index,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year
0,AFLW,aflw,0.0,0.0,,,a74251efa970b92925b89eeef50a5e37d9281ad0,main,http://lrs.icg.tugraz.at/pubs/koestinger_befit_11.pdf,"Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization",2011
1,AFLW,aflw,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
2,AFLW,aflw,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
3,AFLW,aflw,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
4,AFLW,aflw,-27.47715625,153.02841004,Queensland University of Technology,edu,6342a4c54835c1e14159495373ab18b4233d2d9b,citation,http://pdfs.semanticscholar.org/6342/a4c54835c1e14159495373ab18b4233d2d9b.pdf,Towards Pose-robust Face Recognition on Video,2014
5,AFLW,aflw,39.993008,116.329882,SenseTime,company,38183fe28add21693729ddeaf3c8a90a2d5caea3,citation,http://arxiv.org/abs/1706.09876,Scale-Aware Face Detection,2017
6,AFLW,aflw,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
7,AFLW,aflw,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
8,AFLW,aflw,47.05821,15.46019568,Graz University of Technology,edu,4ab10174a4f98f7e2da7cf6ccfeb9bc64c8e7da8,citation,http://pdfs.semanticscholar.org/4ab1/0174a4f98f7e2da7cf6ccfeb9bc64c8e7da8.pdf,Efficient Metric Learning for Real-World Face Recognition,2013
9,AFLW,aflw,22.53521465,113.9315911,Shenzhen University,edu,32ecbbd76fdce249f9109594eee2d52a1cafdfc7,citation,http://pdfs.semanticscholar.org/32ec/bbd76fdce249f9109594eee2d52a1cafdfc7.pdf,Object Specific Deep Learning Feature and Its Application to Face Detection,2016
10,AFLW,aflw,52.9387428,-1.20029569,University of Nottingham,edu,32ecbbd76fdce249f9109594eee2d52a1cafdfc7,citation,http://pdfs.semanticscholar.org/32ec/bbd76fdce249f9109594eee2d52a1cafdfc7.pdf,Object Specific Deep Learning Feature and Its Application to Face Detection,2016
11,AFLW,aflw,40.0044795,116.370238,Chinese Academy of Sciences,edu,4e6c17966efae956133bf8f22edeffc24a0470c1,citation,http://pdfs.semanticscholar.org/4e6c/17966efae956133bf8f22edeffc24a0470c1.pdf,Face Classification: A Specialized Benchmark Study,2016
12,AFLW,aflw,22.15263985,113.56803206,Macau University of Science and Technology,edu,4e6c17966efae956133bf8f22edeffc24a0470c1,citation,http://pdfs.semanticscholar.org/4e6c/17966efae956133bf8f22edeffc24a0470c1.pdf,Face Classification: A Specialized Benchmark Study,2016
13,AFLW,aflw,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,4e6c17966efae956133bf8f22edeffc24a0470c1,citation,http://pdfs.semanticscholar.org/4e6c/17966efae956133bf8f22edeffc24a0470c1.pdf,Face Classification: A Specialized Benchmark Study,2016
14,AFLW,aflw,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
15,AFLW,aflw,29.6328784,-82.3490133,University of Florida,edu,441bf5f7fe7d1a3939d8b200eca9b4bb619449a9,citation,http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W08/papers/Sundararajan_Head_Pose_Estimation_2015_CVPR_paper.pdf,Head pose estimation in the wild using approximate view manifolds,2015
16,AFLW,aflw,37.4102193,-122.05965487,Carnegie Mellon University,edu,1ca815327e62c70f4ee619a836e05183ef629567,citation,http://www.humansensing.cs.cmu.edu/sites/default/files/Xiong_Global_Supervised_Descent_2015_CVPR_paper.pdf,Global supervised descent method,2015
17,AFLW,aflw,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,6495d989fe33b19d2b7755f9077d8b5bf3190151,citation,https://arxiv.org/pdf/1803.07835.pdf,Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network,2018
18,AFLW,aflw,40.62984145,22.9588935,Aristotle University of Thessaloniki,edu,ccebd3bf069f5c73ea2ccc5791976f894bc6023d,citation,https://doi.org/10.1109/ICPR.2016.7900186,Face detection based on deep convolutional neural networks exploiting incremental facial part learning,2016
19,AFLW,aflw,51.24303255,-0.59001382,University of Surrey,edu,c146aa6d56233ce700032f1cb179700778557601,citation,https://arxiv.org/pdf/1708.07199.pdf,3D Morphable Models as Spatial Transformer Networks,2017
20,AFLW,aflw,53.94540365,-1.03138878,University of York,edu,c146aa6d56233ce700032f1cb179700778557601,citation,https://arxiv.org/pdf/1708.07199.pdf,3D Morphable Models as Spatial Transformer Networks,2017
21,AFLW,aflw,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
22,AFLW,aflw,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
23,AFLW,aflw,37.4102193,-122.05965487,Carnegie Mellon University,edu,b1fdd4ae17d82612cefd4e78b690847b071379d3,citation,https://pdfs.semanticscholar.org/4fc5/416b6c7173d3462e5be796bda3ad8d5645a1.pdf,Supervised Descent Method,2015
24,AFLW,aflw,40.0044795,116.370238,Chinese Academy of Sciences,edu,a5f35880477ae82902c620245e258cf854c09be9,citation,http://doi.org/10.1016/j.imavis.2013.12.004,Face detection by structural models,2014
25,AFLW,aflw,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
26,AFLW,aflw,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
