From 362c0ce0cfb7eaaee77510356b3b3a31771e5768 Mon Sep 17 00:00:00 2001 From: "jules@lens" Date: Mon, 18 Feb 2019 14:07:19 +0100 Subject: adding our papers --- site/datasets/final/adience.csv | 102 ---- site/datasets/final/adience.json | 2 +- site/datasets/final/aflw.csv | 212 -------- site/datasets/final/aflw.json | 2 +- site/datasets/final/casia_webface.csv | 312 ------------ site/datasets/final/casia_webface.json | 2 +- site/datasets/final/celeba.json | 2 +- site/datasets/final/cofw.csv | 233 --------- site/datasets/final/cofw.json | 2 +- site/datasets/final/feret.csv | 639 ------------------------ site/datasets/final/feret.json | 2 +- site/datasets/final/helen.json | 2 +- site/datasets/final/ijb_c.csv | 141 ------ site/datasets/final/ijb_c.json | 2 +- site/datasets/final/images_of_groups.csv | 103 ---- site/datasets/final/images_of_groups.json | 2 +- site/datasets/final/imdb_wiki.csv | 130 ----- site/datasets/final/imdb_wiki.json | 2 +- site/datasets/final/lfw.csv | 749 ---------------------------- site/datasets/final/lfw.json | 2 +- site/datasets/final/megaface.json | 2 +- site/datasets/final/morph.csv | 286 ----------- site/datasets/final/morph.json | 2 +- site/datasets/final/morph_nc.csv | 286 ----------- site/datasets/final/morph_nc.json | 2 +- site/datasets/final/msceleb.csv | 113 ----- site/datasets/final/msceleb.json | 2 +- site/datasets/final/pipa.csv | 37 -- site/datasets/final/pipa.json | 2 +- site/datasets/final/uccs.json | 2 +- site/datasets/final/umd_faces.csv | 34 -- site/datasets/final/umd_faces.json | 2 +- site/datasets/final/vgg_faces2.json | 2 +- site/datasets/final/voc.csv | 401 --------------- site/datasets/final/voc.json | 2 +- site/datasets/final/wider_face.json | 2 +- site/datasets/final/yfcc_100m.csv | 69 --- site/datasets/final/yfcc_100m.json | 2 +- site/datasets/final/youtube_faces.json | 2 +- site/datasets/final/youtube_poses.csv | 20 - site/datasets/final/youtube_poses.json | 2 +- site/datasets/unknown/adience.json | 2 +- site/datasets/unknown/aflw.json | 2 +- site/datasets/unknown/casia_webface.json | 2 +- site/datasets/unknown/celeba.json | 2 +- site/datasets/unknown/cofw.json | 2 +- site/datasets/unknown/feret.json | 2 +- site/datasets/unknown/helen.json | 2 +- site/datasets/unknown/ijb_c.json | 2 +- site/datasets/unknown/images_of_groups.json | 2 +- site/datasets/unknown/imdb_wiki.json | 2 +- site/datasets/unknown/lfw.json | 2 +- site/datasets/unknown/megaface.json | 2 +- site/datasets/unknown/morph.json | 2 +- site/datasets/unknown/morph_nc.json | 2 +- site/datasets/unknown/msceleb.json | 2 +- site/datasets/unknown/umd_faces.json | 2 +- site/datasets/unknown/wider_face.json | 2 +- site/datasets/unknown/yfcc_100m.json | 2 +- site/datasets/unknown/youtube_faces.json | 2 +- 60 files changed, 43 insertions(+), 3910 deletions(-) delete mode 100644 site/datasets/final/adience.csv delete mode 100644 site/datasets/final/aflw.csv delete mode 100644 site/datasets/final/casia_webface.csv delete mode 100644 site/datasets/final/cofw.csv delete mode 100644 site/datasets/final/feret.csv delete mode 100644 site/datasets/final/ijb_c.csv delete mode 100644 site/datasets/final/images_of_groups.csv delete mode 100644 site/datasets/final/imdb_wiki.csv delete mode 100644 site/datasets/final/lfw.csv delete mode 100644 site/datasets/final/morph.csv delete mode 100644 site/datasets/final/morph_nc.csv delete mode 100644 site/datasets/final/msceleb.csv delete mode 100644 site/datasets/final/pipa.csv delete mode 100644 site/datasets/final/umd_faces.csv delete mode 100644 site/datasets/final/voc.csv delete mode 100644 site/datasets/final/yfcc_100m.csv delete mode 100644 site/datasets/final/youtube_poses.csv (limited to 'site/datasets') diff --git a/site/datasets/final/adience.csv b/site/datasets/final/adience.csv deleted file mode 100644 index 9c9f2b76..00000000 --- a/site/datasets/final/adience.csv +++ /dev/null @@ -1,102 +0,0 @@ -index,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year -0,Adience,adience,0.0,0.0,,,1be498d4bbc30c3bfd0029114c784bc2114d67c0,main,http://www.openu.ac.il/home/hassner/Adience/EidingerEnbarHassner_tifs.pdf,Age and Gender Estimation of Unfiltered Faces,2014 -1,Adience,adience,28.5456282,77.2731505,"IIIT Delhi, India",edu,f726738954e7055bb3615fa7e8f59f136d3e0bdc,citation,https://arxiv.org/pdf/1803.07385.pdf,Are you eligible? Predicting adulthood from face images via class specific mean autoencoder,2018 -2,Adience,adience,37.43131385,-122.16936535,Stanford University,edu,16d6737b50f969247339a6860da2109a8664198a,citation,https://pdfs.semanticscholar.org/16d6/737b50f969247339a6860da2109a8664198a.pdf,Convolutional Neural Networks for Age and Gender Classification,2016 -3,Adience,adience,40.00229045,116.32098908,Tsinghua University,edu,2149d49c84a83848d6051867290d9c8bfcef0edb,citation,https://doi.org/10.1109/TIFS.2017.2746062,Label-Sensitive Deep Metric Learning for Facial Age Estimation,2018 -4,Adience,adience,51.5217668,-0.13019072,University of London,edu,31ea88f29e7f01a9801648d808f90862e066f9ea,citation,https://arxiv.org/pdf/1605.06391.pdf,Deep Multi-task Representation Learning: A Tensor Factorisation Approach,2016 -5,Adience,adience,40.0044795,116.370238,Chinese Academy of Sciences,edu,d492dbfaa42b4f8b8a74786d7343b3be6a3e9a1d,citation,https://pdfs.semanticscholar.org/d492/dbfaa42b4f8b8a74786d7343b3be6a3e9a1d.pdf,Deep Cost-Sensitive and Order-Preserving Feature Learning for Cross-Population Age Estimation,0 -6,Adience,adience,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,d492dbfaa42b4f8b8a74786d7343b3be6a3e9a1d,citation,https://pdfs.semanticscholar.org/d492/dbfaa42b4f8b8a74786d7343b3be6a3e9a1d.pdf,Deep Cost-Sensitive and Order-Preserving Feature Learning for Cross-Population Age Estimation,0 -7,Adience,adience,34.0224149,-118.28634407,University of Southern California,edu,29f298dd5f806c99951cb434834bc8dcc765df18,citation,https://doi.org/10.1109/ICPR.2016.7899837,Computationally efficient template-based face recognition,2016 -8,Adience,adience,45.5039761,-73.5749687,McGill University,edu,ed9d11e995baeec17c5d2847ec1a8d5449254525,citation,https://pdfs.semanticscholar.org/ed9d/11e995baeec17c5d2847ec1a8d5449254525.pdf,Efficient Gender Classification Using a Deep LDA-Pruned Net,2017 -9,Adience,adience,12.9551259,77.5741985,Bangalore Institute of Technology,edu,10126b467391e153d36f1a496ef5618097775ad1,citation,https://pdfs.semanticscholar.org/1012/6b467391e153d36f1a496ef5618097775ad1.pdf,An Active Age Estimation of Facial image using Anthropometric Model and Fast ICA,2017 -10,Adience,adience,42.36782045,-71.12666653,Harvard University,edu,0ba402af3b8682e2aa89f76bd823ddffdf89fa0a,citation,http://pdfs.semanticscholar.org/c0d8/4377168c554cb8e83099bed940091fe49dec.pdf,Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks,2016 -11,Adience,adience,40.9153196,-73.1270626,Stony Brook University,edu,0ba402af3b8682e2aa89f76bd823ddffdf89fa0a,citation,http://pdfs.semanticscholar.org/c0d8/4377168c554cb8e83099bed940091fe49dec.pdf,Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks,2016 -12,Adience,adience,38.8760446,115.4973873,North China Electric Power University,edu,56f86bef26209c85f2ef66ec23b6803d12ca6cd6,citation,http://arxiv.org/abs/1710.00307,Pyramidal RoR for image classification,2017 -13,Adience,adience,40.00229045,116.32098908,Tsinghua University,edu,51f626540860ad75b68206025a45466a6d087aa6,citation,https://doi.org/10.1109/ICIP.2017.8296595,Cluster convolutional neural networks for facial age estimation,2017 -14,Adience,adience,45.5039761,-73.5749687,McGill University,edu,407bb798ab153bf6156ba2956f8cf93256b6910a,citation,http://pdfs.semanticscholar.org/407b/b798ab153bf6156ba2956f8cf93256b6910a.pdf,Fisher Pruning of Deep Nets for Facial Trait Classification,2018 -15,Adience,adience,39.2899685,-76.62196103,University of Maryland,edu,81fc86e86980a32c47410f0ba7b17665048141ec,citation,http://pdfs.semanticscholar.org/81fc/86e86980a32c47410f0ba7b17665048141ec.pdf,Segment-based Methods for Facial Attribute Detection from Partial Faces,2018 -16,Adience,adience,22.304572,114.17976285,Hong Kong Polytechnic