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id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year
0,,MegaFace,megaface,0.0,0.0,,,,main,,Level Playing Field for Million Scale Face Recognition,2017
1,Netherlands,MegaFace,megaface,53.21967825,6.56251482,University of Groningen,edu,8efda5708bbcf658d4f567e3866e3549fe045bbb,citation,https://pdfs.semanticscholar.org/8efd/a5708bbcf658d4f567e3866e3549fe045bbb.pdf,Pre-trained Deep Convolutional Neural Networks for Face Recognition,2018
2,United States,MegaFace,megaface,41.70456775,-86.23822026,University of Notre Dame,edu,e64c166dc5bb33bc61462a8b5ac92edb24d905a1,citation,https://arxiv.org/pdf/1811.01474.pdf,Fast Face Image Synthesis with Minimal Training.,2018
3,China,MegaFace,megaface,22.4162632,114.2109318,Chinese University of Hong Kong,edu,2401cd5606c6bc5390acc352d00c1685f0c8af60,citation,https://arxiv.org/pdf/1809.01407.pdf,Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition,2018
4,China,MegaFace,megaface,39.993008,116.329882,SenseTime,company,2401cd5606c6bc5390acc352d00c1685f0c8af60,citation,https://arxiv.org/pdf/1809.01407.pdf,Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition,2018
5,Singapore,MegaFace,megaface,1.3484104,103.68297965,Nanyang Technological University,edu,2401cd5606c6bc5390acc352d00c1685f0c8af60,citation,https://arxiv.org/pdf/1809.01407.pdf,Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition,2018
6,United Kingdom,MegaFace,megaface,51.49887085,-0.17560797,Imperial College London,edu,40bb090a4e303f11168dce33ed992f51afe02ff7,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Deng_Marginal_Loss_for_CVPR_2017_paper.pdf,Marginal Loss for Deep Face Recognition,2017
7,China,MegaFace,megaface,39.94976005,116.33629046,Beijing Jiaotong University,edu,d7cbedbee06293e78661335c7dd9059c70143a28,citation,https://arxiv.org/pdf/1804.07573.pdf,MobileFaceNets: Efficient CNNs for Accurate Real-time Face Verification on Mobile Devices,2018
8,China,MegaFace,megaface,40.0044795,116.370238,Chinese Academy of Sciences,edu,1345fb7700389f9d02f203b3cb25ac3594855054,citation,,Hierarchical Training for Large Scale Face Recognition with Few Samples Per Subject,2018
9,United States,MegaFace,megaface,45.57022705,-122.63709346,Concordia University,edu,db374308655256da1479c272582d7c7139c97173,citation,https://arxiv.org/pdf/1811.11080.pdf,MobiFace: A Lightweight Deep Learning Face Recognition on Mobile Devices,2018
10,United States,MegaFace,megaface,33.5866784,-101.87539204,Electrical and Computer Engineering,edu,db374308655256da1479c272582d7c7139c97173,citation,https://arxiv.org/pdf/1811.11080.pdf,MobiFace: A Lightweight Deep Learning Face Recognition on Mobile Devices,2018
11,United States,MegaFace,megaface,36.0678324,-94.1736551,University of Arkansas,edu,db374308655256da1479c272582d7c7139c97173,citation,https://arxiv.org/pdf/1811.11080.pdf,MobiFace: A Lightweight Deep Learning Face Recognition on Mobile Devices,2018
12,United Kingdom,MegaFace,megaface,51.49887085,-0.17560797,Imperial College London,edu,51992fa881541ca3a4520c1ff9100b83e2f1ad87,citation,https://arxiv.org/pdf/1801.07698.pdf,ArcFace: Additive Angular Margin Loss for Deep Face Recognition,2018
13,China,MegaFace,megaface,40.0044795,116.370238,Chinese Academy of Sciences,edu,94f74c6314ffd02db581e8e887b5fd81ce288dbf,citation,https://arxiv.org/pdf/1511.02683.pdf,A Light CNN for Deep Face Representation With Noisy Labels,2018
14,China,MegaFace,megaface,22.4162632,114.2109318,Chinese University of Hong Kong,edu,53840c83f7b6ae78d4310c5b84ab3fde1a33bc4f,citation,https://arxiv.org/pdf/1801.01687.pdf,Accelerated Training for Massive Classification via Dynamic Class Selection,2018
15,China,MegaFace,megaface,39.993008,116.329882,SenseTime,company,53840c83f7b6ae78d4310c5b84ab3fde1a33bc4f,citation,https://arxiv.org/pdf/1801.01687.pdf,Accelerated Training for Massive Classification via Dynamic Class Selection,2018