27,AFLW,aflw,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,d3b0839324d0091e70ce34f44c979b9366547327,citation,https://arxiv.org/pdf/1804.10743.pdf,Precise Box Score: Extract More Information from Datasets to Improve the Performance of Face Detection,2018
28,AFLW,aflw,47.5612651,7.5752961,University of Basel,edu,7caa3a74313f9a7a2dd5b4c2cd7f825d895d3794,citation,http://doi.org/10.1007/s11263-016-0967-5,Markov Chain Monte Carlo for Automated Face Image Analysis,2016
29,AFLW,aflw,42.718568,-84.47791571,Michigan State University,edu,cd55fb30737625e86454a2861302b96833ed549d,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7139094,Annotating Unconstrained Face Imagery: A scalable approach,2015
30,AFLW,aflw,38.95187,-77.363259,"Noblis, Falls Church, VA, U.S.A.",company,cd55fb30737625e86454a2861302b96833ed549d,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7139094,Annotating Unconstrained Face Imagery: A scalable approach,2015
31,AFLW,aflw,51.7534538,-1.25400997,University of Oxford,edu,7117ed0be436c0291bc6fb6ea6db18de74e2464a,citation,https://pdfs.semanticscholar.org/7117/ed0be436c0291bc6fb6ea6db18de74e2464a.pdf,Spatial Transformations,2017
32,AFLW,aflw,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
33,AFLW,aflw,47.05821,15.46019568,Graz University of Technology,edu,5c8672c0d2f28fd5d2d2c4b9818fcff43fb01a48,citation,http://pdfs.semanticscholar.org/5c86/72c0d2f28fd5d2d2c4b9818fcff43fb01a48.pdf,Robust Face Detection by Simple Means,2012
34,AFLW,aflw,30.642769,104.06751175,"Sichuan University, Chengdu",edu,5cbe1445d683d605b31377881ac8540e1d17adf0,citation,https://arxiv.org/pdf/1509.06161.pdf,On 3D face reconstruction via cascaded regression in shape space,2017
35,AFLW,aflw,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
36,AFLW,aflw,22.53521465,113.9315911,Shenzhen University,edu,287de191c49a3caa38ad7594093045dfba1eb420,citation,https://doi.org/10.23919/MVA.2017.7986829,Object specific deep feature and its application to face detection,2017
37,AFLW,aflw,52.9387428,-1.20029569,University of Nottingham,edu,287de191c49a3caa38ad7594093045dfba1eb420,citation,https://doi.org/10.23919/MVA.2017.7986829,Object specific deep feature and its application to face detection,2017
38,AFLW,aflw,40.0044795,116.370238,Chinese Academy of Sciences,edu,2f04ba0f74df046b0080ca78e56898bd4847898b,citation,http://arxiv.org/abs/1407.4023,Aggregate channel features for multi-view face detection,2014
39,AFLW,aflw,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
40,AFLW,aflw,38.99203005,-76.9461029,University of Maryland College Park,edu,4f36c14d1453fc9d6481b09c5a09e91d8d9ee47a,citation,http://pdfs.semanticscholar.org/4f36/c14d1453fc9d6481b09c5a09e91d8d9ee47a.pdf,Video-Based Face Recognition Using the Intra/Extra-Personal Difference Dictionary,2014
41,AFLW,aflw,39.2899685,-76.62196103,University of Maryland,edu,4f36c14d1453fc9d6481b09c5a09e91d8d9ee47a,citation,http://pdfs.semanticscholar.org/4f36/c14d1453fc9d6481b09c5a09e91d8d9ee47a.pdf,Video-Based Face Recognition Using the Intra/Extra-Personal Difference Dictionary,2014
42,AFLW,aflw,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
43,AFLW,aflw,32.87935255,-117.23110049,"University of California, San Diego",edu,a1e07c31184d3728e009d4d1bebe21bf9fe95c8e,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7900056,"On looking at faces in an automobile: Issues, algorithms and evaluation on naturalistic driving dataset",2016
44,AFLW,aflw,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
45,AFLW,aflw,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
46,AFLW,aflw,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,04661729f0ff6afe4b4d6223f18d0da1d479accf,citation,http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.419,From Facial Parts Responses to Face Detection: A Deep Learning Approach,2015
47,AFLW,aflw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,04661729f0ff6afe4b4d6223f18d0da1d479accf,citation,http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.419,From Facial Parts Responses to Face Detection: A Deep Learning Approach,2015
48,AFLW,aflw,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
49,AFLW,aflw,35.77184965,-78.67408695,North Carolina State University,edu,9bd35145c48ce172b80da80130ba310811a44051,citation,https://arxiv.org/pdf/1606.00850.pdf,Face Detection with End-to-End Integration of a ConvNet and a 3D Model,2016
50,AFLW,aflw,39.9922379,116.30393816,Peking University,edu,9bd35145c48ce172b80da80130ba310811a44051,citation,https://arxiv.org/pdf/1606.00850.pdf,Face Detection with End-to-End Integration of a ConvNet and a 3D Model,2016
51,AFLW,aflw,40.0044795,116.370238,Chinese Academy of Sciences,edu,45e616093a92e5f1e61a7c6037d5f637aa8964af,citation,http://www.cs.toronto.edu/~byang/papers/malf_fg15.pdf,Fine-grained evaluation on face detection in the wild,2015
52,AFLW,aflw,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