University,edu,dc2f16f967eac710cb9b7553093e9c977e5b761d,citation,https://doi.org/10.1109/ICPR.2016.7900141,Learning a lightweight deep convolutional network for joint age and gender recognition,2016 -17,Adience,adience,23.09461185,113.28788994,Sun Yat-Sen University,edu,dc2f16f967eac710cb9b7553093e9c977e5b761d,citation,https://doi.org/10.1109/ICPR.2016.7900141,Learning a lightweight deep convolutional network for joint age and gender recognition,2016 -18,Adience,adience,39.65404635,-79.96475355,West Virginia University,edu,7a65fc9e78eff3ab6062707deaadde024d2fad40,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/papers/Zhu_A_Study_on_ICCV_2015_paper.pdf,A Study on Apparent Age Estimation,2015 -19,Adience,adience,42.357757,-83.06286711,Wayne State University,edu,28d99dc2d673d62118658f8375b414e5192eac6f,citation,http://www.cs.wayne.edu/~mdong/cvpr17.pdf,Using Ranking-CNN for Age Estimation,2017 -20,Adience,adience,25.0410728,121.6147562,Institute of Information Science,edu,0951f42abbf649bb564a21d4ff5dddf9a5ea54d9,citation,https://arxiv.org/pdf/1806.02023.pdf,Joint Estimation of Age and Gender from Unconstrained Face Images Using Lightweight Multi-Task CNN for Mobile Applications,2018 -21,Adience,adience,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 -22,Adience,adience,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 -23,Adience,adience,40.51865195,-74.44099801,State University of New Jersey,edu,d00e9a6339e34c613053d3b2c132fccbde547b56,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7791154,A cascaded convolutional neural network for age estimation of unconstrained faces,2016 -24,Adience,adience,39.2899685,-76.62196103,University of Maryland,edu,d00e9a6339e34c613053d3b2c132fccbde547b56,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7791154,A cascaded convolutional neural network for age estimation of unconstrained faces,2016 -25,Adience,adience,32.8536333,-117.2035286,Kyung Hee University,edu,9d4692e243e25eb465a0480376beb60a5d2f0f13,citation,https://doi.org/10.1109/ICCE.2016.7430617,Positional Ternary Pattern (PTP): An edge based image descriptor for human age recognition,2016 -26,Adience,adience,1.340216,103.965089,Singapore University of Technology and Design,edu,00823e6c0b6f1cf22897b8d0b2596743723ec51c,citation,https://arxiv.org/pdf/1708.07689.pdf,Understanding and Comparing Deep Neural Networks for Age and Gender Classification,2017 -27,Adience,adience,45.47567215,9.23336232,Università degli Studi di Milano,edu,a713a01971e73d0c3118d0409dc7699a24f521d6,citation,https://doi.org/10.1109/SSCI.2017.8285381,Age estimation based on face images and pre-trained convolutional neural networks,2017 -28,Adience,adience,37.2830003,127.04548469,Ajou University,edu,c43dc4ae68a317b34a79636fadb3bcc4d1ccb61c,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8369763,Age and gender estimation using deep residual learning network,2018 -29,Adience,adience,37.403917,127.159786,Korea Electronics Technology Institute,edu,c43dc4ae68a317b34a79636fadb3bcc4d1ccb61c,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8369763,Age and gender estimation using deep residual learning network,2018 -30,Adience,adience,37.26728,126.9841151,Seoul National University,edu,c43dc4ae68a317b34a79636fadb3bcc4d1ccb61c,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8369763,Age and gender estimation using deep residual learning network,2018 -31,Adience,adience,49.2767454,-122.91777375,Simon Fraser University,edu,975978ee6a32383d6f4f026b944099e7739e5890,citation,https://pdfs.semanticscholar.org/9759/78ee6a32383d6f4f026b944099e7739e5890.pdf,Privacy-Preserving Age Estimation for Content Rating,2018 -32,Adience,adience,49.8091536,-97.13304179,University of Manitoba,edu,975978ee6a32383d6f4f026b944099e7739e5890,citation,https://pdfs.semanticscholar.org/9759/78ee6a32383d6f4f026b944099e7739e5890.pdf,Privacy-Preserving Age Estimation for Content Rating,2018 -33,Adience,adience,33.7774349,-84.3973208,"College of Computing, Georgia Tech",edu,58df849378fbcfb6b1a8ebddfbe4caa450226b9d,citation,https://doi.org/10.1109/ICIP.2017.8296770,Head pose estimation using learned discretization,2017 -34,Adience,adience,39.95472495,-75.15346905,Temple University,edu,58df849378fbcfb6b1a8ebddfbe4caa450226b9d,citation,https://doi.org/10.1109/ICIP.2017.8296770,Head pose estimation using learned discretization,2017 -35,Adience,adience,36.1017956,-79.501733,Elon University,edu,58df849378fbcfb6b1a8ebddfbe4caa450226b9d,citation,https://doi.org/10.1109/ICIP.2017.8296770,Head pose estimation using learned discretization,2017 -36,Adience,adience,23.7289899,90.3982682,Institute of Information Technology,edu,2e58ec57d71b2b2a3e71086234dd7037559cc17e,citation,https://pdfs.semanticscholar.org/2e58/ec57d71b2b2a3e71086234dd7037559cc17e.pdf,A Gender Recognition System from Facial Image,2018 -37,Adience,adience,23.7316957,90.3965275,University of Dhaka,edu,2e58ec57d71b2b2a3e71086234dd7037559cc17e,citation,https://pdfs.semanticscholar.org/2e58/ec57d71b2b2a3e71086234dd7037559cc17e.pdf,A Gender Recognition System from Facial Image,2018 -38,Adience,adience,37.98782705,23.73179733,National Technical University of Athens,edu,bd572e9cbec095bcf5700cb7cd73d1cdc2fe02f4,citation,http://pdfs.semanticscholar.org/bd57/2e9cbec095bcf5700cb7cd73d1cdc2fe02f4.pdf,Deep Learning for Computer Vision: A Brief Review,2018 -39,Adience,adience,47.00646895,-120.5367304,Central Washington University,edu,56c2fb2438f32529aec604e6fc3b06a595ddbfcc,citation,http://pdfs.semanticscholar.org/56c2/fb2438f32529aec604e6fc3b06a595ddbfcc.pdf,Comparison of Recent Machine Learning Techniques for Gender Recognition from Facial Images,2016 -40,Adience,adience,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 -41,Adience,adience,31.83907195,117.26420748,University of Science and Technology of China,edu,47cd161546c59ab1e05f8841b82e985f72e5ddcb,citation,https://doi.org/10.1109/ICIP.2017.8296552,Gender classification in live videos,2017 -42,Adience,adience,25.0410728,121.6147562,Institute of Information Science,edu,1862f2df2e278505c9ca970f9c5a25ea3aeb9686,citation,https://pdfs.semanticscholar.org/1862/f2df2e278505c9ca970f9c5a25ea3aeb9686.pdf,Merging Deep Neural Networks for Mobile Devices,0 -43,Adience,adience,45.42580475,-75.68740118,University of Ottawa,edu,16820ccfb626dcdc893cc7735784aed9f63cbb70,citation,http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W12/papers/Azarmehr_Real-Time_Embedded_Age_2015_CVPR_paper.pdf,Real-time embedded age and gender classification in unconstrained video,2015 -44,Adience,adience,37.26728,126.9841151,Seoul National University,edu,282503fa0285240ef42b5b4c74ae0590fe169211,citation,http://pdfs.semanticscholar.org/2825/03fa0285240ef42b5b4c74ae0590fe169211.pdf,Feeding Hand-Crafted Features for Enhancing the Performance of Convolutional Neural Networks,2018 -45,Adience,adience,32.8536333,-117.2035286,Kyung Hee University,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7903648,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017 -46,Adience,adience,24.7246403,46.62335012,King Saud University,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7903648,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017 -47,Adience,adience,23.7289899,90.3982682,Institute of Information Technology,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7903648,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017 -48,Adience,adience,53.21967825,6.56251482,University of Groningen,edu,361c9ba853c7d69058ddc0f32cdbe94fbc2166d5,citation,http://pdfs.semanticscholar.org/361c/9ba853c7d69058ddc0f32cdbe94fbc2166d5.pdf,Deep Reinforcement Learning of Video Games,2017 -49,Adience,adience,41.1664858,-73.1920564,University of Bridgeport,edu,ac9a331327cceda4e23f9873f387c9fd161fad76,citation,http://pdfs.semanticscholar.org/ac9a/331327cceda4e23f9873f387c9fd161fad76.pdf,Deep Convolutional Neural Network for Age Estimation based on VGG-Face Model,2017 -50,Adience,adience,53.21967825,6.56251482,University of Groningen,edu,4ff4c27e47b0aa80d6383427642bb8ee9d01c0ac,citation,https://doi.org/10.1109/SSCI.2015.37,Deep Convolutional Neural Networks and Support Vector Machines for