff788ddf1043af7b3,citation,http://doi.acm.org/10.1145/2601097.2601204,Displaced dynamic expression regression for real-time facial tracking and animation,2014
61,COFW,cofw,51.49887085,-0.17560797,Imperial College London,edu,034b3f3bac663fb814336a69a9fd3514ca0082b9,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7298991,Unifying holistic and Parts-Based Deformable Model fitting,2015
62,COFW,cofw,50.74223495,-1.89433739,Bournemouth University,edu,91f0a95b8eb76e8fa24c8267e4a7a17815fc7a11,citation,http://doi.org/10.1007/s41095-016-0068-y,Robust facial landmark detection and tracking across poses and expressions for in-the-wild monocular video,2016
63,COFW,cofw,45.7413921,126.62552755,Harbin Institute of Technology,edu,91f0a95b8eb76e8fa24c8267e4a7a17815fc7a11,citation,http://doi.org/10.1007/s41095-016-0068-y,Robust facial landmark detection and tracking across poses and expressions for in-the-wild monocular video,2016
64,COFW,cofw,39.9808333,116.34101249,Beihang University,edu,86b6afc667bb14ff4d69e7a5e8bb2454a6bbd2cd,citation,https://pdfs.semanticscholar.org/86b6/afc667bb14ff4d69e7a5e8bb2454a6bbd2cd.pdf,Attentional Alignment Networks,2018
65,COFW,cofw,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,86b6afc667bb14ff4d69e7a5e8bb2454a6bbd2cd,citation,https://pdfs.semanticscholar.org/86b6/afc667bb14ff4d69e7a5e8bb2454a6bbd2cd.pdf,Attentional Alignment Networks,2018
66,COFW,cofw,32.7283683,-97.11201835,University of Texas at Arlington,edu,86b6afc667bb14ff4d69e7a5e8bb2454a6bbd2cd,citation,https://pdfs.semanticscholar.org/86b6/afc667bb14ff4d69e7a5e8bb2454a6bbd2cd.pdf,Attentional Alignment Networks,2018
67,COFW,cofw,40.0044795,116.370238,Chinese Academy of Sciences,edu,22e2066acfb795ac4db3f97d2ac176d6ca41836c,citation,http://pdfs.semanticscholar.org/26f5/3a1abb47b1f0ea1f213dc7811257775dc6e6.pdf,Coarse-to-Fine Auto-Encoder Networks (CFAN) for Real-Time Face Alignment,2014
68,COFW,cofw,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,22e2066acfb795ac4db3f97d2ac176d6ca41836c,citation,http://pdfs.semanticscholar.org/26f5/3a1abb47b1f0ea1f213dc7811257775dc6e6.pdf,Coarse-to-Fine Auto-Encoder Networks (CFAN) for Real-Time Face Alignment,2014
69,COFW,cofw,43.13800205,-75.22943591,SUNY Polytechnic Institute,edu,69b18d62330711bfd7f01a45f97aaec71e9ea6a5,citation,http://pdfs.semanticscholar.org/69b1/8d62330711bfd7f01a45f97aaec71e9ea6a5.pdf,M-Track: A New Software for Automated Detection of Grooming Trajectories in Mice,2016
70,COFW,cofw,-30.0338248,-51.218828,Federal University of Rio Grande do Sul,edu,fa08b52dda21ccf71ebc91bc0c4d206ac0aa3719,citation,https://doi.org/10.1109/TIM.2015.2415012,Customized Orthogonal Locality Preserving Projections With Soft-Margin Maximization for Face Recognition,2015
71,COFW,cofw,-28.234493,-52.38044,University of Passo Fundo,edu,fa08b52dda21ccf71ebc91bc0c4d206ac0aa3719,citation,https://doi.org/10.1109/TIM.2015.2415012,Customized Orthogonal Locality Preserving Projections With Soft-Margin Maximization for Face Recognition,2015
72,COFW,cofw,34.0224149,-118.28634407,University of Southern California,edu,632b24ddd42fda4aebc5a8af3ec44f7fd3ecdc6c,citation,https://arxiv.org/pdf/1604.02647.pdf,Real-Time Facial Segmentation and Performance Capture from RGB Input,2016
73,COFW,cofw,22.42031295,114.20788644,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
74,COFW,cofw,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
75,COFW,cofw,43.07982815,-89.43066425,University of Wisconsin Madison,edu,77fbbf0c5729f97fcdbfdc507deee3d388cd4889,citation,https://pdfs.semanticscholar.org/ec7f/c7bf79204166f78c27e870b620205751fff6.pdf,Pose-Robust 3D Facial Landmark Estimation from a Single 2D Image,2016
76,COFW,cofw,36.3697191,127.362537,Korea Advanced Institute of Science and Technology,edu,72e10a2a7a65db7ecdc7d9bd3b95a4160fab4114,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/ext/2B_094_ext.pdf,Face alignment using cascade Gaussian process regression trees,2015
77,COFW,cofw,-33.88890695,151.18943366,University of Sydney,edu,58d43e32660446669ff54f29658961fe8bb6cc72,citation,https://doi.org/10.1109/ISBI.2017.7950504,Automatic detection of obstructive sleep apnea using facial images,2017
78,COFW,cofw,52.3793131,-1.5604252,University of Warwick,edu,0bc53b338c52fc635687b7a6c1e7c2b7191f42e5,citation,http://pdfs.semanticscholar.org/a32a/8d6d4c3b4d69544763be48ffa7cb0d7f2f23.pdf,Loglet SIFT for Part Description in Deformable Part Models: Application to Face Alignment,2016
79,COFW,cofw,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
80,COFW,cofw,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,7f2a4cd506fe84dee26c0fb41848cb219305173f,citation,http://pdfs.semanticscholar.org/7f2a/4cd506fe84dee26c0fb41848cb219305173f.pdf,Face Detection and Pose Estimation Based on Evaluating Facial Feature Selection,2015
81,COFW,cofw,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
82,COFW,cofw,23.09461185,113.28788994,Sun Yat-Sen University,edu,4c078c2919c7bdc26ca2238fa1a79e0331898b56,citation,http://pdfs.semanticscholar.org/4c07/8c2919c7bdc26ca2238fa1a79e0331898b56.pdf,Unconstrained Facial Landmark Localization with Backbone-Branches Fully-Convolutional Networks,2015