53,AFLW,aflw,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
54,AFLW,aflw,42.36782045,-71.12666653,Harvard University,edu,3cb057a24a8adba6fe964b5d461ba4e4af68af14,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6701391,Perceptual Annotation: Measuring Human Vision to Improve Computer Vision,2014
55,AFLW,aflw,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
56,AFLW,aflw,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
57,AFLW,aflw,39.329053,-76.619425,Johns Hopkins University,edu,377f2b65e6a9300448bdccf678cde59449ecd337,citation,https://arxiv.org/pdf/1804.10275.pdf,Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results,2018
58,AFLW,aflw,40.47913175,-74.43168868,Rutgers University,edu,377f2b65e6a9300448bdccf678cde59449ecd337,citation,https://arxiv.org/pdf/1804.10275.pdf,Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results,2018
59,AFLW,aflw,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
60,AFLW,aflw,49.10184375,8.4331256,Karlsruhe Institute of Technology,edu,8ee5b1c9fb0bded3578113c738060290403ed472,citation,https://infoscience.epfl.ch/record/200452/files/wacv2014-RGE.pdf,Extending explicit shape regression with mixed feature channels and pose priors,2014
61,AFLW,aflw,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
62,AFLW,aflw,38.88140235,121.52281098,Dalian University of Technology,edu,f074e86e003d5b7a3b6e1780d9c323598d93f3bc,citation,http://pdfs.semanticscholar.org/f074/e86e003d5b7a3b6e1780d9c323598d93f3bc.pdf,Characteristic Number: Theory and Its Application to Shape Analysis,2014
63,AFLW,aflw,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
64,AFLW,aflw,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
65,AFLW,aflw,51.7534538,-1.25400997,University of Oxford,edu,8d9ffe9f7bf1ff3ecc320afe50a92a867a12aeb7,citation,https://arxiv.org/pdf/1809.02169.pdf,Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings,2018
66,AFLW,aflw,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
67,AFLW,aflw,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
68,AFLW,aflw,39.7275037,39.47127034,Firat University,edu,5cfbeae360398de9e20e4165485837bd42b93217,citation,http://pdfs.semanticscholar.org/5cfb/eae360398de9e20e4165485837bd42b93217.pdf,Comparison Of Hog (Histogram of Oriented Gradients) and Haar Cascade Algorithms with a Convolutional Neural Network Based Face Detection Approaches,2017
69,AFLW,aflw,29.5084174,106.57858552,Chongqing University,edu,a065080353d18809b2597246bb0b48316234c29a,citation,http://pdfs.semanticscholar.org/a065/080353d18809b2597246bb0b48316234c29a.pdf,FHEDN: A based on context modeling Feature Hierarchy Encoder-Decoder Network for face detection,2017
70,AFLW,aflw,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
71,AFLW,aflw,53.46600455,-2.23300881,University of Manchester,edu,68c1090f912b69b76437644dd16922909dd40d60,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6987312,Robust and Accurate Shape Model Matching Using Random Forest Regression-Voting,2012
72,AFLW,aflw,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
73,AFLW,aflw,50.0764296,14.41802312,Czech Technical University,edu,f4ba07d2ae6c9673502daf50ee751a5e9262848f,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7284810,Real-time multi-view facial landmark detector learned by the structured output SVM,2015
74,AFLW,aflw,35.6924853,139.7582533,"National Institute of Informatics, Japan",edu,f4ba07d2ae6c9673502daf50ee751a5e9262848f,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7284810,Real-time multi-view facial landmark detector learned by the structured output SVM,2015
75,AFLW,aflw,40.00229045,116.32098908,Tsinghua University,edu,204f1cf56794bb23f9516b5f225a6ae00d3d30b8,citation,https://doi.org/10.1109/JSYST.2015.2418680,An AdaBoost-Based Face Detection System Using Parallel Configurable Architecture With Optimized Computation,2017
76,AFLW,aflw,30.44235995,-84.29747867,Florida State University,edu,1ed6c7e02b4b3ef76f74dd04b2b6050faa6e2177,citation,http://pdfs.semanticscholar.org/6433/c412149382418ccd8aa966aa92973af41671.pdf,Face Detection with a 3D Model,2014
77,AFLW,aflw,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
78,AFLW,aflw,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
79,AFLW,aflw,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
80,AFLW,aflw,43.07982815,-89.43066425,University of Wisconsin Madison,edu,2e091b311ac48c18aaedbb5117e94213f1dbb529,citation,http://pdfs.semanticscholar.org/b1a1/a049f1d78f6e3d072236237c467292ccd537.pdf,Collaborative Facial Landmark Localization for Transferring Annotations Across Datasets,2014
81,AFLW,aflw,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
82,AFLW,aflw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,abdd17e411a7bfe043f280abd4e560a04ab6e992,citation,https://arxiv.org/pdf/1803.00839.pdf,Pose-Robust Face Recognition via Deep Residual Equivariant Mapping,2018