16,United States,MegaFace,megaface,38.99203005,-76.9461029,University of Maryland College Park,edu,7323b594d3a8508f809e276aa2d224c4e7ec5a80,citation,https://arxiv.org/pdf/1808.05508.pdf,An Experimental Evaluation of Covariates Effects on Unconstrained Face Verification,2018
17,China,MegaFace,megaface,22.304572,114.17976285,Hong Kong Polytechnic University,edu,f60070d3a4d333aa1436e4c372b1feb5b316a7ba,citation,https://arxiv.org/pdf/1801.05678.pdf,Face Recognition via Centralized Coordinate Learning,2018
18,United Kingdom,MegaFace,megaface,54.687254,-5.882736,Ulster University,edu,ddfde808af8dc8b737d115869d6cca780d050884,citation,https://arxiv.org/pdf/1805.06741.pdf,Minimum Margin Loss for Deep Face Recognition,2018
19,China,MegaFace,megaface,39.9922379,116.30393816,Peking University,edu,4f0b641860d90dfa4c185670bf636149a2b2b717,citation,,Improve Cross-Domain Face Recognition with IBN-block,2018
20,United States,MegaFace,megaface,40.4441619,-79.94272826,Carnegie Mellon University,edu,67a9659de0bf671fafccd7f39b7587f85fb6dfbd,citation,,Ring Loss: Convex Feature Normalization for Face Recognition,2018
21,United States,MegaFace,megaface,41.70456775,-86.23822026,University of Notre Dame,edu,841855205818d3a6d6f85ec17a22515f4f062882,citation,https://arxiv.org/pdf/1805.11529.pdf,Low Resolution Face Recognition in the Wild,2018
22,United Kingdom,MegaFace,megaface,51.5247272,-0.03931035,Queen Mary University of London,edu,2306b2a8fba28539306052764a77a0d0f5d1236a,citation,https://arxiv.org/pdf/1804.09691.pdf,Surveillance Face Recognition Challenge,2018
23,United Kingdom,MegaFace,megaface,55.378051,-3.435973,"Vision Semantics Ltd, UK",edu,2306b2a8fba28539306052764a77a0d0f5d1236a,citation,https://arxiv.org/pdf/1804.09691.pdf,Surveillance Face Recognition Challenge,2018
24,United States,MegaFace,megaface,42.366183,-71.092455,Mitsubishi Electric Research Laboratories,company,57246142814d7010d3592e3a39a1ed819dd01f3b,citation,https://pdfs.semanticscholar.org/5724/6142814d7010d3592e3a39a1ed819dd01f3b.pdf,Verification of Very Low-Resolution Faces Using An Identity-Preserving Deep Face Super-resolution Network,0
25,China,MegaFace,megaface,22.4162632,114.2109318,Chinese University of Hong Kong,edu,f3a59d85b7458394e3c043d8277aa1ffe3cdac91,citation,https://arxiv.org/pdf/1802.09900.pdf,Query-Free Attacks on Industry-Grade Face Recognition Systems under Resource Constraints,2018
26,United States,MegaFace,megaface,39.86948105,-84.87956905,Indiana University,edu,f3a59d85b7458394e3c043d8277aa1ffe3cdac91,citation,https://arxiv.org/pdf/1802.09900.pdf,Query-Free Attacks on Industry-Grade Face Recognition Systems under Resource Constraints,2018
27,Singapore,MegaFace,megaface,1.3484104,103.68297965,Nanyang Technological University,edu,9e31e77f9543ab42474ba4e9330676e18c242e72,citation,https://arxiv.org/pdf/1807.11649.pdf,The Devil of Face Recognition is in the Noise,2018
28,China,MegaFace,megaface,39.993008,116.329882,SenseTime,company,9e31e77f9543ab42474ba4e9330676e18c242e72,citation,https://arxiv.org/pdf/1807.11649.pdf,The Devil of Face Recognition is in the Noise,2018
29,United States,MegaFace,megaface,32.87935255,-117.23110049,"University of California, San Diego",edu,9e31e77f9543ab42474ba4e9330676e18c242e72,citation,https://arxiv.org/pdf/1807.11649.pdf,The Devil of Face Recognition is in the Noise,2018
30,United States,MegaFace,megaface,22.5447154,113.9357164,Tencent,company,7a7fddb3020e0c2dd4e3fe275329eb10f1cfbb8a,citation,https://arxiv.org/pdf/1810.07599.pdf,Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition,2018
31,United States,MegaFace,megaface,47.6423318,-122.1369302,Microsoft,company,6cacda04a541d251e8221d70ac61fda88fb61a70,citation,https://arxiv.org/pdf/1707.05574.pdf,One-shot Face Recognition by Promoting Underrepresented Classes,2017