83,COFW,cofw,34.0224149,-118.28634407,University of Southern California,edu,43e99b76ca8e31765d4571d609679a689afdc99e,citation,http://arxiv.org/abs/1709.00536,Learning Dense Facial Correspondences in Unconstrained Images,2017
84,COFW,cofw,32.0565957,118.77408833,Nanjing University,edu,46b2ecef197b465abc43e0e017543b1af61921ac,citation,https://doi.org/10.1109/ICPR.2016.7899652,Face alignment with Cascaded Bidirectional LSTM Neural Networks,2016
85,COFW,cofw,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,c00f402b9cfc3f8dd2c74d6b3552acbd1f358301,citation,http://pdfs.semanticscholar.org/c00f/402b9cfc3f8dd2c74d6b3552acbd1f358301.pdf,Learning deep representation from coarse to fine for face alignment,2016
86,COFW,cofw,17.4454957,78.34854698,International Institute of Information Technology,edu,185263189a30986e31566394680d6d16b0089772,citation,https://pdfs.semanticscholar.org/1852/63189a30986e31566394680d6d16b0089772.pdf,Efficient Annotation of Objects for Video Analysis,2018
87,COFW,cofw,-33.8809651,151.20107299,University of Technology Sydney,edu,ebc2a3e8a510c625353637e8e8f07bd34410228f,citation,https://doi.org/10.1109/TIP.2015.2502485,Dual Sparse Constrained Cascade Regression for Robust Face Alignment,2016
88,COFW,cofw,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,1922ad4978ab92ce0d23acc4c7441a8812f157e5,citation,http://personal.ie.cuhk.edu.hk/~ccloy/files/cvpr_2015_alignment.pdf,Face alignment by coarse-to-fine shape searching,2015
89,COFW,cofw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,1922ad4978ab92ce0d23acc4c7441a8812f157e5,citation,http://personal.ie.cuhk.edu.hk/~ccloy/files/cvpr_2015_alignment.pdf,Face alignment by coarse-to-fine shape searching,2015
90,COFW,cofw,34.0224149,-118.28634407,University of Southern California,edu,53e081f5af505374c3b8491e9c4470fe77fe7934,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Hsieh_Unconstrained_Realtime_Facial_2015_CVPR_paper.pdf,Unconstrained realtime facial performance capture,2015
91,COFW,cofw,39.9922379,116.30393816,Peking University,edu,11ba01ce7d606bab5c2d7e998c6d94325521b8a0,citation,https://doi.org/10.1109/ICIP.2015.7350911,Regression based landmark estimation and multi-feature fusion for visual speech recognition,2015
92,COFW,cofw,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,b11bb6bd63ee6f246d278dd4edccfbe470263803,citation,http://pdfs.semanticscholar.org/b11b/b6bd63ee6f246d278dd4edccfbe470263803.pdf,Joint Voxel and Coordinate Regression for Accurate 3D Facial Landmark Localization,2018
93,COFW,cofw,42.718568,-84.47791571,Michigan State University,edu,86204fc037936754813b91898377e8831396551a,citation,https://arxiv.org/pdf/1709.01442.pdf,Dense Face Alignment,2017
94,COFW,cofw,43.07982815,-89.43066425,University of Wisconsin Madison,edu,0eac652139f7ab44ff1051584b59f2dc1757f53b,citation,http://pdfs.semanticscholar.org/0eac/652139f7ab44ff1051584b59f2dc1757f53b.pdf,Efficient Branching Cascaded Regression for Face Alignment under Significant Head Rotation,2016
95,COFW,cofw,39.9586652,116.30971281,Beijing Institute of Technology,edu,0ea7b7fff090c707684fd4dc13e0a8f39b300a97,citation,http://arxiv.org/abs/1711.06055,Integrated Face Analytics Networks through Cross-Dataset Hybrid Training,2017
96,COFW,cofw,33.5866784,-101.87539204,Electrical and Computer Engineering,edu,0ea7b7fff090c707684fd4dc13e0a8f39b300a97,citation,http://arxiv.org/abs/1711.06055,Integrated Face Analytics Networks through Cross-Dataset Hybrid Training,2017
97,COFW,cofw,1.2962018,103.77689944,National University of Singapore,edu,0ea7b7fff090c707684fd4dc13e0a8f39b300a97,citation,http://arxiv.org/abs/1711.06055,Integrated Face Analytics Networks through Cross-Dataset Hybrid Training,2017
98,COFW,cofw,-26.1888813,28.02479073,University of the Witwatersrand,edu,aa4af9b3811db6a30e1c7cc1ebf079078c1ee152,citation,http://doi.acm.org/10.1145/3129416.3129451,Deformable part models with CNN features for facial landmark detection under occlusion,2017
99,COFW,cofw,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
100,COFW,cofw,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
101,COFW,cofw,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,b5da4943c348a6b4c934c2ea7330afaf1d655e79,citation,http://pdfs.semanticscholar.org/b5da/4943c348a6b4c934c2ea7330afaf1d655e79.pdf,Facial Landmarks Detection by Self-Iterative Regression based Landmarks-Attention Network,2018
102,COFW,cofw,40.0044795,116.370238,Chinese Academy of Sciences,edu,a73405038fdc0d8bf986539ef755a80ebd341e97,citation,https://doi.org/10.1109/TIP.2017.2698918,Conditional High-Order Boltzmann Machines for Supervised Relation Learning,2017
103,COFW,cofw,34.13710185,-118.12527487,California Institute of Technology,edu,11aa527c01e61ec3a7a67eef8d7ffe9d9ce63f1d,citation,http://pdfs.semanticscholar.org/11aa/527c01e61ec3a7a67eef8d7ffe9d9ce63f1d.pdf,"Automated measurement of mouse social behaviors using depth sensing, video tracking, and machine learning.",2015
104,COFW,cofw,51.24303255,-0.59001382,University of Surrey,edu,56e25056153a15eae2a6b10c109f812d2b753cee,citation,https://arxiv.org/pdf/1711.06753.pdf,Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks,2017