Gender Recognition,2015 -51,Adience,adience,40.4319722,-86.92389368,Purdue University,edu,6193c833ad25ac27abbde1a31c1cabe56ce1515b,citation,https://pdfs.semanticscholar.org/5f25/7ca18a92c3595db3bda3224927ec494003a5.pdf,Trojaning Attack on Neural Networks,2018 -52,Adience,adience,40.4319722,-86.92389368,Purdue University,edu,b18858ad6ec88d8b443dffd3e944e653178bc28b,citation,http://pdfs.semanticscholar.org/b188/58ad6ec88d8b443dffd3e944e653178bc28b.pdf,Trojaning Attack on Neural Networks,2017 -53,Adience,adience,40.9153196,-73.1270626,Stony Brook University,edu,25bf288b2d896f3c9dab7e7c3e9f9302e7d6806b,citation,http://pdfs.semanticscholar.org/25bf/288b2d896f3c9dab7e7c3e9f9302e7d6806b.pdf,Neural Networks with Smooth Adaptive Activation Functions for Regression,2016 -54,Adience,adience,40.9153196,-73.1270626,Stony Brook University,edu,1190cba0cae3c8bb81bf80d6a0a83ae8c41240bc,citation,https://pdfs.semanticscholar.org/1190/cba0cae3c8bb81bf80d6a0a83ae8c41240bc.pdf,Squared Earth Mover ’ s Distance Loss for Training Deep Neural Networks on Ordered-Classes,2017 -55,Adience,adience,40.9153196,-73.1270626,Stony Brook University,edu,14e9158daf17985ccbb15c9cd31cf457e5551990,citation,http://pdfs.semanticscholar.org/14e9/158daf17985ccbb15c9cd31cf457e5551990.pdf,ConvNets with Smooth Adaptive Activation Functions for Regression,2017 -56,Adience,adience,40.90826665,-73.11520891,Stony Brook University Hospital,edu,14e9158daf17985ccbb15c9cd31cf457e5551990,citation,http://pdfs.semanticscholar.org/14e9/158daf17985ccbb15c9cd31cf457e5551990.pdf,ConvNets with Smooth Adaptive Activation Functions for Regression,2017 -57,Adience,adience,45.5039761,-73.5749687,McGill 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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 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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 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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 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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 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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 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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 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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 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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 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Maryland,edu,23dd8d17ce09c22d367e4d62c1ccf507bcbc64da,citation,https://pdfs.semanticscholar.org/23dd/8d17ce09c22d367e4d62c1ccf507bcbc64da.pdf,Deep Density Clustering of Unconstrained Faces ( Supplementary Material ),2018 -84,CASIA Webface,casia_webface,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,3dfb822e16328e0f98a47209d7ecd242e4211f82,citation,https://arxiv.org/pdf/1708.08197.pdf,Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments,2017 -85,CASIA Webface,casia_webface,40.0044795,116.370238,Chinese Academy of Sciences,edu,64b9ad39d115f3e375bde4f70fb8fdef5d681df8,citation,https://doi.org/10.1109/ICB.2016.7550088,Bootstrapping Joint Bayesian model for robust face verification,2016 -86,CASIA Webface,casia_webface,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 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Maryland,edu,5865b6d83ba6dbbf9167f1481e9339c2ef1d1f6b,citation,https://doi.org/10.1109/ICPR.2016.7900278,Regularized metric adaptation for unconstrained face verification,2016 -91,CASIA Webface,casia_webface,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,e9c008d31da38d9eef67a28d2c77cb7daec941fb,citation,https://arxiv.org/pdf/1708.03769.pdf,Noisy Softmax: Improving the Generalization Ability of DCNN via Postponing the Early Softmax Saturation,2017 -92,CASIA Webface,casia_webface,23.09461185,113.28788994,Sun Yat-Sen University,edu,c675534be881e59a78a5986b8fb4e649ddd2abbe,citation,https://doi.org/10.1109/ICIP.2017.8296548,Face recognition by landmark pooling-based CNN with concentrate loss,2017 -93,CASIA Webface,casia_webface,40.51865195,-74.44099801,State University of New Jersey,edu,ea03a569272d329090fe60d6bff8d119e18057d7,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7532906,Fisher vector encoded deep convolutional features for unconstrained face verification,2016 -94,CASIA Webface,casia_webface,39.2899685,-76.62196103,University of Maryland,edu,ea03a569272d329090fe60d6bff8d119e18057d7,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7532906,Fisher vector encoded deep convolutional features for unconstrained face verification,2016 -95,CASIA Webface,casia_webface,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 -96,CASIA Webface,casia_webface,36.20304395,117.05842113,Tianjin University,edu,5180df9d5eb26283fb737f491623395304d57497,citation,https://arxiv.org/pdf/1804.10899.pdf,Scalable Angular Discriminative Deep Metric Learning for Face Recognition,2018 -97,CASIA Webface,casia_webface,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,3ac3a714042d3ebc159546c26321a1f8f4f5f80c,citation,http://dl.acm.org/citation.cfm?id=3025149,Clustering lightened deep representation for large scale face identification,2017 -98,CASIA Webface,casia_webface,37.26728,126.9841151,Seoul National University,edu,282503fa0285240ef42b5b4c74ae0590fe169211,citation,http://pdfs.semanticscholar.org/2825/03fa0285240ef42b5b4c74ae0590fe169211.pdf,Feeding Hand-Crafted Features for Enhancing the Performance of Convolutional Neural Networks,2018 -99,CASIA Webface,casia_webface,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,29db046dd1f8100b279c3f5f5c5ef19bdbf5af9a,citation,https://arxiv.org/pdf/1706.04717.pdf,Recent Progress of Face Image Synthesis,2017 -100,CASIA Webface,casia_webface,39.2899685,-76.62196103,University of Maryland,edu,4f7b92bd678772552b3c3edfc9a7c5c4a8c60a8e,citation,https://pdfs.semanticscholar.org/4f7b/92bd678772552b3c3edfc9a7c5c4a8c60a8e.pdf,Deep Density Clustering of Unconstrained Faces,0 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University,edu,450c6a57f19f5aa45626bb08d7d5d6acdb863b4b,citation,https://arxiv.org/pdf/1805.00611.pdf,Towards Interpretable Face Recognition,2018 -121,CASIA Webface,casia_webface,30.2931534,120.1620458,Zhejiang University of Technology,edu,cb9921d5fc4ffa50be537332e111f03d74622442,citation,https://doi.org/10.1007/978-3-319-46654-5_79,Face Occlusion Detection Using Cascaded Convolutional Neural Network,2016 -122,CASIA Webface,casia_webface,29.6328784,-82.3490133,University of Florida,edu,291de30ceecb5dcf0644c35e2b5935d341ea148b,citation,https://arxiv.org/pdf/1810.00024.pdf,Explainable Black-Box Attacks Against Model-based Authentication,2018 -123,CASIA Webface,casia_webface,42.3383668,-71.08793524,Northeastern University,edu,3f540faf85e1f8de6ce04fb37e556700b67e4ad3,citation,http://pdfs.semanticscholar.org/3f54/0faf85e1f8de6ce04fb37e556700b67e4ad3.pdf,Face Verification with Multi-Task and Multi-Scale Feature Fusion,2017 -124,CASIA Webface,casia_webface,29.7207902,-95.34406271,University of Houston,edu,8334da483f1986aea87b62028672836cb3dc6205,citation,https://arxiv.org/pdf/1805.06306.pdf,Fully Associative Patch-Based 1-to-N Matcher for Face Recognition,2018 -125,CASIA Webface,casia_webface,39.2899685,-76.62196103,University of Maryland,edu,0019925779bff96448f0c75492717e4473f88377,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w3/papers/Reale_Deep_Heterogeneous_Face_CVPR_2017_paper.pdf,Deep Heterogeneous Face Recognition Networks Based on Cross-Modal Distillation and an Equitable Distance Metric,2017 -126,CASIA Webface,casia_webface,45.7413921,126.62552755,Harbin Institute of Technology,edu,05455f5e3c3989be4991cb74b73cdfd0d6522622,citation,https://arxiv.org/pdf/1804.04829.pdf,Learning Warped Guidance for Blind Face Restoration,2018 -127,CASIA Webface,casia_webface,23.09461185,113.28788994,Sun Yat-Sen University,edu,05455f5e3c3989be4991cb74b73cdfd0d6522622,citation,https://arxiv.org/pdf/1804.04829.pdf,Learning Warped Guidance for Blind Face Restoration,2018 -128,CASIA Webface,casia_webface,38.0333742,-84.5017758,University of Kentucky,edu,05455f5e3c3989be4991cb74b73cdfd0d6522622,citation,https://arxiv.org/pdf/1804.04829.pdf,Learning Warped Guidance for Blind Face Restoration,2018 -129,CASIA Webface,casia_webface,51.49887085,-0.17560797,Imperial College London,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017 -130,CASIA Webface,casia_webface,51.59029705,-0.22963221,Middlesex University,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017 -131,CASIA Webface,casia_webface,25.01682835,121.53846924,National Taiwan University,edu,81884e1de00e59f24bc20254584d73a1a1806933,citation,https://arxiv.org/pdf/1811.02328.pdf,Super-Identity Convolutional Neural Network for Face Hallucination,2018 -132,CASIA Webface,casia_webface,39.993008,116.329882,SenseTime,company,81884e1de00e59f24bc20254584d73a1a1806933,citation,https://arxiv.org/pdf/1811.02328.pdf,Super-Identity Convolutional Neural Network for Face Hallucination,2018 -133,CASIA Webface,casia_webface,30.284151,-97.73195598,University of Texas at Austin,edu,81884e1de00e59f24bc20254584d73a1a1806933,citation,https://arxiv.org/pdf/1811.02328.pdf,Super-Identity Convolutional Neural Network for Face Hallucination,2018 -134,CASIA Webface,casia_webface,47.6543238,-122.30800894,University of Washington,edu,405526dfc79de98f5bf3c97bf4aa9a287700f15d,citation,http://pdfs.semanticscholar.org/8a6c/57fcd99a77982ec754e0b97fd67519ccb60c.pdf,MegaFace: A Million Faces for Recognition at Scale,2015 -135,CASIA Webface,casia_webface,32.77824165,34.99565673,Open University of Israel,edu,582edc19f2b1ab2ac6883426f147196c8306685a,citation,http://pdfs.semanticscholar.org/be6c/db7b181e73f546d43cf2ab6bc7181d7d619b.pdf,Do We Really Need to Collect Millions of Faces for Effective Face Recognition?,2016 -136,CASIA Webface,casia_webface,36.3697191,127.362537,Korea Advanced Institute of Science and Technology,edu,076d3fc800d882445c11b9af466c3af7d2afc64f,citation,http://slsp.kaist.ac.kr/paperdata/Face_attribute_classification.pdf,Face attribute classification using attribute-aware correlation map and gated convolutional neural networks,2015 -137,CASIA Webface,casia_webface,31.83907195,117.26420748,University of Science and Technology of China,edu,e1256ff535bf4c024dd62faeb2418d48674ddfa2,citation,https://arxiv.org/pdf/1803.11182.pdf,Towards Open-Set Identity Preserving Face Synthesis,2018 -138,CASIA Webface,casia_webface,36.383765,127.36694,"Electronics and Telecommunications Research Institute, Daejeon, Korea",edu,77c5437107f8138d48cb7e10b2b286fa51473678,citation,https://doi.org/10.1109/URAI.2016.7734005,A pseudo ensemble convolutional neural networks,2016 -139,CASIA Webface,casia_webface,36.3851395,127.3683413,"University of Science and Technology, Korea",edu,77c5437107f8138d48cb7e10b2b286fa51473678,citation,https://doi.org/10.1109/URAI.2016.7734005,A pseudo ensemble convolutional neural networks,2016 -140,CASIA Webface,casia_webface,40.00229045,116.32098908,Tsinghua University,edu,93eb3963bc20e28af26c53ef3bce1e76b15e3209,citation,https://doi.org/10.1109/ICIP.2017.8296992,Occlusion robust face recognition based on mask learning,2017 -141,CASIA Webface,casia_webface,40.0141905,-83.0309143,University of Electronic Science and Technology of China,edu,16b9d258547f1eccdb32111c9f45e2e4bbee79af,citation,https://arxiv.org/pdf/1704.06369.pdf,NormFace: L2 Hypersphere Embedding for Face Verification,2017 -142,CASIA Webface,casia_webface,39.94976005,116.33629046,Beijing Jiaotong University,edu,7e2cfbfd43045fbd6aabd9a45090a5716fc4e179,citation,https://arxiv.org/pdf/1808.00435.pdf,Global Norm-Aware Pooling for Pose-Robust Face Recognition at Low False Positive Rate,2018 -143,CASIA Webface,casia_webface,40.0044795,116.370238,Chinese Academy of Sciences,edu,99facca6fc50cc30f13b7b6dd49ace24bc94f702,citation,https://arxiv.org/pdf/1609.03892.pdf,VIPLFaceNet: an open source deep face recognition SDK,2016 -144,CASIA Webface,casia_webface,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,99facca6fc50cc30f13b7b6dd49ace24bc94f702,citation,https://arxiv.org/pdf/1609.03892.pdf,VIPLFaceNet: an open source deep face recognition SDK,2016 -145,CASIA Webface,casia_webface,25.01682835,121.53846924,National Taiwan University,edu,b50edfea790f86373407a964b4255bf8e436d377,citation,http://doi.acm.org/10.1145/3136755.3143008,Group emotion recognition with individual facial emotion CNNs and global image based CNNs,2017 -146,CASIA Webface,casia_webface,1.2962018,103.77689944,National University of Singapore,edu,c17c7b201cfd0bcd75441afeaa734544c6ca3416,citation,https://doi.org/10.1109/TCSVT.2016.2587389,Layerwise Class-Aware Convolutional Neural Network,2017 -147,CASIA Webface,casia_webface,32.0575279,118.78682252,Southeast University,edu,c17c7b201cfd0bcd75441afeaa734544c6ca3416,citation,https://doi.org/10.1109/TCSVT.2016.2587389,Layerwise Class-Aware Convolutional Neural Network,2017 -148,CASIA Webface,casia_webface,22.3386304,114.2620337,Hong Kong University of Science and Technology,edu,585260468d023ffc95f0e539c3fa87254c28510b,citation,http://pdfs.semanticscholar.org/5852/60468d023ffc95f0e539c3fa87254c28510b.pdf,Cardea: Context-Aware Visual Privacy Protection from Pervasive Cameras,2016 -149,CASIA Webface,casia_webface,39.65404635,-79.96475355,West Virginia University,edu,3b9b200e76a35178da940279d566bbb7dfebb787,citation,http://pdfs.semanticscholar.org/3b9b/200e76a35178da940279d566bbb7dfebb787.pdf,Learning Channel Inter-dependencies at Multiple Scales on Dense Networks for Face Recognition,2017 -150,CASIA Webface,casia_webface,39.65404635,-79.96475355,West Virginia University,edu,8bfada57140aa1aa22a575e960c2a71140083293,citation,http://pdfs.semanticscholar.org/8bfa/da57140aa1aa22a575e960c2a71140083293.pdf,Can we match Ultraviolet Face Images against their Visible Counterparts?,2015 -151,CASIA Webface,casia_webface,40.0044795,116.370238,Chinese Academy of Sciences,edu,72a7eb68f0955564e1ceafa75aeeb6b5bbb14e7e,citation,https://pdfs.semanticscholar.org/72a7/eb68f0955564e1ceafa75aeeb6b5bbb14e7e.pdf,Face Recognition with Contrastive Convolution,2018 -152,CASIA Webface,casia_webface,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,72a7eb68f0955564e1ceafa75aeeb6b5bbb14e7e,citation,https://pdfs.semanticscholar.org/72a7/eb68f0955564e1ceafa75aeeb6b5bbb14e7e.pdf,Face Recognition with Contrastive Convolution,2018 -153,CASIA Webface,casia_webface,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,44b827df6c433ca49bcf44f9f3ebfdc0774ee952,citation,https://doi.org/10.1109/LSP.2017.2726105,Deep Correlation Feature Learning for Face Verification in the Wild,2017 -154,CASIA Webface,casia_webface,22.42031295,114.20788644,Chinese University of Hong Kong,edu,58d76380d194248b3bb291b8c7c5137a0a376897,citation,https://pdfs.semanticscholar.org/58d7/6380d194248b3bb291b8c7c5137a0a376897.pdf,FaceID-GAN : Learning a Symmetry Three-Player GAN for Identity-Preserving Face Synthesis,2018 -155,CASIA Webface,casia_webface,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,58d76380d194248b3bb291b8c7c5137a0a376897,citation,https://pdfs.semanticscholar.org/58d7/6380d194248b3bb291b8c7c5137a0a376897.pdf,FaceID-GAN : Learning a Symmetry Three-Player GAN for Identity-Preserving Face Synthesis,2018 -156,CASIA Webface,casia_webface,39.2899685,-76.62196103,University of Maryland,edu,b6f758be954d34817d4ebaa22b30c63a4b8ddb35,citation,http://arxiv.org/abs/1703.04835,A Proximity-Aware Hierarchical Clustering of Faces,2017 -157,CASIA Webface,casia_webface,28.2290209,112.99483204,"National University of Defense Technology, China",edu,511a8cdf2127ef8aa07cbdf9660fe9e0e2dfbde7,citation,https://pdfs.semanticscholar.org/511a/8cdf2127ef8aa07cbdf9660fe9e0e2dfbde7.pdf,A Community Detection Approach to Cleaning Extremely Large Face Database,2018 -158,CASIA Webface,casia_webface,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,cdef0eaff4a3c168290d238999fc066ebc3a93e8,citation,https://arxiv.org/pdf/1707.07391.pdf,Contrastive-center loss for deep neural networks,2017 -159,CASIA Webface,casia_webface,30.2810654,120.02139087,"Alibaba