83,AFLW,aflw,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
84,AFLW,aflw,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,fcd3d557863e71dd5ce8bcf918adbe22ec59e62f,citation,http://doi.acm.org/10.1145/2502081.2502148,Facial landmark localization based on hierarchical pose regression with cascaded random ferns,2013
85,AFLW,aflw,40.62984145,22.9588935,Aristotle University of Thessaloniki,edu,c00df53bd46f78ae925c5768d46080159d4ef87d,citation,https://arxiv.org/pdf/1707.08105.pdf,Learning Bag-of-Features Pooling for Deep Convolutional Neural Networks,2017
86,AFLW,aflw,31.4854255,120.2739581,Jiangnan University,edu,d22dd4a6752a5ffa40aebd260ff63d2c2a9e1da1,citation,https://arxiv.org/pdf/1811.05295.pdf,Pose Invariant 3D Face Reconstruction,2018
87,AFLW,aflw,28.59899755,-81.19712501,University of Central Florida,edu,c4fb2de4a5dc28710d9880aece321acf68338fde,citation,https://arxiv.org/pdf/1801.09092.pdf,Interactive Generative Adversarial Networks for Facial Expression Generation in Dyadic Interactions,2018
88,AFLW,aflw,40.0044795,116.370238,Chinese Academy of Sciences,edu,c94b3a05f6f41d015d524169972ae8fd52871b67,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Yan_The_Fastest_Deformable_2014_CVPR_paper.pdf,The Fastest Deformable Part Model for Object Detection,2014
89,AFLW,aflw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,2a171f8d14b6b8735001a11c217af9587d095848,citation,http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.414,Learning Social Relation Traits from Face Images,2015
90,AFLW,aflw,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
91,AFLW,aflw,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
92,AFLW,aflw,47.5612651,7.5752961,University of Basel,edu,0c20fd90d867fe1be2459223a3cb1a69fa3d44bf,citation,http://pdfs.semanticscholar.org/0c20/fd90d867fe1be2459223a3cb1a69fa3d44bf.pdf,A Monte Carlo Strategy to Integrate Detection and Model-Based Face Analysis,2013
93,AFLW,aflw,39.9041999,116.4073963,"Beijing FaceAll Co., Beijing, China",edu,c7cd490e43ee4ff81e8f86f790063695369c2830,citation,https://doi.org/10.1109/VCIP.2016.7805472,Use fast R-CNN and cascade structure for face detection,2016
94,AFLW,aflw,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,c7cd490e43ee4ff81e8f86f790063695369c2830,citation,https://doi.org/10.1109/VCIP.2016.7805472,Use fast R-CNN and cascade structure for face detection,2016
95,AFLW,aflw,47.05821,15.46019568,Graz University of Technology,edu,96a9ca7a8366ae0efe6b58a515d15b44776faf6e,citation,https://arxiv.org/pdf/1609.00129.pdf,Grid Loss: Detecting Occluded Faces,2016
96,AFLW,aflw,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,3b73f8a2b39751efb7d7b396bf825af2aaadee24,citation,https://arxiv.org/pdf/1712.01066.pdf,Connecting Pixels to Privacy and Utility: Automatic Redaction of Private Information in Images,2017
97,AFLW,aflw,47.5612651,7.5752961,University of Basel,edu,043efe5f465704ced8d71a067d2b9d5aa5b59c29,citation,https://pdfs.semanticscholar.org/000a/c6b0865c79bcf0d6f7f069b3abfe229e1462.pdf,Occlusion-aware 3D Morphable Face Models,2016
98,AFLW,aflw,40.62984145,22.9588935,Aristotle University of Thessaloniki,edu,ede5982980aa76deae8f9dc5143a724299d67742,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8081396,Lightweight two-stream convolutional face detection,2017
99,AFLW,aflw,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
100,AFLW,aflw,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
101,AFLW,aflw,51.24303255,-0.59001382,University of Surrey,edu,ed07856461da6c7afa4f1782b5b607b45eebe9f6,citation,https://pdfs.semanticscholar.org/ed07/856461da6c7afa4f1782b5b607b45eebe9f6.pdf,D Morphable Models as Spatial Transformer Networks,2017
102,AFLW,aflw,53.94540365,-1.03138878,University of York,edu,ed07856461da6c7afa4f1782b5b607b45eebe9f6,citation,https://pdfs.semanticscholar.org/ed07/856461da6c7afa4f1782b5b607b45eebe9f6.pdf,D Morphable Models as Spatial Transformer Networks,2017
103,AFLW,aflw,37.4173931,-121.9475721,"ARM, Inc.",company,0974677f59e78649a40f0a1d85735410d21b906a,citation,https://doi.org/10.1109/ASPDAC.2017.7858282,A real-time 17-scale object detection accelerator with adaptive 2000-stage classification in 65nm CMOS,2017
104,AFLW,aflw,30.19331415,120.11930822,Zhejiang University,edu,0974677f59e78649a40f0a1d85735410d21b906a,citation,https://doi.org/10.1109/ASPDAC.2017.7858282,A real-time 17-scale object detection accelerator with adaptive 2000-stage classification in 65nm CMOS,2017
105,AFLW,aflw,33.30715065,-111.67653157,Arizona State University,edu,0974677f59e78649a40f0a1d85735410d21b906a,citation,https://doi.org/10.1109/ASPDAC.2017.7858282,A real-time 17-scale object detection accelerator with adaptive 2000-stage classification in 65nm CMOS,2017
106,AFLW,aflw,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
107,AFLW,aflw,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
108,AFLW,aflw,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