105,COFW,cofw,31.4854255,120.2739581,Jiangnan University,edu,56e25056153a15eae2a6b10c109f812d2b753cee,citation,https://arxiv.org/pdf/1711.06753.pdf,Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks,2017
106,COFW,cofw,37.4102193,-122.05965487,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
107,COFW,cofw,40.00229045,116.32098908,Tsinghua University,edu,445e3ba7eabcc55b5d24f951b029196b47830684,citation,https://doi.org/10.1109/TMM.2016.2591508,Learning Cascaded Deep Auto-Encoder Networks for Face Alignment,2016
108,COFW,cofw,1.3484104,103.68297965,Nanyang Technological University,edu,445e3ba7eabcc55b5d24f951b029196b47830684,citation,https://doi.org/10.1109/TMM.2016.2591508,Learning Cascaded Deep Auto-Encoder Networks for Face Alignment,2016
109,COFW,cofw,38.99203005,-76.9461029,University of Maryland College Park,edu,1389ba6c3ff34cdf452ede130c738f37dca7e8cb,citation,http://pdfs.semanticscholar.org/1389/ba6c3ff34cdf452ede130c738f37dca7e8cb.pdf,A Convolution Tree with Deconvolution Branches: Exploiting Geometric Relationships for Single Shot Keypoint Detection,2017
110,COFW,cofw,32.87935255,-117.23110049,"University of California, San Diego",edu,b806a31c093b31e98cc5fca7e3ec53f2cc169db9,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7995928,Gaze fixations and dynamics for behavior modeling and prediction of on-road driving maneuvers,2017
111,COFW,cofw,37.4102193,-122.05965487,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
112,COFW,cofw,38.99203005,-76.9461029,University of Maryland College Park,edu,f8e64dd25c3174dff87385db56abc48101b69009,citation,https://arxiv.org/pdf/1802.06713.pdf,Disentangling 3D Pose in A Dendritic CNN for Unconstrained 2D Face Alignment,2018
113,COFW,cofw,43.7047927,-72.2925909,Dartmouth College,edu,df71a00071d5a949f9c31371c2e5ee8b478e7dc8,citation,http://studentlife.cs.dartmouth.edu/facelogging.pdf,Using opportunistic face logging from smartphone to infer mental health: challenges and future directions,2015
114,COFW,cofw,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,2c17d36bab56083293456fe14ceff5497cc97d75,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhu_Unconstrained_Face_Alignment_CVPR_2016_paper.pdf,Unconstrained Face Alignment via Cascaded Compositional Learning,2016
115,COFW,cofw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,2c17d36bab56083293456fe14ceff5497cc97d75,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhu_Unconstrained_Face_Alignment_CVPR_2016_paper.pdf,Unconstrained Face Alignment via Cascaded Compositional Learning,2016
116,COFW,cofw,51.24303255,-0.59001382,University of Surrey,edu,438e7999c937b94f0f6384dbeaa3febff6d283b6,citation,https://arxiv.org/pdf/1705.02402v2.pdf,"Face Detection, Bounding Box Aggregation and Pose Estimation for Robust Facial Landmark Localisation in the Wild",2017
117,COFW,cofw,31.4854255,120.2739581,Jiangnan University,edu,438e7999c937b94f0f6384dbeaa3febff6d283b6,citation,https://arxiv.org/pdf/1705.02402v2.pdf,"Face Detection, Bounding Box Aggregation and Pose Estimation for Robust Facial Landmark Localisation in the Wild",2017
118,COFW,cofw,40.0044795,116.370238,Chinese Academy of Sciences,edu,5ee0103048e1ce46e34a04c45ff2c2c31529b466,citation,https://doi.org/10.1109/ICIP.2015.7350886,Learning occlusion patterns using semantic phrases for object detection,2015
119,COFW,cofw,32.77824165,34.99565673,Open University of Israel,edu,c75e6ce54caf17b2780b4b53f8d29086b391e839,citation,https://arxiv.org/pdf/1802.00542.pdf,"ExpNet: Landmark-Free, Deep, 3D Facial Expressions",2018
120,COFW,cofw,51.24303255,-0.59001382,University of Surrey,edu,96c6f50ce8e1b9e8215b8791dabd78b2bbd5f28d,citation,https://arxiv.org/pdf/1611.05396.pdf,Dynamic Attention-Controlled Cascaded Shape Regression Exploiting Training Data Augmentation and Fuzzy-Set Sample Weighting,2017
121,COFW,cofw,31.4854255,120.2739581,Jiangnan University,edu,96c6f50ce8e1b9e8215b8791dabd78b2bbd5f28d,citation,https://arxiv.org/pdf/1611.05396.pdf,Dynamic Attention-Controlled Cascaded Shape Regression Exploiting Training Data Augmentation and Fuzzy-Set Sample Weighting,2017
122,COFW,cofw,30.60903415,114.3514284,Wuhan University of Technology,edu,258b3b1df82186dd76064ef86b28555e91389b73,citation,https://doi.org/10.1109/ACCESS.2017.2739822,Initial Shape Pool Construction for Facial Landmark Localization Under Occlusion,2017
123,COFW,cofw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,85674b1b6007634f362cbe9b921912b697c0a32c,citation,http://pdfs.semanticscholar.org/8567/4b1b6007634f362cbe9b921912b697c0a32c.pdf,Optimizing Facial Landmark Detection by Facial Attribute Learning,2014
124,COFW,cofw,43.07982815,-89.43066425,University of Wisconsin Madison,edu,716d6c2eb8a0d8089baf2087ce9fcd668cd0d4c0,citation,http://pdfs.semanticscholar.org/ec7f/c7bf79204166f78c27e870b620205751fff6.pdf,Pose-Robust 3D Facial Landmark Estimation from a Single 2D Image,2016
125,COFW,cofw,40.0141905,-83.0309143,University of Electronic Science and Technology of China,edu,2a84f7934365f05b6707ea0ac225210f78e547af,citation,https://doi.org/10.1109/ICPR.2016.7899690,A joint facial point detection method of deep convolutional network and shape regression,2016