32,Czech Republic,MegaFace,megaface,49.20172,16.6033168,Brno University of Technology,edu,b55e70df03d9b80c91446a97957bc95772dcc45b,citation,,MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis,2018
33,Germany,MegaFace,megaface,48.5670466,13.4517835,University of Passau,edu,b55e70df03d9b80c91446a97957bc95772dcc45b,citation,,MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis,2018
34,Germany,MegaFace,megaface,50.7171497,7.12825184,"Deutsche Welle, Bonn, Germany",edu,b55e70df03d9b80c91446a97957bc95772dcc45b,citation,,MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis,2018
35,Italy,MegaFace,megaface,44.6531692,10.8586228,"Expert Systems, Modena, Italy",company,b55e70df03d9b80c91446a97957bc95772dcc45b,citation,,MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis,2018
36,Spain,MegaFace,megaface,40.4486372,-3.7192798,"GSI Universidad Politécnica de Madrid, Madrid, Spain",edu,b55e70df03d9b80c91446a97957bc95772dcc45b,citation,,MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis,2018
37,Ireland,MegaFace,megaface,53.27639715,-9.05829961,National University of Ireland Galway,edu,b55e70df03d9b80c91446a97957bc95772dcc45b,citation,,MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis,2018
38,Spain,MegaFace,megaface,40.4402995,-3.7870076,"Paradigma Digital, Madrid, Spain",company,b55e70df03d9b80c91446a97957bc95772dcc45b,citation,,MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis,2018
39,Czech Republic,MegaFace,megaface,49.2238302,16.5982602,"Phonexia, Brno-Krlovo Pole, Czech Republic",edu,b55e70df03d9b80c91446a97957bc95772dcc45b,citation,,MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis,2018
40,Ireland,MegaFace,megaface,53.3498053,-6.2603097,"Siren Solutions, Dublin, Ireland",company,b55e70df03d9b80c91446a97957bc95772dcc45b,citation,,MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis,2018
41,China,MegaFace,megaface,22.5447154,113.9357164,"Tencent AI Lab, Shenzhen, China",company,1174b869c325222c3446d616975842e8d2989cf2,citation,https://arxiv.org/pdf/1801.09414.pdf,CosFace: Large Margin Cosine Loss for Deep Face Recognition,2018
42,United States,MegaFace,megaface,33.776033,-84.39884086,Georgia Institute of Technology,edu,bd8f77b7d3b9d272f7a68defc1412f73e5ac3135,citation,https://arxiv.org/pdf/1704.08063.pdf,SphereFace: Deep Hypersphere Embedding for Face Recognition,2017
43,United States,MegaFace,megaface,40.4441619,-79.94272826,Carnegie Mellon University,edu,bd8f77b7d3b9d272f7a68defc1412f73e5ac3135,citation,https://arxiv.org/pdf/1704.08063.pdf,SphereFace: Deep Hypersphere Embedding for Face Recognition,2017
44,China,MegaFace,megaface,23.09461185,113.28788994,Sun Yat-Sen University,edu,bd8f77b7d3b9d272f7a68defc1412f73e5ac3135,citation,https://arxiv.org/pdf/1704.08063.pdf,SphereFace: Deep Hypersphere Embedding for Face Recognition,2017
45,China,MegaFace,megaface,30.672721,104.098806,University of Electronic Science and Technology of China,edu,93af36da08bf99e68c9b0d36e141ed8154455ac2,citation,https://pdfs.semanticscholar.org/93af/36da08bf99e68c9b0d36e141ed8154455ac2.pdf,A Dditive M Argin S Oftmax for F Ace V Erification,2018
46,United States,MegaFace,megaface,33.776033,-84.39884086,Georgia Institute of Technology,edu,93af36da08bf99e68c9b0d36e141ed8154455ac2,citation,https://pdfs.semanticscholar.org/93af/36da08bf99e68c9b0d36e141ed8154455ac2.pdf,A Dditive M Argin S Oftmax for F Ace V Erification,2018
47,United States,MegaFace,megaface,45.57022705,-122.63709346,Concordia University,edu,eb8519cec0d7a781923f68fdca0891713cb81163,citation,https://arxiv.org/pdf/1703.08617.pdf,Temporal Non-volume Preserving Approach to Facial Age-Progression and Age-Invariant Face Recognition,2017
48,United States,MegaFace,megaface,40.4441619,-79.94272826,Carnegie Mellon University,edu,eb8519cec0d7a781923f68fdca0891713cb81163,citation,https://arxiv.org/pdf/1703.08617.pdf,Temporal Non-volume Preserving Approach to Facial Age-Progression and Age-Invariant Face Recognition,2017