109,AFLW,aflw,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
110,AFLW,aflw,-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
111,AFLW,aflw,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
112,AFLW,aflw,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
113,AFLW,aflw,47.5612651,7.5752961,University of Basel,edu,5789f8420d8f15e7772580ec373112f864627c4b,citation,http://doi.ieeecomputersociety.org/10.1109/ICCV.2017.417,Efficient Global Illumination for Morphable Models,2017
114,AFLW,aflw,51.4293086,-0.2684044,Kingston University,edu,01125e3c68edb420b8d884ff53fb38d9fbe4f2b8,citation,http://openaccess.thecvf.com/content_ICCV_2017/papers/Jackson_Large_Pose_3D_ICCV_2017_paper.pdf,Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression,2017
115,AFLW,aflw,52.9387428,-1.20029569,University of Nottingham,edu,01125e3c68edb420b8d884ff53fb38d9fbe4f2b8,citation,http://openaccess.thecvf.com/content_ICCV_2017/papers/Jackson_Large_Pose_3D_ICCV_2017_paper.pdf,Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression,2017
116,AFLW,aflw,39.9808333,116.34101249,Beihang University,edu,86b6afc667bb14ff4d69e7a5e8bb2454a6bbd2cd,citation,https://pdfs.semanticscholar.org/86b6/afc667bb14ff4d69e7a5e8bb2454a6bbd2cd.pdf,Attentional Alignment Networks,2018
117,AFLW,aflw,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,86b6afc667bb14ff4d69e7a5e8bb2454a6bbd2cd,citation,https://pdfs.semanticscholar.org/86b6/afc667bb14ff4d69e7a5e8bb2454a6bbd2cd.pdf,Attentional Alignment Networks,2018
118,AFLW,aflw,32.7283683,-97.11201835,University of Texas at Arlington,edu,86b6afc667bb14ff4d69e7a5e8bb2454a6bbd2cd,citation,https://pdfs.semanticscholar.org/86b6/afc667bb14ff4d69e7a5e8bb2454a6bbd2cd.pdf,Attentional Alignment Networks,2018
119,AFLW,aflw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,52d7eb0fbc3522434c13cc247549f74bb9609c5d,citation,https://arxiv.org/pdf/1511.06523.pdf,WIDER FACE: A Face Detection Benchmark,2016
120,AFLW,aflw,32.0565957,118.77408833,Nanjing University,edu,b8978a5251b6e341a1171e4fd9177aec1432dd3a,citation,https://doi.org/10.1016/j.image.2016.04.004,FaceHunter: A multi-task convolutional neural network based face detector,2016
121,AFLW,aflw,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
122,AFLW,aflw,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
123,AFLW,aflw,51.6091578,-3.97934429,Swansea University,edu,d115c4a66d765fef596b0b171febca334cea15b5,citation,http://pdfs.semanticscholar.org/d115/c4a66d765fef596b0b171febca334cea15b5.pdf,Combining Stacked Denoising Autoencoders and Random Forests for Face Detection,2016
124,AFLW,aflw,38.88140235,121.52281098,Dalian University of Technology,edu,19705579b8e7d955092ef54a22f95f557a455338,citation,https://doi.org/10.1109/ICIP.2014.7025277,Fiducial facial point extraction with cross ratio,2014
125,AFLW,aflw,51.7534538,-1.25400997,University of Oxford,edu,79eb06c8acce1feef4a8654287d9cf5081e19600,citation,https://arxiv.org/pdf/1808.06882.pdf,Self-supervised learning of a facial attribute embedding from video,2018
126,AFLW,aflw,37.4102193,-122.05965487,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
127,AFLW,aflw,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
128,AFLW,aflw,40.0044795,116.370238,Chinese Academy of Sciences,edu,7fcfd72ba6bc14bbb90b31fe14c2c77a8b220ab2,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2017.255,Robust FEC-CNN: A High Accuracy Facial Landmark Detection System,2017
129,AFLW,aflw,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,7fcfd72ba6bc14bbb90b31fe14c2c77a8b220ab2,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2017.255,Robust FEC-CNN: A High Accuracy Facial Landmark Detection System,2017
130,AFLW,aflw,40.00229045,116.32098908,Tsinghua University,edu,3fb26f3abcf0d287243646426cd5ddeee33624d4,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.376,Joint Training of Cascaded CNN for Face Detection,2016
131,AFLW,aflw,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
132,AFLW,aflw,31.9078499,34.81334092,Weizmann Institute of Science,edu,d4c2d26523f577e2d72fc80109e2540c887255c8,citation,http://pdfs.semanticscholar.org/d4c2/d26523f577e2d72fc80109e2540c887255c8.pdf,Face-space Action Recognition by Face-Object Interactions,2016
133,AFLW,aflw,40.62984145,22.9588935,Aristotle University of Thessaloniki,edu,3251f40ed1113d592c61d2017e67beca66e678bb,citation,https://doi.org/10.1007/978-3-319-65172-9_17,Improving Face Pose Estimation Using Long-Term Temporal Averaging for Stochastic Optimization,2017
134,AFLW,aflw,56.46255985,84.95565495,Tomsk Polytechnic University,edu,17ded725602b4329b1c494bfa41527482bf83a6f,citation,http://pdfs.semanticscholar.org/cb10/434a5d68ffbe9ed0498771192564ecae8894.pdf,Compact Convolutional Neural Network Cascade for Face Detection,2015