126,COFW,cofw,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
127,COFW,cofw,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
128,COFW,cofw,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
129,COFW,cofw,1.2962018,103.77689944,National University of Singapore,edu,30cd39388b5c1aae7d8153c0ab9d54b61b474ffe,citation,http://pdfs.semanticscholar.org/3be8/f1f7501978287af8d7ebfac5963216698249.pdf,Deep Cascaded Regression for Face Alignment,2015
130,COFW,cofw,23.09461185,113.28788994,Sun Yat-Sen University,edu,30cd39388b5c1aae7d8153c0ab9d54b61b474ffe,citation,http://pdfs.semanticscholar.org/3be8/f1f7501978287af8d7ebfac5963216698249.pdf,Deep Cascaded Regression for Face Alignment,2015
131,COFW,cofw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,8a3c5507237957d013a0fe0f082cab7f757af6ee,citation,http://pdfs.semanticscholar.org/fcd7/1c18192928a2e0b264edd4d919ab2f8f652a.pdf,Facial Landmark Detection by Deep Multi-task Learning,2014
132,COFW,cofw,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,cf5c9b521c958b84bb63bea9d5cbb522845e4ba7,citation,http://pdfs.semanticscholar.org/cf5c/9b521c958b84bb63bea9d5cbb522845e4ba7.pdf,Towards Arbitrary-View Face Alignment by Recommendation Trees,2015
133,COFW,cofw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,cf5c9b521c958b84bb63bea9d5cbb522845e4ba7,citation,http://pdfs.semanticscholar.org/cf5c/9b521c958b84bb63bea9d5cbb522845e4ba7.pdf,Towards Arbitrary-View Face Alignment by Recommendation Trees,2015
134,COFW,cofw,30.642769,104.06751175,"Sichuan University, Chengdu",edu,b29b42f7ab8d25d244bfc1413a8d608cbdc51855,citation,http://pdfs.semanticscholar.org/b29b/42f7ab8d25d244bfc1413a8d608cbdc51855.pdf,Effective face landmark localization via single deep network,2017
135,COFW,cofw,23.0490047,113.3971571,South China University of China,edu,7d7be6172fc2884e1da22d1e96d5899a29831ad2,citation,http://pdfs.semanticscholar.org/7d7b/e6172fc2884e1da22d1e96d5899a29831ad2.pdf,L2GSCI: Local to Global Seam Cutting and Integrating for Accurate Face Contour Extraction,2017
136,COFW,cofw,22.46935655,114.19474194,Education University of Hong Kong,edu,7d7be6172fc2884e1da22d1e96d5899a29831ad2,citation,http://pdfs.semanticscholar.org/7d7b/e6172fc2884e1da22d1e96d5899a29831ad2.pdf,L2GSCI: Local to Global Seam Cutting and Integrating for Accurate Face Contour Extraction,2017
137,COFW,cofw,39.9922379,116.30393816,Peking University,edu,8c048be9dd2b601808b893b5d3d51f00907bdee0,citation,https://doi.org/10.1631/FITEE.1600041,Spontaneous versus posed smile recognition via region-specific texture descriptor and geometric facial dynamics,2017
138,COFW,cofw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,433a6d6d2a3ed8a6502982dccc992f91d665b9b3,citation,http://pdfs.semanticscholar.org/433a/6d6d2a3ed8a6502982dccc992f91d665b9b3.pdf,Transferring Landmark Annotations for Cross-Dataset Face Alignment,2014
139,COFW,cofw,40.00229045,116.32098908,Tsinghua University,edu,433a6d6d2a3ed8a6502982dccc992f91d665b9b3,citation,http://pdfs.semanticscholar.org/433a/6d6d2a3ed8a6502982dccc992f91d665b9b3.pdf,Transferring Landmark Annotations for Cross-Dataset Face Alignment,2014
140,COFW,cofw,47.05821,15.46019568,Graz University of Technology,edu,96a9ca7a8366ae0efe6b58a515d15b44776faf6e,citation,https://arxiv.org/pdf/1609.00129.pdf,Grid Loss: Detecting Occluded Faces,2016
141,COFW,cofw,40.0044795,116.370238,Chinese Academy of Sciences,edu,3d18ce183b5a5b4dcaa1216e30b774ef49eaa46f,citation,https://arxiv.org/pdf/1511.07212.pdf,Face Alignment in Full Pose Range: A 3D Total Solution,2017
142,COFW,cofw,42.718568,-84.47791571,Michigan State University,edu,3d18ce183b5a5b4dcaa1216e30b774ef49eaa46f,citation,https://arxiv.org/pdf/1511.07212.pdf,Face Alignment in Full Pose Range: A 3D Total Solution,2017
143,COFW,cofw,31.30104395,121.50045497,Fudan University,edu,862d17895fe822f7111e737cbcdd042ba04377e8,citation,http://pdfs.semanticscholar.org/862d/17895fe822f7111e737cbcdd042ba04377e8.pdf,Semi-Latent GAN: Learning to generate and modify facial images from attributes,2017
144,COFW,cofw,42.718568,-84.47791571,Michigan State University,edu,085ceda1c65caf11762b3452f87660703f914782,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Jourabloo_Large-Pose_Face_Alignment_CVPR_2016_paper.pdf,Large-Pose Face Alignment via CNN-Based Dense 3D Model Fitting,2016
145,COFW,cofw,39.977217,116.337632,Microsoft Research Asia,company,9aade3d26996ce7ef6d657130464504b8d812534,citation,https://doi.org/10.1109/TNNLS.2016.2618340,Face Alignment With Deep Regression,2018
146,COFW,cofw,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,9aade3d26996ce7ef6d657130464504b8d812534,citation,https://doi.org/10.1109/TNNLS.2016.2618340,Face Alignment With Deep Regression,2018
147,COFW,cofw,40.0044795,116.370238,Chinese Academy of Sciences,edu,055cd8173536031e189628c879a2acad6cf2a5d0,citation,https://doi.org/10.1109/BTAS.2017.8272740,Fast multi-view face alignment via multi-task auto-encoders,2017