Group, Hangzhou, China",edu,1e62ca5845a6f0492574a5da049e9b43dbeadb1b,citation,https://doi.org/10.1109/LSP.2016.2637400,Cross-Modality Face Recognition via Heterogeneous Joint Bayesian,2017 -160,CASIA 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University,edu,9fb93b7c2bae866608f26c4254e5bd69cc5031d6,citation,https://arxiv.org/pdf/1809.08999.pdf,Fast Geometrically-Perturbed Adversarial Faces,2018 -175,CASIA Webface,casia_webface,32.1638824,34.8115862,FDNA Israel,company,92de9a54515f4ac8cc8e4e6b0dfab20e5e6bb09d,citation,https://doi.org/10.1109/ICIP.2016.7533062,Quality scores for deep regression systems,2016 -176,CASIA Webface,casia_webface,39.2899685,-76.62196103,University of Maryland,edu,2d748f8ee023a5b1fbd50294d176981ded4ad4ee,citation,http://pdfs.semanticscholar.org/2d74/8f8ee023a5b1fbd50294d176981ded4ad4ee.pdf,Triplet Similarity Embedding for Face Verification,2016 -177,CASIA Webface,casia_webface,40.51865195,-74.44099801,State University of New Jersey,edu,5495e224ac7b45b9edc5cfeabbb754d8a40a879b,citation,http://pdfs.semanticscholar.org/5495/e224ac7b45b9edc5cfeabbb754d8a40a879b.pdf,Feature Reconstruction Disentangling for Pose-invariant Face Recognition Supplementary Material,2017 -178,CASIA 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University,edu,2594a77a3f0dd5073f79ba620e2f287804cec630,citation,https://arxiv.org/pdf/1702.06925v1.pdf,Regularizing face verification nets for pain intensity regression,2017 -196,CASIA Webface,casia_webface,41.70456775,-86.23822026,University of Notre Dame,edu,987a649cb33302c41412419f8eeb77048aa5513e,citation,https://arxiv.org/pdf/1803.07140.pdf,Visual Psychophysics for Making Face Recognition Algorithms More Explainable,2018 -197,CASIA Webface,casia_webface,42.36782045,-71.12666653,Harvard University,edu,987a649cb33302c41412419f8eeb77048aa5513e,citation,https://arxiv.org/pdf/1803.07140.pdf,Visual Psychophysics for Making Face Recognition Algorithms More Explainable,2018 -198,CASIA Webface,casia_webface,43.614386,7.071125,EURECOM,edu,70569810e46f476515fce80a602a210f8d9a2b95,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2016.105,Apparent Age Estimation from Face Images Combining General and Children-Specialized Deep Learning Models,2016 -199,CASIA Webface,casia_webface,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 -200,CASIA Webface,casia_webface,40.0141905,-83.0309143,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 -201,CASIA Webface,casia_webface,31.30104395,121.50045497,Fudan University,edu,5a259f2f5337435f841d39dada832ab24e7b3325,citation,http://doi.acm.org/10.1145/2964284.2984059,Face Recognition via Active Annotation and Learning,2016 -202,CASIA Webface,casia_webface,40.0044795,116.370238,Chinese Academy of Sciences,edu,5a259f2f5337435f841d39dada832ab24e7b3325,citation,http://doi.acm.org/10.1145/2964284.2984059,Face Recognition via 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University,edu,8d955b025495522e67e8cb6e29436001ebbd0abb,citation,https://arxiv.org/pdf/1803.11366.pdf,Disentangling Features in 3D Face Shapes for Joint Face Reconstruction and Recognition,2018 -211,CASIA Webface,casia_webface,23.09461185,113.28788994,Sun Yat-Sen University,edu,c92e36689ef561df726a7ae861d9c166c3934908,citation,https://doi.org/10.1109/ICPR.2016.7900140,Face hallucination by deep traversal network,2016 -212,CASIA Webface,casia_webface,22.42031295,114.20788644,Chinese University of Hong Kong,edu,2296d79753118cfcd0fecefece301557f4cb66e2,citation,https://arxiv.org/pdf/1804.03487.pdf,Exploring Disentangled Feature Representation Beyond Face Identification,2018 -213,CASIA Webface,casia_webface,39.993008,116.329882,SenseTime,company,2296d79753118cfcd0fecefece301557f4cb66e2,citation,https://arxiv.org/pdf/1804.03487.pdf,Exploring Disentangled Feature Representation Beyond Face Identification,2018 -214,CASIA Webface,casia_webface,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 -215,CASIA Webface,casia_webface,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 -216,CASIA Webface,casia_webface,42.718568,-84.47791571,Michigan State University,edu,486c9a0e5eb1e0bf107c31c2bf9689b25e18383b,citation,https://arxiv.org/pdf/1804.08790.pdf,Face Recognition: Primates in the Wild,2018 -217,CASIA Webface,casia_webface,38.8920756,-104.79716389,"University of Colorado, Colorado Springs",edu,d4f1eb008eb80595bcfdac368e23ae9754e1e745,citation,https://arxiv.org/pdf/1708.02337.pdf,Unconstrained Face Detection and Open-Set Face Recognition Challenge,2017 -218,CASIA Webface,casia_webface,40.8419836,-73.94368971,Columbia University,edu,61f93ed515b3bfac822deed348d9e21d5dffe373,citation,http://dvmmweb.cs.columbia.edu/files/set_hash_wacv17.pdf,Deep Image Set Hashing,2017 -219,CASIA Webface,casia_webface,39.2899685,-76.62196103,University of Maryland,edu,19458454308a9f56b7de76bf7d8ff8eaa52b0173,citation,https://pdfs.semanticscholar.org/1945/8454308a9f56b7de76bf7d8ff8eaa52b0173.pdf,Deep Features for Recognizing Disguised Faces in the Wild,0 -220,CASIA Webface,casia_webface,41.10427915,29.02231159,Istanbul Technical University,edu,7fb7ccc1aa093ca526f2d8b6f2c404d2c886f69a,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8404767,A multi-view face database from Turkish TV series,2018 -221,CASIA Webface,casia_webface,39.9922379,116.30393816,Peking University,edu,26973cf1552250f402c82e9a4445f03fe6757b58,citation,http://doi.acm.org/10.1145/3126686.3130239,Surveillance Video Quality Assessment Based on Face Recognition,2017 -222,CASIA 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China,edu,3107316f243233d45e3c7e5972517d1ed4991f91,citation,http://arxiv.org/abs/1703.10155,CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training,2017 -285,CASIA Webface,casia_webface,46.010737,8.958109,University of Lugano,edu,cae41c3d5508f57421faf672ee1bea0da4be66e0,citation,https://doi.org/10.1109/ICPR.2016.7900298,Palmprint recognition via discriminative index learning,2016 -286,CASIA Webface,casia_webface,39.2899685,-76.62196103,University of Maryland,edu,a3201e955d6607d383332f3a12a7befa08c5a18c,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7900276,VLAD encoded Deep Convolutional features for unconstrained face verification,2016 -287,CASIA Webface,casia_webface,40.47913175,-74.43168868,Rutgers University,edu,a3201e955d6607d383332f3a12a7befa08c5a18c,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7900276,VLAD encoded Deep Convolutional features for unconstrained face verification,2016 -288,CASIA 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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 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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 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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 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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 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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 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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 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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 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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 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Surrey,edu,81a8b2e55bcea9d9b26e67fcbb5a30ca8a8defc3,citation,http://multispectral-imagery-lab.sandbox.wvu.edu/files/d/337b61b4-b6af-4c96-8314-c282ebebf299/databasesizeeffectsonperformancesmartcardfaceverification.pdf,Database size effects on performance on a smart card face verification system,2006 -34,FERET,feret,41.10427915,29.02231159,Istanbul Technical University,edu,b8b0f0ca35cb02334aaa3192559fb35f0c90f8fa,citation,http://pdfs.semanticscholar.org/b8b0/f0ca35cb02334aaa3192559fb35f0c90f8fa.pdf,Face Recognition in Low-resolution Images by Using Local Zernike Moments,2014 -35,FERET,feret,1.29500195,103.84909214,Singapore Management University,edu,76d1c6c6b67e67ced1f19a89a5034dafc9599f25,citation,http://doi.acm.org/10.1145/2590296.2590315,Understanding OSN-based facial disclosure