49,Portugal,MegaFace,megaface,40.277859,-7.508983,University of Beira Interior,edu,e11bc0f7c73c04d38b7fb80bd1ca886495a4d43c,citation,http://www.di.ubi.pt/~hugomcp/doc/Leopard_TIFS.pdf,“A Leopard Cannot Change Its Spots”: Improving Face Recognition Using 3D-Based Caricatures,2019
50,United States,MegaFace,megaface,39.3299013,-76.6205177,Johns Hopkins University,edu,672fae3da801b2a0d2bad65afdbbbf1b2320623e,citation,https://arxiv.org/pdf/1609.07042.pdf,Pose-Selective Max Pooling for Measuring Similarity,2016
51,China,MegaFace,megaface,22.53521465,113.9315911,Shenzhen University,edu,a32878e85941b5392d58d28e5248f94e16e25d78,citation,https://arxiv.org/pdf/1801.06445.pdf,Quality Classified Image Analysis with Application to Face Detection and Recognition,2018
52,China,MegaFace,megaface,22.4162632,114.2109318,Chinese University of Hong Kong,edu,380d5138cadccc9b5b91c707ba0a9220b0f39271,citation,https://arxiv.org/pdf/1806.00194.pdf,Deep Imbalanced Learning for Face Recognition and Attribute Prediction,2018
53,United States,MegaFace,megaface,40.4432741,-79.9456995,Robotics Institute at Carnegie Mellon University,edu,380d5138cadccc9b5b91c707ba0a9220b0f39271,citation,https://arxiv.org/pdf/1806.00194.pdf,Deep Imbalanced Learning for Face Recognition and Attribute Prediction,2018
54,Israel,MegaFace,megaface,32.7767783,35.0231271,Technion-Israel Institute of Technology,edu,d00787e215bd74d32d80a6c115c4789214da5edb,citation,https://pdfs.semanticscholar.org/d007/87e215bd74d32d80a6c115c4789214da5edb.pdf,Faster and Lighter Online Sparse Dictionary Learning Project report,0
55,China,MegaFace,megaface,39.9808333,116.34101249,Beihang University,edu,0a23bdc55fb0d04acdac4d3ea0a9994623133562,citation,https://arxiv.org/pdf/1806.03018.pdf,Large-scale Bisample Learning on ID vs. Spot Face Recognition,2018
56,United States,MegaFace,megaface,45.57022705,-122.63709346,Concordia University,edu,8e0becfc5fe3ecdd2ac93fabe34634827b21ef2b,citation,https://arxiv.org/pdf/1711.10520.pdf,Learning from Longitudinal Face Demonstration - Where Tractable Deep Modeling Meets Inverse Reinforcement Learning,2017
57,United States,MegaFace,megaface,40.4437954,-79.9465522,"CyLab, Carnegie Mellon, Pittsburgh, USA",edu,8e0becfc5fe3ecdd2ac93fabe34634827b21ef2b,citation,https://arxiv.org/pdf/1711.10520.pdf,Learning from Longitudinal Face Demonstration - Where Tractable Deep Modeling Meets Inverse Reinforcement Learning,2017
58,United States,MegaFace,megaface,33.776033,-84.39884086,Georgia Institute of Technology,edu,9fc17fa5708584fa848164461f82a69e97f6ed69,citation,,Additive Margin Softmax for Face Verification,2018
59,China,MegaFace,megaface,30.672721,104.098806,University of Electronic Science and Technology of China,edu,9fc17fa5708584fa848164461f82a69e97f6ed69,citation,,Additive Margin Softmax for Face Verification,2018
60,Italy,MegaFace,megaface,45.1867156,9.1561041,University of Pavia,edu,746c0205fdf191a737df7af000eaec9409ede73f,citation,,Investigating Nuisances in DCNN-Based Face Recognition,2018
61,Italy,MegaFace,megaface,43.7776426,11.259765,University of Florence,edu,746c0205fdf191a737df7af000eaec9409ede73f,citation,,Investigating Nuisances in DCNN-Based Face Recognition,2018
62,United States,MegaFace,megaface,47.6543238,-122.30800894,University of Washington,edu,28d4e027c7e90b51b7d8908fce68128d1964668a,citation,,Level Playing Field for Million Scale Face Recognition,2017
63,China,MegaFace,megaface,31.30104395,121.50045497,Fudan University,edu,c5e37630d0672e4d44f7dee83ac2c1528be41c2e,citation,,Multi-task Deep Neural Network for Joint Face Recognition and Facial Attribute Prediction,2017
64,United States,MegaFace,megaface,39.65404635,-79.96475355,West Virginia University,edu,b1b7603a70860cbe5ff7b963976b5e6f780c88fc,citation,,A Deep Face Identification Network Enhanced by Facial Attributes Prediction,2018
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