135,AFLW,aflw,40.47913175,-74.43168868,Rutgers University,edu,c8ca6a2dc41516c16ea0747e9b3b7b1db788dbdd,citation,https://arxiv.org/pdf/1609.02825.pdf,Track Facial Points in Unconstrained Videos,2016
136,AFLW,aflw,30.44235995,-84.29747867,Florida State University,edu,42ea8a96eea023361721f0ea34264d3d0fc49ebd,citation,https://arxiv.org/pdf/1608.04695.pdf,Parameterized Principal Component Analysis,2018
137,AFLW,aflw,-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
138,AFLW,aflw,42.0551164,-87.67581113,Northwestern University,edu,7c953868cd51f596300c8231192d57c9c514ae17,citation,http://courses.cs.washington.edu/courses/cse590v/13au/CVPR13_FaceDetection.pdf,Detecting and Aligning Faces by Image Retrieval,2013
139,AFLW,aflw,40.0044795,116.370238,Chinese Academy of Sciences,edu,19a9f658ea14701502d169dc086651b1d9b2a8ea,citation,http://www.cbsr.ia.ac.cn/users/zlei/papers/JJYan-FG2013.pdf,Structural models for face detection,2013
140,AFLW,aflw,-27.47715625,153.02841004,Queensland University of Technology,edu,be632b206f1cd38eab0c01c5f2004d1e8fc72880,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6607601,Gradual training of cascaded shape regression for facial landmark localization and pose estimation,2013
141,AFLW,aflw,33.6431901,-117.84016494,"University of California, Irvine",edu,0e986f51fe45b00633de9fd0c94d082d2be51406,citation,http://vision.ics.uci.edu/papers/ZhuR_CVPR_2012/ZhuR_CVPR_2012.pdf,"Face detection, pose estimation, and landmark localization in the wild",2012
142,AFLW,aflw,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
143,AFLW,aflw,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
144,AFLW,aflw,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
145,AFLW,aflw,41.21002475,-73.80407056,IBM Thomas J. Watson Research Center,company,eb87151fd2796ff5b4bbcf1906d41d53ac6c5595,citation,https://doi.org/10.1109/ICPR.2016.7899719,Enhanced face detection using body part detections for wearable cameras,2016
146,AFLW,aflw,29.5357046,106.60482474,Chongqing University of Posts and Telecommunications,edu,35d272877b178aa97c678e3fcbb619ff512af4c2,citation,https://doi.org/10.1109/SMC.2017.8122743,A multi-scale fusion convolutional neural network for face detection,2017
147,AFLW,aflw,52.7663577,-1.2292461,Loughborough University,edu,9e8f95503bebdfb623d4e5b51347f72677d89d99,citation,https://pdfs.semanticscholar.org/9e8f/95503bebdfb623d4e5b51347f72677d89d99.pdf,Multi-dimensional local binary pattern texture descriptors and their application for medical image analysis,2014
148,AFLW,aflw,40.0044795,116.370238,Chinese Academy of Sciences,edu,492f41e800c52614c5519f830e72561db205e86c,citation,http://openaccess.thecvf.com/content_cvpr_2017/papers/Lv_A_Deep_Regression_CVPR_2017_paper.pdf,A Deep Regression Architecture with Two-Stage Re-initialization for High Performance Facial Landmark Detection,2017
149,AFLW,aflw,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,492f41e800c52614c5519f830e72561db205e86c,citation,http://openaccess.thecvf.com/content_cvpr_2017/papers/Lv_A_Deep_Regression_CVPR_2017_paper.pdf,A Deep Regression Architecture with Two-Stage Re-initialization for High Performance Facial Landmark Detection,2017
150,AFLW,aflw,42.718568,-84.47791571,Michigan State University,edu,c6382de52636705be5898017f2f8ed7c70d7ae96,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7139089,Unconstrained face detection: State of the art baseline and challenges,2015
151,AFLW,aflw,38.95187,-77.363259,"Noblis, Falls Church, VA, U.S.A.",company,c6382de52636705be5898017f2f8ed7c70d7ae96,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7139089,Unconstrained face detection: State of the art baseline and challenges,2015
152,AFLW,aflw,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
153,AFLW,aflw,22.53521465,113.9315911,Shenzhen University,edu,66dcd855a6772d2731b45cfdd75f084327b055c2,citation,http://pdfs.semanticscholar.org/66dc/d855a6772d2731b45cfdd75f084327b055c2.pdf,Quality Classified Image Analysis with Application to Face Detection and Recognition,2018
154,AFLW,aflw,38.5336349,-121.79077264,"University of California, Davis",edu,fdf8e293a7618f560e76bd83e3c40a0788104547,citation,https://arxiv.org/pdf/1704.04023.pdf,Interspecies Knowledge Transfer for Facial Keypoint Detection,2017
155,AFLW,aflw,30.19331415,120.11930822,Zhejiang University,edu,fdf8e293a7618f560e76bd83e3c40a0788104547,citation,https://arxiv.org/pdf/1704.04023.pdf,Interspecies Knowledge Transfer for Facial Keypoint Detection,2017
156,AFLW,aflw,51.49887085,-0.17560797,Imperial College London,edu,38cbb500823057613494bacd0078aa0e57b30af8,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2017.252,Deep Face Deblurring,2017
157,AFLW,aflw,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
158,AFLW,aflw,52.2380139,6.8566761,University of Twente,edu,71b07c537a9e188b850192131bfe31ef206a39a0,citation,http://pdfs.semanticscholar.org/71b0/7c537a9e188b850192131bfe31ef206a39a0.pdf,300 Faces In-The-Wild Challenge: database and results,2016