148,COFW,cofw,36.20304395,117.05842113,Tianjin University,edu,4223917177405eaa6bdedca061eb28f7b440ed8e,citation,http://pdfs.semanticscholar.org/4223/917177405eaa6bdedca061eb28f7b440ed8e.pdf,B-spline Shape from Motion & Shading: An Automatic Free-form Surface Modeling for Face Reconstruction,2016
149,COFW,cofw,22.304572,114.17976285,Hong Kong Polytechnic University,edu,4cfa8755fe23a8a0b19909fa4dec54ce6c1bd2f7,citation,https://arxiv.org/pdf/1611.09956v1.pdf,Efficient likelihood Bayesian constrained local model,2017
150,COFW,cofw,40.0044795,116.370238,Chinese Academy of Sciences,edu,2a4153655ad1169d482e22c468d67f3bc2c49f12,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhu_Face_Alignment_Across_CVPR_2016_paper.pdf,Face Alignment Across Large Poses: A 3D Solution,2016
151,COFW,cofw,42.718568,-84.47791571,Michigan State University,edu,2a4153655ad1169d482e22c468d67f3bc2c49f12,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhu_Face_Alignment_Across_CVPR_2016_paper.pdf,Face Alignment Across Large Poses: A 3D Solution,2016
152,COFW,cofw,40.0044795,116.370238,Chinese Academy of Sciences,edu,090ff8f992dc71a1125636c1adffc0634155b450,citation,http://pdfs.semanticscholar.org/090f/f8f992dc71a1125636c1adffc0634155b450.pdf,Topic-Aware Deep Auto-Encoders (TDA) for Face Alignment,2014
153,COFW,cofw,51.49887085,-0.17560797,Imperial College London,edu,090ff8f992dc71a1125636c1adffc0634155b450,citation,http://pdfs.semanticscholar.org/090f/f8f992dc71a1125636c1adffc0634155b450.pdf,Topic-Aware Deep Auto-Encoders (TDA) for Face Alignment,2014
154,COFW,cofw,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,090ff8f992dc71a1125636c1adffc0634155b450,citation,http://pdfs.semanticscholar.org/090f/f8f992dc71a1125636c1adffc0634155b450.pdf,Topic-Aware Deep Auto-Encoders (TDA) for Face Alignment,2014
155,COFW,cofw,40.0044795,116.370238,Chinese Academy of Sciences,edu,26949c1ba7f55f0c389000aa234238bf01a32d3b,citation,https://doi.org/10.1109/ICIP.2017.8296814,Coupled cascade regression for simultaneous facial landmark detection and head pose estimation,2017
156,COFW,cofw,42.7298459,-73.67950216,Rensselaer Polytechnic Institute,edu,26949c1ba7f55f0c389000aa234238bf01a32d3b,citation,https://doi.org/10.1109/ICIP.2017.8296814,Coupled cascade regression for simultaneous facial landmark detection and head pose estimation,2017
157,COFW,cofw,-27.49741805,153.01316956,University of Queensland,edu,de79437f74e8e3b266afc664decf4e6e4bdf34d7,citation,https://doi.org/10.1109/IVCNZ.2016.7804415,To face or not to face: Towards reducing false positive of face detection,2016
158,COFW,cofw,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,a26fd9df58bb76d6c7a3254820143b3da5bd584b,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8446759,Monitor Pupils' Attention by Image Super-Resolution and Anomaly Detection,2017
159,COFW,cofw,51.5247272,-0.03931035,Queen Mary University of London,edu,0f81b0fa8df5bf3fcfa10f20120540342a0c92e5,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2015.7299100,"Mirror, mirror on the wall, tell me, is the error small?",2015
160,COFW,cofw,31.2284923,121.40211389,East China Normal University,edu,83295bce2340cb87901499cff492ae6ff3365475,citation,https://arxiv.org/pdf/1808.01558.pdf,Deep Multi-Center Learning for Face Alignment,2018
161,COFW,cofw,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,83295bce2340cb87901499cff492ae6ff3365475,citation,https://arxiv.org/pdf/1808.01558.pdf,Deep Multi-Center Learning for Face Alignment,2018
162,COFW,cofw,52.9387428,-1.20029569,University of Nottingham,edu,721e5ba3383b05a78ef1dfe85bf38efa7e2d611d,citation,http://pdfs.semanticscholar.org/74f1/9d0986c9d39aabb359abaa2a87a248a48deb.pdf,"BULAT, TZIMIROPOULOS: CONVOLUTIONAL AGGREGATION OF LOCAL EVIDENCE 1 Convolutional aggregation of local evidence for large pose face alignment",2016
163,COFW,cofw,32.0565957,118.77408833,Nanjing University,edu,ad5a35a251e07628dd035c68e44a64c53652be6b,citation,https://doi.org/10.1016/j.patcog.2016.12.024,Robust facial landmark tracking via cascade regression,2017
164,COFW,cofw,39.9922379,116.30393816,Peking University,edu,5df17c81c266cf2ebb0778e48e825905e161a8d9,citation,https://doi.org/10.1109/TMM.2016.2520091,A Novel Lip Descriptor for Audio-Visual Keyword Spotting Based on Adaptive Decision Fusion,2016
165,COFW,cofw,45.2182986,5.80703193,INRIA Grenoble,edu,5df17c81c266cf2ebb0778e48e825905e161a8d9,citation,https://doi.org/10.1109/TMM.2016.2520091,A Novel Lip Descriptor for Audio-Visual Keyword Spotting Based on Adaptive Decision Fusion,2016
166,COFW,cofw,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,3d9e44d8f8bc2663192c7ce668ccbbb084e466e4,citation,http://doi.ieeecomputersociety.org/10.1109/ICME.2017.8019505,Learning a multi-center convolutional network for unconstrained face alignment,2017
167,COFW,cofw,53.8338371,10.7035939,Institute of Systems and Robotics,edu,6604fd47f92ce66dd0c669dd66b347b80e17ebc9,citation,https://pdfs.semanticscholar.org/6604/fd47f92ce66dd0c669dd66b347b80e17ebc9.pdf,Simultaneous Cascaded Regression,2018
168,COFW,cofw,25.01353105,121.54173736,National Taiwan University of Science and Technology,edu,e4e07f5f201c6986e93ddb42dcf11a43c339ea2e,citation,https://doi.org/10.1109/BTAS.2017.8272722,Cross-pose landmark localization using multi-dropout framework,2017