against face authentication systems,2014 -36,FERET,feret,50.89273635,-1.39464295,University of Southampton,edu,8a12edaf81fd38f81057cf9577c822eb09ff6fc1,citation,http://pdfs.semanticscholar.org/8a12/edaf81fd38f81057cf9577c822eb09ff6fc1.pdf,Measuring and mitigating targeted biometric impersonation,2014 -37,FERET,feret,65.0592157,25.46632601,University of Oulu,edu,8a12edaf81fd38f81057cf9577c822eb09ff6fc1,citation,http://pdfs.semanticscholar.org/8a12/edaf81fd38f81057cf9577c822eb09ff6fc1.pdf,Measuring and mitigating targeted biometric impersonation,2014 -38,FERET,feret,1.3484104,103.68297965,Nanyang Technological University,edu,4b86e711658003a600666d3ccfa4a9905463df1c,citation,https://pdfs.semanticscholar.org/4b86/e711658003a600666d3ccfa4a9905463df1c.pdf,Fusion of Appearance Image and Passive Stereo Depth Map for Face Recognition Based on the Bilateral 2DLDA,2007 -39,FERET,feret,40.7286484,-73.9956863,Courant Institute of Mathematical Sciences,edu,4b8d80f91d271f61b26db5ad627e24e59955c56a,citation,http://pdfs.semanticscholar.org/4b8d/80f91d271f61b26db5ad627e24e59955c56a.pdf,Learning Long-Range Vision for an Offroad Robot,2008 -40,FERET,feret,40.72925325,-73.99625394,New York University,edu,4b8d80f91d271f61b26db5ad627e24e59955c56a,citation,http://pdfs.semanticscholar.org/4b8d/80f91d271f61b26db5ad627e24e59955c56a.pdf,Learning Long-Range Vision for an Offroad Robot,2008 -41,FERET,feret,51.24303255,-0.59001382,University of Surrey,edu,7af15295224c3ad69d56f17ff635763dd008a8a4,citation,http://pdfs.semanticscholar.org/7af1/5295224c3ad69d56f17ff635763dd008a8a4.pdf,Learning Support Vectors for Face Authentication: Sensitivity to Mis-Registrations,2007 -42,FERET,feret,50.0764296,14.41802312,Czech Technical University,edu,7af15295224c3ad69d56f17ff635763dd008a8a4,citation,http://pdfs.semanticscholar.org/7af1/5295224c3ad69d56f17ff635763dd008a8a4.pdf,Learning Support Vectors for Face Authentication: Sensitivity to Mis-Registrations,2007 -43,FERET,feret,40.00229045,116.32098908,Tsinghua University,edu,5ea51401eea9a50a16bd17471bfd559d2d989760,citation,http://pdfs.semanticscholar.org/5ea5/1401eea9a50a16bd17471bfd559d2d989760.pdf,Robust Face Alignment Based on Hierarchical Classifier Network,2006 -44,FERET,feret,40.0044795,116.370238,Chinese Academy of Sciences,edu,71644fab2275cfd6a8f770a26aba4e6228e85dec,citation,http://www.jdl.ac.cn/doc/2011/20131910365517756_2012_eccv_mnkan_mvda.pdf,Multi-View Discriminant Analysis,2012 -45,FERET,feret,31.4006332,74.2137296,"COMSATS Institute of Information Technology, Lahore",edu,280bc9751593897091015aaf2cab39805768b463,citation,http://pdfs.semanticscholar.org/280b/c9751593897091015aaf2cab39805768b463.pdf,Gender Perception From Faces Using Boosted LBPH (Local Binary Patten Histograms),2013 -46,FERET,feret,34.0687788,-118.4450094,"University of California, Los Angeles",edu,23b80dc704e25cf52b5a14935002fc083ce9c317,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2007.383035,Learning Generative Models via Discriminative Approaches,2007 -47,FERET,feret,45.42580475,-75.68740118,University of Ottawa,edu,857ad04fca2740b016f0066b152bd1fa1171483f,citation,http://pdfs.semanticscholar.org/857a/d04fca2740b016f0066b152bd1fa1171483f.pdf,Sample Images can be Independently Restored from Face Recognition Templates,2003 -48,FERET,feret,8.76554685,77.65100445,Manonmaniam Sundaranar University,edu,87b81c8821a2cb9cdf26c75c1531717cab4b942f,citation,http://pdfs.semanticscholar.org/87b8/1c8821a2cb9cdf26c75c1531717cab4b942f.pdf,Face Detection with Facial Features and Gender Classification Based On Support Vector Machine,2010 -49,FERET,feret,43.66333345,-79.39769975,University of Toronto,edu,099ce5cb6f42bff5ad117852d62c5a07e6407b8a,citation,https://pdfs.semanticscholar.org/099c/e5cb6f42bff5ad117852d62c5a07e6407b8a.pdf,Spectral Methods for Multi-Scale Feature Extraction and Data Clustering,0 -50,FERET,feret,34.0224149,-118.28634407,University of Southern California,edu,21358489b5ce0e94ff37792a8a5eea198e7272f3,citation,http://pdfs.semanticscholar.org/c0cc/2073cad539d979fc6f860177b531b45fafc1.pdf,Face Inpainting with Local Linear Representations,2004 -51,FERET,feret,61.44964205,23.85877462,Tampere University of Technology,edu,dc4e4b9c507e8be2d832faf64e5a2e8887115265,citation,https://pdfs.semanticscholar.org/dc4e/4b9c507e8be2d832faf64e5a2e8887115265.pdf,Face Retrieval Based on Robust Local Features and Statistical-Structural Learning Approach,2008 -52,FERET,feret,37.3219575,127.1250723,Dankook University,edu,891d435fd1a070bb66225abfd62b2e2c5350e87c,citation,https://pdfs.semanticscholar.org/891d/435fd1a070bb66225abfd62b2e2c5350e87c.pdf,Selective Feature Generation Method for Classification of Low-dimensional Data,2018 -53,FERET,feret,32.8536333,-117.2035286,Kyung Hee University,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7903648,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017 -54,FERET,feret,24.7246403,46.62335012,King Saud University,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7903648,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017 -55,FERET,feret,23.7289899,90.3982682,Institute of Information Technology,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7903648,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017 -56,FERET,feret,51.24303255,-0.59001382,University of Surrey,edu,cbb55f5885f9a0d0bfaa2c0bf5293ef45a04c5cd,citation,https://pdfs.semanticscholar.org/cbb5/5f5885f9a0d0bfaa2c0bf5293ef45a04c5cd.pdf,Performance Characterisation of Face Recognition Algorithms and Their Sensitivity to Severe Illumination Changes,2006 -57,FERET,feret,40.0044795,116.370238,Chinese Academy of 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Eigenvectors Contribute?,2002 -64,FERET,feret,36.1017956,-79.501733,Elon University,edu,a129c30b176820bf7f4756b4b4efc92d2a83f190,citation,https://pdfs.semanticscholar.org/a129/c30b176820bf7f4756b4b4efc92d2a83f190.pdf,Older adults' associative memory is modified by manner of presentation at encoding and retrieval.,2018 -65,FERET,feret,13.01119095,74.79498825,"National Institute of Technology, Karnataka",edu,e1fac9e9427499d3758213daf1c781b9a42a3420,citation,https://pdfs.semanticscholar.org/7c90/60a809bd28ef61421588f48e33f6eae6ddfd.pdf,Face Image Retrieval Based on Probe Sketch Using SIFT Feature Descriptors,2012 -66,FERET,feret,35.9542493,-83.9307395,University of Tennessee,edu,7735f63e5790006cb3d989c8c19910e40200abfc,citation,http://pdfs.semanticscholar.org/7735/f63e5790006cb3d989c8c19910e40200abfc.pdf,Multispectral Imaging For Face Recognition Over Varying Illumination,2008 -67,FERET,feret,32.0565957,118.77408833,Nanjing 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Maryland,edu,355af3c3adbb17d25f0d2a4193e3daadffc0d4e8,citation,http://pdfs.semanticscholar.org/355a/f3c3adbb17d25f0d2a4193e3daadffc0d4e8.pdf,Pattern recognition: Historical perspective and future directions,2000 -75,FERET,feret,38.83133325,-77.30798839,George Mason University,edu,355af3c3adbb17d25f0d2a4193e3daadffc0d4e8,citation,http://pdfs.semanticscholar.org/355a/f3c3adbb17d25f0d2a4193e3daadffc0d4e8.pdf,Pattern recognition: Historical perspective and future directions,2000 -76,FERET,feret,40.8927159,29.37863323,Sabanci University,edu,1e6d1e811da743df02481bca1a7bdaa73b809913,citation,http://research.sabanciuniv.edu/608/1/3011800001159.pdf,Multimodal person recognition for human-vehicle interaction,2006 -77,FERET,feret,50.7338124,7.1022465,University of Bonn,edu,f4aafb50c93c5ad3e5c4696ed24b063a1932915a,citation,http://pdfs.semanticscholar.org/f4aa/fb50c93c5ad3e5c4696ed24b063a1932915a.pdf,What would you look like in Springfield? Linear Transformations between High-Dimensional Spaces,2011 -78,FERET,feret,45.7413921,126.62552755,Harbin Institute of Technology,edu,10156890bc53cb6be97bd144a68fde693bf13612,citation,http://pdfs.semanticscholar.org/1015/6890bc53cb6be97bd144a68fde693bf13612.pdf,Face Recognition Using Sparse Representation-Based Classification on K-Nearest Subspace,2013 -79,FERET,feret,45.42580475,-75.68740118,University of Ottawa,edu,16820ccfb626dcdc893cc7735784aed9f63cbb70,citation,http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W12/papers/Azarmehr_Real-Time_Embedded_Age_2015_CVPR_paper.pdf,Real-time