159,AFLW,aflw,35.6924853,139.7582533,"National Institute of Informatics, Japan",edu,4dd71a097e6b3cd379d8c802460667ee0cbc8463,citation,http://www.dgcv.nii.ac.jp/Publications/Papers/2015/BWILD2015.pdf,Real-time multi-view facial landmark detector learned by the structured output SVM,2015
160,AFLW,aflw,33.856111,-5.574391,Moulay Ismail University,edu,1fd7a17a6c630a122c1a3d1c0668d14c0c375de0,citation,https://doi.org/10.1109/CIST.2016.7805097,"Facial landmark localization: Past, present and future",2016
161,AFLW,aflw,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
162,AFLW,aflw,37.4102193,-122.05965487,Carnegie Mellon University,edu,6dbdb07ce2991db0f64c785ad31196dfd4dae721,citation,https://arxiv.org/pdf/1802.09058.pdf,Seeing Small Faces from Robust Anchor's Perspective,2018
163,AFLW,aflw,30.04287695,31.23664139,American University in Cairo,edu,1a12eec3ceb1c81cde4ae6e8f27aac08b36317d4,citation,https://arxiv.org/pdf/1706.09498.pdf,Real-time Distracted Driver Posture Classification,2017
164,AFLW,aflw,51.6091578,-3.97934429,Swansea University,edu,cc70fb1ab585378c79a2ab94776723e597afe379,citation,https://doi.org/10.1109/ICIP.2017.8297067,Detect face in the wild using CNN cascade with feature aggregation at multi-resolution,2017
165,AFLW,aflw,51.49887085,-0.17560797,Imperial College London,edu,59d8fa6fd91cdb72cd0fa74c04016d79ef5a752b,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Zafeiriou_The_Menpo_Facial_CVPR_2017_paper.pdf,The Menpo Facial Landmark Localisation Challenge: A Step Towards the Solution,2017
166,AFLW,aflw,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,2f61d91033a06dd904ff9d1765d57e5b4d7f57a6,citation,https://doi.org/10.1109/ICIP.2016.7532953,FCFD: Teach the machine to accomplish face detection step by step,2016
167,AFLW,aflw,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
168,AFLW,aflw,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
169,AFLW,aflw,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
170,AFLW,aflw,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
171,AFLW,aflw,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
172,AFLW,aflw,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
173,AFLW,aflw,40.0044795,116.370238,Chinese Academy of Sciences,edu,4c6233765b5f83333f6c675d3389bbbf503805e3,citation,https://perceptual.mpi-inf.mpg.de/files/2015/03/Yan_Vis13.pdf,Real-time high performance deformable model for face detection in the wild,2013
174,AFLW,aflw,40.51865195,-74.44099801,State University of New Jersey,edu,02820c1491b10a1ff486fed32c269e4077c36551,citation,https://arxiv.org/pdf/1610.07930v1.pdf,Active user authentication for smartphones: A challenge data set and benchmark results,2016
175,AFLW,aflw,39.2899685,-76.62196103,University of Maryland,edu,02820c1491b10a1ff486fed32c269e4077c36551,citation,https://arxiv.org/pdf/1610.07930v1.pdf,Active user authentication for smartphones: A challenge data set and benchmark results,2016
176,AFLW,aflw,33.776033,-84.39884086,Georgia Institute of Technology,edu,e659221538d256b2c3e0724deff749eda903fc7d,citation,https://arxiv.org/pdf/1710.00925.pdf,Fine-Grained Head Pose Estimation Without Keypoints,2017
177,AFLW,aflw,49.20172,16.6033168,Brno University of Technology,edu,b55e70df03d9b80c91446a97957bc95772dcc45b,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8269329,MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis,2018
178,AFLW,aflw,48.5670466,13.4517835,University of Passau,edu,b55e70df03d9b80c91446a97957bc95772dcc45b,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8269329,MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis,2018
179,AFLW,aflw,50.7171497,7.12825184,"Deutsche Welle, Bonn, Germany",edu,b55e70df03d9b80c91446a97957bc95772dcc45b,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8269329,MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis,2018
180,AFLW,aflw,44.6531692,10.8586228,"Expert Systems, Modena, Italy",company,b55e70df03d9b80c91446a97957bc95772dcc45b,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8269329,MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis,2018
181,AFLW,aflw,53.27639715,-9.05829961,National University of Ireland Galway,edu,b55e70df03d9b80c91446a97957bc95772dcc45b,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8269329,MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis,2018
182,AFLW,aflw,40.4402995,-3.7870076,"Paradigma Digital, Madrid, Spain",company,b55e70df03d9b80c91446a97957bc95772dcc45b,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8269329,MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis,2018
183,AFLW,aflw,53.3498053,-6.2603097,"Siren Solutions, Dublin, Ireland",company,b55e70df03d9b80c91446a97957bc95772dcc45b,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8269329,MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis,2018