169,COFW,cofw,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,9048732c8591a92a1f4f589b520a733f07578f80,citation,https://doi.org/10.1109/CISP-BMEI.2017.8301921,Improved CNN-based facial landmarks tracking via ridge regression at 150 Fps on mobile devices,2017
170,COFW,cofw,31.83907195,117.26420748,University of Science and Technology of China,edu,084bd02d171e36458f108f07265386f22b34a1ae,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Ren_Face_Alignment_at_2014_CVPR_paper.pdf,Face Alignment at 3000 FPS via Regressing Local Binary Features,2014
171,COFW,cofw,33.6431901,-117.84016494,"University of California, Irvine",edu,65126e0b1161fc8212643b8ff39c1d71d262fbc1,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Ghiasi_Occlusion_Coherence_Localizing_2014_CVPR_paper.pdf,Occlusion Coherence: Localizing Occluded Faces with a Hierarchical Deformable Part Model,2014
172,COFW,cofw,17.4454957,78.34854698,International Institute of Information Technology,edu,156cd2a0e2c378e4c3649a1d046cd080d3338bca,citation,http://pdfs.semanticscholar.org/156c/d2a0e2c378e4c3649a1d046cd080d3338bca.pdf,Exemplar based approaches on Face Fiducial Detection and Frontalization,2017
173,COFW,cofw,-27.5953995,-48.6154218,University of Campinas,edu,159b1e3c3ed0982061dae3cc8ab7d9b149a0cdb1,citation,https://doi.org/10.1109/TIP.2017.2694226,Weak Classifier for Density Estimation in Eye Localization and Tracking,2017
174,COFW,cofw,-22.9541412,-43.1753638,Universidade Federal do Rio de Janeiro,edu,159b1e3c3ed0982061dae3cc8ab7d9b149a0cdb1,citation,https://doi.org/10.1109/TIP.2017.2694226,Weak Classifier for Density Estimation in Eye Localization and Tracking,2017
175,COFW,cofw,38.99203005,-76.9461029,University of Maryland College Park,edu,b2cd92d930ed9b8d3f9dfcfff733f8384aa93de8,citation,http://pdfs.semanticscholar.org/b2cd/92d930ed9b8d3f9dfcfff733f8384aa93de8.pdf,"HyperFace: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition",2016
176,COFW,cofw,39.2899685,-76.62196103,University of Maryland,edu,b2cd92d930ed9b8d3f9dfcfff733f8384aa93de8,citation,http://pdfs.semanticscholar.org/b2cd/92d930ed9b8d3f9dfcfff733f8384aa93de8.pdf,"HyperFace: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition",2016
177,COFW,cofw,32.87935255,-117.23110049,"University of California, San Diego",edu,d4a5eaf2e9f2fd3e264940039e2cbbf08880a090,citation,https://arxiv.org/pdf/1802.02137.pdf,An Occluded Stacked Hourglass Approach to Facial Landmark Localization and Occlusion Estimation,2017
178,COFW,cofw,30.274084,120.15507,Alibaba,company,89497854eada7e32f06aa8f3c0ceedc0e91ecfef,citation,https://doi.org/10.1109/TIP.2017.2784571,Deep Context-Sensitive Facial Landmark Detection With Tree-Structured Modeling,2018
179,COFW,cofw,30.19331415,120.11930822,Zhejiang University,edu,89497854eada7e32f06aa8f3c0ceedc0e91ecfef,citation,https://doi.org/10.1109/TIP.2017.2784571,Deep Context-Sensitive Facial Landmark Detection With Tree-Structured Modeling,2018
180,COFW,cofw,32.0565957,118.77408833,Nanjing University,edu,9cb7b3b14fd01cc2ed76784ab76304132dab6ff3,citation,https://doi.org/10.1109/ICIP.2015.7351174,Facial landmark detection via pose-induced auto-encoder networks,2015
181,COFW,cofw,12.9803537,77.6975101,"Samsung R&D Institute, Bangalore, India",company,cf736f596bf881ca97ec4b29776baaa493b9d50e,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7952629,Low Dimensional Deep Features for facial landmark alignment,2017
182,COFW,cofw,46.0658836,11.1159894,University of Trento,edu,f201baf618574108bcee50e9a8b65f5174d832ee,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8031057,Viewpoint-Consistent 3D Face Alignment,2018
183,COFW,cofw,13.65450525,100.49423171,Robotics Institute,edu,f201baf618574108bcee50e9a8b65f5174d832ee,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8031057,Viewpoint-Consistent 3D Face Alignment,2018
184,COFW,cofw,50.74223495,-1.89433739,Bournemouth University,edu,370b6b83c7512419188f5373a962dd3175a56a9b,citation,https://pdfs.semanticscholar.org/370b/6b83c7512419188f5373a962dd3175a56a9b.pdf,Face Alignment Refinement via Exploiting Low-Rank property and Temporal Stability,2017
185,COFW,cofw,30.19331415,120.11930822,Zhejiang University,edu,370b6b83c7512419188f5373a962dd3175a56a9b,citation,https://pdfs.semanticscholar.org/370b/6b83c7512419188f5373a962dd3175a56a9b.pdf,Face Alignment Refinement via Exploiting Low-Rank property and Temporal Stability,2017
186,COFW,cofw,32.0565957,118.77408833,Nanjing University,edu,63c74794aedb40dd6b1650352a2da7a968180302,citation,https://doi.org/10.1016/j.neucom.2016.09.015,Recurrent neural network for facial landmark detection,2017
187,COFW,cofw,38.88140235,121.52281098,Dalian University of Technology,edu,940e5c45511b63f609568dce2ad61437c5e39683,citation,https://doi.org/10.1109/TIP.2015.2390976,Fiducial Facial Point Extraction Using a Novel Projective Invariant,2015
188,COFW,cofw,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