embedded age and gender classification in unconstrained video,2015 -80,FERET,feret,42.3504253,-71.10056114,Boston University,edu,966b76acfa75253679b1a82ecc5a68e523f5c0c9,citation,http://pdfs.semanticscholar.org/f204/2494d5666e436f5e96ff5e0cd3b5f5e5485b.pdf,Preference suppression caused by misattribution of task-irrelevant subliminal motion.,2012 -81,FERET,feret,40.8419836,-73.94368971,Columbia University,edu,0c7f27d23a162d4f3896325d147f412c40160b52,citation,http://pdfs.semanticscholar.org/0c7f/27d23a162d4f3896325d147f412c40160b52.pdf,Models and Algorithms for Vision through the Atmosphere,2003 -82,FERET,feret,40.47913175,-74.43168868,Rutgers University,edu,6069b4bc1a21341b77b49f01341c238c770d52e0,citation,http://pdfs.semanticscholar.org/b02b/50ed995fe526208b1577b9d7ef6262bf3ecf.pdf,Comparing Kernel-based Learning Methods for Face Recognition,2003 -83,FERET,feret,51.49887085,-0.17560797,Imperial College London,edu,af31ef1e81c1132f186d7aebb141d7f59a815010,citation,http://cas.ee.ic.ac.uk/people/ccb98/papers/LiuGlobalSIP13.pdf,Domain-specific progressive sampling of face images,2013 -84,FERET,feret,51.5073219,-0.1276474,"London, United Kingdom",edu,af31ef1e81c1132f186d7aebb141d7f59a815010,citation,http://cas.ee.ic.ac.uk/people/ccb98/papers/LiuGlobalSIP13.pdf,Domain-specific progressive sampling of face images,2013 -85,FERET,feret,39.1254938,-77.22293475,National Institute of Standards and Technology,edu,07f31bef7a7035792e3791473b3c58d03928abbf,citation,https://doi.org/10.1016/j.imavis.2016.08.004,Lessons from collecting a million biometric samples,2015 -86,FERET,feret,41.70456775,-86.23822026,University of Notre Dame,edu,07f31bef7a7035792e3791473b3c58d03928abbf,citation,https://doi.org/10.1016/j.imavis.2016.08.004,Lessons from collecting a million biometric samples,2015 -87,FERET,feret,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,d275714c323dd4e400e8003fa8c33070f8ea03d1,citation,https://pdfs.semanticscholar.org/d275/714c323dd4e400e8003fa8c33070f8ea03d1.pdf,"White Fear, Dehumanization and Low Empathy: a Lethal Combination for Shooting Biases by Yara Mekawi",2014 -88,FERET,feret,40.0044795,116.370238,Chinese Academy of Sciences,edu,1a5a79b4937b89420049bc279a7b7f765d143881,citation,http://pdfs.semanticscholar.org/1a5a/79b4937b89420049bc279a7b7f765d143881.pdf,Are Rich People Perceived as More Trustworthy? Perceived Socioeconomic Status Modulates Judgments of Trustworthiness and Trust Behavior Based on Facial Appearance,2018 -89,FERET,feret,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,1a5a79b4937b89420049bc279a7b7f765d143881,citation,http://pdfs.semanticscholar.org/1a5a/79b4937b89420049bc279a7b7f765d143881.pdf,Are Rich People Perceived as More Trustworthy? Perceived Socioeconomic Status Modulates Judgments of Trustworthiness and Trust Behavior Based on Facial Appearance,2018 -90,FERET,feret,37.548215,-77.45306424,Virginia Commonwealth University,edu,1a5a79b4937b89420049bc279a7b7f765d143881,citation,http://pdfs.semanticscholar.org/1a5a/79b4937b89420049bc279a7b7f765d143881.pdf,Are Rich People Perceived as More Trustworthy? Perceived Socioeconomic Status Modulates Judgments of Trustworthiness and Trust Behavior Based on Facial Appearance,2018 -91,FERET,feret,39.1254938,-77.22293475,National Institute of Standards and Technology,edu,88ee6d0b8342852a5bd55864dc7a1c8452c10bbf,citation,http://pdfs.semanticscholar.org/88ee/6d0b8342852a5bd55864dc7a1c8452c10bbf.pdf,Support Vector Machines Applied to Face Recognition,1998 -92,FERET,feret,32.0565957,118.77408833,Nanjing University,edu,59f83e94a7f52cbb728d434426f6fe85f756259c,citation,https://pdfs.semanticscholar.org/59f8/3e94a7f52cbb728d434426f6fe85f756259c.pdf,An Improved Illumination Normalization Approach based on Wavelet Tranform for Face Recognition from Single Training Image Per Person,2010 -93,FERET,feret,40.0044795,116.370238,Chinese Academy of Sciences,edu,5d1c4e93e32ee686234c5aae7f38025523993c8c,citation,http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/IEEE_CVPR2013/data/Papers/4989d539.pdf,Towards Pose Robust Face Recognition,2013 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University,edu,45a3ba54fc2210cf8a4fba0cbdce9dad3cefc826,citation,http://pdfs.semanticscholar.org/45a3/ba54fc2210cf8a4fba0cbdce9dad3cefc826.pdf,Complete Cross-Validation for Nearest Neighbor Classifiers,2000 -98,FERET,feret,51.24303255,-0.59001382,University of Surrey,edu,71e942e05f73b163a7ec814a85ff4131cb48f650,citation,http://pdfs.semanticscholar.org/8f83/e1a0c05da3a2f316b75b4a178fadf709dd68.pdf,The BANCA Database and Evaluation Protocol,2003 -99,FERET,feret,32.0565957,118.77408833,Nanjing University,edu,1fe0c5562c8dffecc0cadeef2c592bfa6e89b5ca,citation,http://cs.boisestate.edu/~dxu/publications/ICTAI04.pdf,Illumination invariant face recognition based on neural network ensemble,2004 -100,FERET,feret,46.897155,-96.81827603,North Dakota State University,edu,1fe0c5562c8dffecc0cadeef2c592bfa6e89b5ca,citation,http://cs.boisestate.edu/~dxu/publications/ICTAI04.pdf,Illumination invariant face recognition based on neural network ensemble,2004 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marks",2009 -105,FERET,feret,-34.40505545,150.87834655,University of Wollongong,edu,a3bc6020cd57ebe3a82a0b232f969bcc4e372e53,citation,http://pdfs.semanticscholar.org/a3bc/6020cd57ebe3a82a0b232f969bcc4e372e53.pdf,A Hybrid Feature Extraction Technique for Face Recognition,2014 -106,FERET,feret,40.0044795,116.370238,Chinese Academy of Sciences,edu,13d591220f9fdb22d81c2438a008c80843b61fd4,citation,https://pdfs.semanticscholar.org/13d5/91220f9fdb22d81c2438a008c80843b61fd4.pdf,Boosting Multi-gabor Subspaces for Face Recognition,2006 -107,FERET,feret,22.42031295,114.20788644,Chinese University of Hong Kong,edu,13d591220f9fdb22d81c2438a008c80843b61fd4,citation,https://pdfs.semanticscholar.org/13d5/91220f9fdb22d81c2438a008c80843b61fd4.pdf,Boosting Multi-gabor Subspaces for Face Recognition,2006 -108,FERET,feret,-27.49741805,153.01316956,University of Queensland,edu,621e8882c41cdaf03a2c4a986a6404f0272ba511,citation,https://doi.org/10.1109/IJCNN.2012.6252611,On robust biometric identity verification via sparse encoding of faces: Holistic vs local approaches,2012 -109,FERET,feret,52.2380139,6.8566761,University of Twente,edu,8780f14d04671d4f2ed50307d16062d72cc51863,citation,http://pdfs.semanticscholar.org/8780/f14d04671d4f2ed50307d16062d72cc51863.pdf,Likelihood Ratio-Based Detection of Facial Features,2000 -110,FERET,feret,43.66333345,-79.39769975,University of Toronto,edu,7a52eb0886892c04c6c80b78795d880a70796cb6,citation,http://www.cs.toronto.edu/~jepson/papers/ChennubhotlaJepsonICPR2004.pdf,Perceptual distance normalization for appearance detection,2004 -111,FERET,feret,65.0592157,25.46632601,University of Oulu,edu,1fe121925668743762ce9f6e157081e087171f4c,citation,https://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W02/papers/Ylioinas_Unsupervised_Learning_of_2015_CVPR_paper.pdf,Unsupervised learning of overcomplete face descriptors,2015 -112,FERET,feret,29.5084174,106.57858552,Chongqing University,edu,f3cb97791ded4a5c3bed717f820215a1c9648226,citation,http://pdfs.semanticscholar.org/f3cb/97791ded4a5c3bed717f820215a1c9648226.pdf,Multi-scale Block Weber Local Descriptor for Face Recognition,2015 -113,FERET,feret,38.83133325,-77.30798839,George Mason University,edu,d28d697b578867500632b35b1b19d3d76698f4a9,citation,http://pdfs.semanticscholar.org/d28d/697b578867500632b35b1b19d3d76698f4a9.pdf,Face Recognition Using Shape and Texture,1999 -114,FERET,feret,58.38131405,26.72078081,University of Tartu,edu,5a5ae31263517355d15b7b09d74cb03e40093046,citation,http://pdfs.semanticscholar.org/5a5a/e31263517355d15b7b09d74cb03e40093046.pdf,Super Resolution and Face Recognition Based People Activity Monitoring Enhancement Using Surveillance Camera,2016 -115,FERET,feret,32.0565957,118.77408833,Nanjing University,edu,82524c49ea20390c711e0606e50570ac2183c281,citation,http://pdfs.semanticscholar.org/8252/4c49ea20390c711e0606e50570ac2183c281.pdf,(2D)PCA: 2-Directional 