184,AFLW,aflw,39.86742125,32.73519072,Hacettepe University,edu,9865fe20df8fe11717d92b5ea63469f59cf1635a,citation,https://arxiv.org/pdf/1805.07566.pdf,Wildest Faces: Face Detection and Recognition in Violent Settings,2018
185,AFLW,aflw,39.87549675,32.78553506,Middle East Technical University,edu,9865fe20df8fe11717d92b5ea63469f59cf1635a,citation,https://arxiv.org/pdf/1805.07566.pdf,Wildest Faces: Face Detection and Recognition in Violent Settings,2018
186,AFLW,aflw,47.3764534,8.54770931,ETH Zürich,edu,961a5d5750f18e91e28a767b3cb234a77aac8305,citation,http://pdfs.semanticscholar.org/961a/5d5750f18e91e28a767b3cb234a77aac8305.pdf,Face Detection without Bells and Whistles,2014
187,AFLW,aflw,40.51865195,-74.44099801,State University of New Jersey,edu,0d746111135c2e7f91443869003d05cde3044beb,citation,https://doi.org/10.1109/ICIP.2016.7532908,Partial face detection for continuous authentication,2016
188,AFLW,aflw,39.2899685,-76.62196103,University of Maryland,edu,0d746111135c2e7f91443869003d05cde3044beb,citation,https://doi.org/10.1109/ICIP.2016.7532908,Partial face detection for continuous authentication,2016
189,AFLW,aflw,34.0224149,-118.28634407,University of Southern California,edu,eb6ee56e085ebf473da990d032a4249437a3e462,citation,http://www-scf.usc.edu/~chuntinh/doc/Age_Gender_Classification_APSIPA_2017.pdf,Age/gender classification with whole-component convolutional neural networks (WC-CNN),2017
190,AFLW,aflw,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
191,AFLW,aflw,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
192,AFLW,aflw,36.3697191,127.362537,Korea Advanced Institute of Science and Technology,edu,ca8f23d9b9a40016eaf0467a3df46720ac718e1d,citation,https://doi.org/10.1109/ICASSP.2015.7178214,Face detection using Local Hybrid Patterns,2015
193,AFLW,aflw,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
194,AFLW,aflw,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
195,AFLW,aflw,-33.8809651,151.20107299,University of Technology Sydney,edu,bbf28f39e5038813afd74cf1bc78d55fcbe630f1,citation,https://arxiv.org/pdf/1803.04108.pdf,Style Aggregated Network for Facial Landmark Detection,2018
196,AFLW,aflw,-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
197,AFLW,aflw,42.7298459,-73.67950216,Rensselaer Polytechnic Institute,edu,1b794b944fd462a2742b6c2f8021fecc663004c9,citation,http://arxiv.org/abs/1709.05732,A Hierarchical Probabilistic Model for Facial Feature Detection,2014
198,AFLW,aflw,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
199,AFLW,aflw,50.3755269,-4.13937687,Plymouth University,edu,239958d6778643101ab631ec354ea1bc4d33e7e0,citation,http://doi.org/10.1016/j.patcog.2017.06.009,Head pose estimation in the wild using Convolutional Neural Networks and adaptive gradient methods,2017
200,AFLW,aflw,39.2899685,-76.62196103,University of Maryland,edu,40c8cffd5aac68f59324733416b6b2959cb668fd,citation,http://arxiv.org/abs/1701.08341,Pooling Facial Segments to Face: The Shallow and Deep Ends,2017
201,AFLW,aflw,-27.49741805,153.01316956,University of Queensland,edu,28646c6220848db46c6944967298d89a6559c700,citation,https://pdfs.semanticscholar.org/2864/6c6220848db46c6944967298d89a6559c700.pdf,It takes two to tango : Cascading off-the-shelf face detectors,2018
202,AFLW,aflw,37.4102193,-122.05965487,Carnegie Mellon University,edu,48a9241edda07252c1aadca09875fabcfee32871,citation,https://arxiv.org/pdf/1611.08657v5.pdf,Convolutional Experts Constrained Local Model for Facial Landmark Detection,2017
203,AFLW,aflw,52.2380139,6.8566761,University of Twente,edu,044d9a8c61383312cdafbcc44b9d00d650b21c70,citation,https://ibug.doc.ic.ac.uk/media/uploads/documents/sagonas_iccv_2013_300_w.pdf,300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge,2013
204,AFLW,aflw,51.49887085,-0.17560797,Imperial College London,edu,044d9a8c61383312cdafbcc44b9d00d650b21c70,citation,https://ibug.doc.ic.ac.uk/media/uploads/documents/sagonas_iccv_2013_300_w.pdf,300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge,2013
205,AFLW,aflw,53.22853665,-0.54873472,University of Lincoln,edu,044d9a8c61383312cdafbcc44b9d00d650b21c70,citation,https://ibug.doc.ic.ac.uk/media/uploads/documents/sagonas_iccv_2013_300_w.pdf,300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge,2013
206,AFLW,aflw,52.9387428,-1.20029569,University of Nottingham,edu,4cd0da974af9356027a31b8485a34a24b57b8b90,citation,https://arxiv.org/pdf/1703.00862v2.pdf,Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment with Limited Resources,2017
207,AFLW,aflw,41.70456775,-86.23822026,University of Notre Dame,edu,17479e015a2dcf15d40190e06419a135b66da4e0,citation,https://arxiv.org/pdf/1610.08119.pdf,Predicting First Impressions With Deep Learning,2017
208,AFLW,aflw,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
209,AFLW,aflw,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
210,AFLW,aflw,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