189,COFW,cofw,40.0044795,116.370238,Chinese Academy of Sciences,edu,a820941eaf03077d68536732a4d5f28d94b5864a,citation,http://www.cv-foundation.org/openaccess/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
190,COFW,cofw,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,a820941eaf03077d68536732a4d5f28d94b5864a,citation,http://www.cv-foundation.org/openaccess/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
191,COFW,cofw,34.0687788,-118.4450094,"University of California, Los Angeles",edu,195d331c958f2da3431f37a344559f9bce09c0f7,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/ext/2B_066_ext.pdf,Parsing occluded people by flexible compositions,2015
192,COFW,cofw,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,5f448ab700528888019542e6fea1d1e0db6c35f2,citation,https://doi.org/10.1109/LSP.2016.2533721,Transferred Deep Convolutional Neural Network Features for Extensive Facial Landmark Localization,2016
193,COFW,cofw,31.846918,117.29053367,Hefei University of Technology,edu,2f73203fd71b755a9601d00fc202bbbd0a595110,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8394868,Micro-expression Analysis by Fusing Deep Convolutional Neural Network and Optical Flow,2018
194,COFW,cofw,33.620813,133.719755,Kochi University of Technology,edu,2f73203fd71b755a9601d00fc202bbbd0a595110,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8394868,Micro-expression Analysis by Fusing Deep Convolutional Neural Network and Optical Flow,2018
195,COFW,cofw,32.0565957,118.77408833,Nanjing University,edu,5b0bf1063b694e4b1575bb428edb4f3451d9bf04,citation,http://doi.ieeecomputersociety.org/10.1109/ICCVW.2015.131,Facial Shape Tracking via Spatio-Temporal Cascade Shape Regression,2015
196,COFW,cofw,31.83907195,117.26420748,University of Science and Technology of China,edu,dd715a98dab34437ad05758b20cc640c2cdc5715,citation,https://doi.org/10.1007/s41095-017-0082-8,Joint head pose and facial landmark regression from depth images,2017
197,COFW,cofw,52.17638955,0.14308882,University of Cambridge,edu,2aa2b312da1554a7f3e48f71f2fce7ade6d5bf40,citation,http://www.cl.cam.ac.uk/~pr10/publications/fg17.pdf,Estimating Sheep Pain Level Using Facial Action Unit Detection,2017
198,COFW,cofw,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
199,COFW,cofw,13.65450525,100.49423171,Robotics Institute,edu,b6f15bf8723b2d5390122442ab04630d2d3878d8,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7163142,Dense 3D face alignment from 2D videos in real-time,2015
200,COFW,cofw,32.77824165,34.99565673,Open University of Israel,edu,62e913431bcef5983955e9ca160b91bb19d9de42,citation,http://pdfs.semanticscholar.org/62e9/13431bcef5983955e9ca160b91bb19d9de42.pdf,Facial Landmark Detection with Tweaked Convolutional Neural Networks,2015
201,COFW,cofw,37.4102193,-122.05965487,Carnegie Mellon University,edu,7cfbf90368553333b47731729e0e358479c25340,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7346480,"Towards a Unified Framework for Pose, Expression, and Occlusion Tolerant Automatic Facial Alignment",2016
202,COFW,cofw,40.986904,29.0530981,"Marmara University, Istanbul, Turkey",edu,a78025f39cf78f2fc66c4b2942fbe5bad3ea65fc,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8404357,A comparison of facial landmark detection methods,2018
203,COFW,cofw,41.6771297,26.5557145,"Trakya University, Edirne, Turkey",edu,a78025f39cf78f2fc66c4b2942fbe5bad3ea65fc,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8404357,A comparison of facial landmark detection methods,2018
204,COFW,cofw,65.0592157,25.46632601,University of Oulu,edu,193debca0be1c38dabc42dc772513e6653fd91d8,citation,http://ibug.doc.ic.ac.uk/media/uploads/documents/trigeorgis2016mnemonic.pdf,Mnemonic Descent Method: A Recurrent Process Applied for End-to-End Face Alignment,2016
205,COFW,cofw,51.5217668,-0.13019072,University of London,edu,193debca0be1c38dabc42dc772513e6653fd91d8,citation,http://ibug.doc.ic.ac.uk/media/uploads/documents/trigeorgis2016mnemonic.pdf,Mnemonic Descent Method: A Recurrent Process Applied for End-to-End Face Alignment,2016
206,COFW,cofw,51.49887085,-0.17560797,Imperial College London,edu,193debca0be1c38dabc42dc772513e6653fd91d8,citation,http://ibug.doc.ic.ac.uk/media/uploads/documents/trigeorgis2016mnemonic.pdf,Mnemonic Descent Method: A Recurrent Process Applied for End-to-End Face Alignment,2016
207,COFW,cofw,50.7791703,6.06728733,RWTH Aachen University,edu,141ee531d03fb6626043e33dd8f269a6f1f63a4b,citation,https://arxiv.org/pdf/1808.09316.pdf,How Robust is 3D Human Pose Estimation to Occlusion?,2018
208,COFW,cofw,42.718568,-84.47791571,Michigan State University,edu,37ce1d3a6415d6fc1760964e2a04174c24208173,citation,http://www.cse.msu.edu/~liuxm/publication/Jourabloo_Liu_ICCV2015.pdf,Pose-Invariant 3D Face Alignment,2015
209,COFW,cofw,38.88140235,121.52281098,Dalian University of Technology,edu,5f4219118556d2c627137827a617cf4e26242a6e,citation,https://doi.org/10.1109/TMM.2017.2751143,Explicit Shape Regression With Characteristic Number for Facial Landmark Localization,2018
210,COFW,cofw,42.2942142,-83.71003894,University of Michigan,edu,860588fafcc80c823e66429fadd7e816721da42a,citation,https://arxiv.org/pdf/1804.04412.pdf,Unsupervised Discovery of Object Landmarks as Structural Representations,2018
211,COFW,cofw,26.88111275,112.62850666,Hunan University,edu,4b936847f39094d6cb0bde68cea654d948c4735d,citation,http://doi.org/10.1007/s11042-016-3470-7,Face alignment under occlusion based on local and global feature regression,2016
212,COFW,cofw,34.13710185,-118.12527487,California Institute of Technology,edu,56ae6d94fc6097ec4ca861f0daa87941d1c10b70,citation,http://pdfs.semanticscholar.org/56ae/6d94fc6097ec4ca861f0daa87941d1c10b70.pdf,Distance Estimation of an Unknown Person from a Portrait,2014
213,COFW,cofw,40.0044795,116.370238,Chinese Academy of Sciences,edu,51b42da0706a1260430f27badcf9ee6694768b9b,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7471882,Shape initialization without ground truth for face alignment,2016
214,COFW,cofw,35.9542493,-83.9307395,University of Tennessee,edu,c2e03efd8c5217188ab685e73cc2e52c54835d1a,citation,http://doi.ieeecomputersociety.org/10.1109/WACV.2016.7477585,Deep tree-structured face: A unified representation for multi-task facial biometrics,2016
215,COFW,cofw,40.47913175,-74.43168868,Rutgers University,edu,3b470b76045745c0ef5321e0f1e0e6a4b1821339,citation,http://pdfs.semanticscholar.org/8e72/fa02f2d90ba31f31e0a7aa96a6d3e10a66fc.pdf,Consensus of Regression for Occlusion-Robust Facial Feature Localization,2014
216,COFW,cofw,30.642769,104.06751175,"Sichuan University, Chengdu",edu,3080026f2f0846d520bd5bacb0cb2acea0ffe16b,citation,https://doi.org/10.1109/BTAS.2017.8272690,2.5D cascaded regression for robust facial landmark detection,2017
217,COFW,cofw,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,3fdfd6fa7a1cc9142de1f53e4ac7c2a7ac64c2e3,citation,http://pdfs.semanticscholar.org/3fdf/d6fa7a1cc9142de1f53e4ac7c2a7ac64c2e3.pdf,Intensity-Depth Face Alignment Using Cascade Shape Regression,2015
218,COFW,cofw,42.718568,-84.47791571,Michigan State University,edu,ec8ec2dfd73cf3667f33595fef84c95c42125945,citation,https://arxiv.org/pdf/1707.06286.pdf,Pose-Invariant Face Alignment with a Single CNN,2017
219,COFW,cofw,39.977217,116.337632,Microsoft Research Asia,company,898ff1bafee2a6fb3c848ad07f6f292416b5f07d,citation,https://doi.org/10.1109/TIP.2016.2518867,Face Alignment via Regressing Local Binary Features,2016
220,COFW,cofw,31.83907195,117.26420748,University of Science and Technology of China,edu,898ff1bafee2a6fb3c848ad07f6f292416b5f07d,citation,https://doi.org/10.1109/TIP.2016.2518867,Face Alignment via Regressing Local Binary Features,2016
221,COFW,cofw,47.6423318,-122.1369302,Microsoft,company,898ff1bafee2a6fb3c848ad07f6f292416b5f07d,citation,https://doi.org/10.1109/TIP.2016.2518867,Face Alignment via Regressing Local Binary Features,2016
222,COFW,cofw,39.977217,116.337632,Microsoft Research Asia,company,63d865c66faaba68018defee0daf201db8ca79ed,citation,http://pdfs.semanticscholar.org/63d8/65c66faaba68018defee0daf201db8ca79ed.pdf,Deep Regression for Face Alignment,2014
223,COFW,cofw,52.17638955,0.14308882,University of Cambridge,edu,be57d2aaab615ec8bc1dd2dba8bee41a4d038b85,citation,http://doi.acm.org/10.1145/2946796,Automatic Analysis of Naturalistic Hand-Over-Face Gestures,2016
224,COFW,cofw,-33.8840504,151.1992254,University of Technology,edu,336488746cc76e7f13b0ec68ccfe4df6d76cdc8f,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7762938,Adaptive Cascade Regression Model For Robust Face Alignment,2017
225,COFW,cofw,42.36782045,-71.12666653,Harvard University,edu,023be757b1769ecb0db810c95c010310d7daf00b,citation,http://pdfs.semanticscholar.org/023b/e757b1769ecb0db810c95c010310d7daf00b.pdf,Face Alignment Assisted by Head Pose Estimation,2015
226,COFW,cofw,51.5247272,-0.03931035,Queen Mary University of London,edu,023be757b1769ecb0db810c95c010310d7daf00b,citation,http://pdfs.semanticscholar.org/023b/e757b1769ecb0db810c95c010310d7daf00b.pdf,Face Alignment Assisted by Head Pose Estimation,2015
227,COFW,cofw,52.17638955,0.14308882,University of Cambridge,edu,023be757b1769ecb0db810c95c010310d7daf00b,citation,http://pdfs.semanticscholar.org/023b/e757b1769ecb0db810c95c010310d7daf00b.pdf,Face Alignment Assisted by Head Pose Estimation,2015
228,COFW,cofw,39.2899685,-76.62196103,University of Maryland,edu,93420d9212dd15b3ef37f566e4d57e76bb2fab2f,citation,https://arxiv.org/pdf/1611.00851.pdf,An All-In-One Convolutional Neural Network for Face Analysis,2017
229,COFW,cofw,34.13710185,-118.12527487,California Institute of Technology,edu,72282287f25c5419dc6fd9e89ec9d86d660dc0b5,citation,https://arxiv.org/pdf/1609.07495v1.pdf,A Rotation Invariant Latent Factor Model for Moveme Discovery from Static Poses,2016
230,COFW,cofw,51.5247272,-0.03931035,Queen Mary University of London,edu,f11c76efdc9651db329c8c862652820d61933308,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7163100,Inference of personality traits and affect schedule by analysis of spontaneous reactions to affective videos,2015
231,COFW,cofw,46.0658836,11.1159894,University of Trento,edu,f11c76efdc9651db329c8c862652820d61933308,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7163100,Inference of personality traits and affect schedule by analysis of spontaneous reactions to affective videos,2015