2-Dimensional PCA for Efficient Face Representation and Recognition,2005 -116,FERET,feret,38.99203005,-76.9461029,University of Maryland College Park,edu,b13a882e6168afc4058fe14cc075c7e41434f43e,citation,http://pdfs.semanticscholar.org/b13a/882e6168afc4058fe14cc075c7e41434f43e.pdf,Recognition of Humans and Their Activities Using Video,2005 -117,FERET,feret,39.2899685,-76.62196103,University of Maryland,edu,b13a882e6168afc4058fe14cc075c7e41434f43e,citation,http://pdfs.semanticscholar.org/b13a/882e6168afc4058fe14cc075c7e41434f43e.pdf,Recognition of Humans and Their Activities Using Video,2005 -118,FERET,feret,32.9820799,-96.7566278,University of Texas at Dallas,edu,ac9516a589901f1421e8ce905dd8bc5b689317ca,citation,http://pdfs.semanticscholar.org/ac95/16a589901f1421e8ce905dd8bc5b689317ca.pdf,A Practical Framework for Executing Complex Queries over Encrypted Multimedia Data,2016 -119,FERET,feret,42.357757,-83.06286711,Wayne State 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Machines (SVM),1998 -147,FERET,feret,51.4584837,-2.6097752,University of Bristol,edu,a632ebe6f1e7d9b2b652b0186abef8db218037f3,citation,http://pdfs.semanticscholar.org/a632/ebe6f1e7d9b2b652b0186abef8db218037f3.pdf,Subliminally and Supraliminally Acquired Long-Term Memories Jointly Bias Delayed Decisions,2017 -148,FERET,feret,32.8536333,-117.2035286,Kyung Hee University,edu,027f769aed0cfcb3169ef60f182ce1decc0e99eb,citation,http://www.ijicic.org/10-12018-1.pdf,Local Directional Pattern (LDP) for face recognition,2010 -149,FERET,feret,32.0565957,118.77408833,Nanjing University,edu,edd6ed94207ab614c71ac0591d304a708d708e7b,citation,http://doi.org/10.1016/j.neucom.2012.02.001,Reconstructive discriminant analysis: A feature extraction method induced from linear regression classification,2012 -150,FERET,feret,42.718568,-84.47791571,Michigan State University,edu,5dbf772b98cb944befa9cf01ec5d15da713a338b,citation,http://pdfs.semanticscholar.org/9d82/44d5a32ecc314860c1d673d687df28f77d84.pdf,Face 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non-rigid 3D tracking for face recognition in real-world videos,2011 -175,FERET,feret,43.7743911,-79.50481085,York University,edu,ffe4bb47ec15f768e1744bdf530d5796ba56cfc1,citation,https://arxiv.org/pdf/1706.04277.pdf,AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces,2017 -176,FERET,feret,27.18794105,31.17009498,Assiut University,edu,ffe4bb47ec15f768e1744bdf530d5796ba56cfc1,citation,https://arxiv.org/pdf/1706.04277.pdf,AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces,2017 -177,FERET,feret,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,cd6aaa37fffd0b5c2320f386be322b8adaa1cc68,citation,https://arxiv.org/pdf/1804.06655.pdf,Deep Face Recognition: A Survey,2018 -178,FERET,feret,32.0565957,118.77408833,Nanjing 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Network,2017 -229,FERET,feret,31.9078499,34.81334092,Weizmann Institute of Science,edu,4cb8a691a15e050756640c0a35880cdd418e2b87,citation,http://www.vision.caltech.edu/~bart/Publications/2004/BartUllmanClassBasedMatching.pdf,Class-Based Matching of Object Parts,2004 -230,FERET,feret,39.4808376,-0.3409522,Universitat Politècnica de València,edu,99b8a24aacaa53fa3f8a7e48734037c7b16f1c40,citation,https://doi.org/10.1109/ACCESS.2017.2752176,A Proposal to Improve the Authentication Process in m-Health Environments,2017 -231,FERET,feret,32.7283683,-97.11201835,University of Texas at Arlington,edu,c2fa83e8a428c03c74148d91f60468089b80c328,citation,http://pdfs.semanticscholar.org/c2fa/83e8a428c03c74148d91f60468089b80c328.pdf,Optimal Mean Robust Principal Component Analysis,2014 -232,FERET,feret,28.0599999,-82.41383619,University of South Florida,edu,1b3e66bef13f114943d460b4f942e941b4761ba2,citation,http://www.nist.gov/customcf/get_pdf.cfm?pub_id=890061,Subspace Approximation of Face Recognition Algorithms: An Empirical Study,2008 -233,FERET,feret,39.1254938,-77.22293475,National Institute of Standards and Technology,edu,1b3e66bef13f114943d460b4f942e941b4761ba2,citation,http://www.nist.gov/customcf/get_pdf.cfm?pub_id=890061,Subspace Approximation of Face Recognition Algorithms: An Empirical Study,2008 -234,FERET,feret,28.0599999,-82.41383619,University of South Florida,edu,bdc3546ceee0c2bda9debff7de9aa7d53a03fe7d,citation,https://pdfs.semanticscholar.org/bdc3/546ceee0c2bda9debff7de9aa7d53a03fe7d.pdf,Modeling distance functions induced by face recognition algorithms,2015 -235,FERET,feret,37.4102193,-122.05965487,Carnegie Mellon University,edu,0fbe38527279f49561c0e1c6ff4e8f733fb79bbe,citation,http://pdfs.semanticscholar.org/7561/b691eb5e9913e4c3cb11caf2738d58b9c896.pdf,Integrating Utility into Face De-identification,2005 -236,FERET,feret,43.66333345,-79.39769975,University of Toronto,edu,90ea3a35e946af97372c3f32a170b179fe8352aa,citation,http://pdfs.semanticscholar.org/90ea/3a35e946af97372c3f32a170b179fe8352aa.pdf,Discriminant Learning for Face Recognition,2004 -237,FERET,feret,35.93006535,-84.31240032,Oak Ridge National Laboratory,edu,43a03cbe8b704f31046a5aba05153eb3d6de4142,citation,http://pdfs.semanticscholar.org/9594/3329cd6922a869dd6d58ef01e9492879034c.pdf,Towards Robust Face Recognition from Video,2001 -238,FERET,feret,37.43131385,-122.16936535,Stanford University,edu,cdd2ba6e6436cb5950692702053195a22789d129,citation,https://pdfs.semanticscholar.org/976c/3b5ad438fb0cf2fb157964e8e6f07a09ad9e.pdf,Face-likeness and image variability drive responses in human face-selective ventral regions.,2012 -239,FERET,feret,31.76909325,117.17795091,Anhui University,edu,b910590a0eb191d03e1aedb3d55c905129e92e6b,citation,http://doi.acm.org/10.1145/2808492.2808570,Robust gender classification on unconstrained face images,2015 -240,FERET,feret,40.0044795,116.370238,Chinese Academy of Sciences,edu,b910590a0eb191d03e1aedb3d55c905129e92e6b,citation,http://doi.acm.org/10.1145/2808492.2808570,Robust gender classification on unconstrained face images,2015 -241,FERET,feret,43.66333345,-79.39769975,University of Toronto,edu,dc4089294cb15e071893d24bdf2baa15de5dcb0b,citation,http://www.comm.toronto.edu/~kostas/Publications2008/pub/proceed/105.pdf,Feature selection for subject identification in surveillance photos [face recognition applications],2004 -242,FERET,feret,-33.3578899,151.37834708,University of Newcastle,edu,a80d057099a6ca872508f5d416a8cd67b788506a,citation,https://pdfs.semanticscholar.org/a80d/057099a6ca872508f5d416a8cd67b788506a.pdf,A dissociation between similarity effects in episodic face recognition.,2009 -243,FERET,feret,44.97308605,-93.23708813,University of Minnesota,edu,998cdde7c83a50f0abac69c7c3d20f3729a65d00,citation,https://pdfs.semanticscholar.org/998c/dde7c83a50f0abac69c7c3d20f3729a65d00.pdf,Redundancy effects in the perception and memory of visual objects,2010 -244,FERET,feret,34.66869155,-82.83743476,Clemson University,edu,56c273538a2dbb4cf43c39fa4725592e97ec1681,citation,http://pdfs.semanticscholar.org/56c2/73538a2dbb4cf43c39fa4725592e97ec1681.pdf,Eye Tracking to Enhance Facial Recognition Algorithms,2011 -245,FERET,feret,25.7173339,-80.27866887,University of Miami,edu,c1f07ec629be1c6fe562af0e34b04c54e238dcd1,citation,http://pdfs.semanticscholar.org/c1f0/7ec629be1c6fe562af0e34b04c54e238dcd1.pdf,A Novel Facial Feature Localization Method Using Probabilistic-like Output,2004 -246,FERET,feret,37.5600406,126.9369248,Yonsei University,edu,5173a20304ea7baa6bfe97944a5c7a69ea72530f,citation,http://pdfs.semanticscholar.org/5173/a20304ea7baa6bfe97944a5c7a69ea72530f.pdf,Best Basis Selection Method Using Learning Weights for Face Recognition,2013 -247,FERET,feret,40.00229045,116.32098908,Tsinghua University,edu,83e893858d6a6b8abb07d89e9f821f90c2b074ea,citation,http://doi.ieeecomputersociety.org/10.1109/ICPR.2004.1334677,Facial image retrieval based on demographic classification,2004 -248,FERET,feret,34.0224149,-118.28634407,University of Southern California,edu,2d8a84a8e661ce3913cb6c05b18984b14ed11dac,citation,http://pdfs.semanticscholar.org/6fd6/af3864fc5eb62e6328be79bf8174e939efcc.pdf,P3: Toward Privacy-Preserving Photo Sharing,2013 -249,FERET,feret,40.34829285,-74.66308325,Princeton University,edu,643d11703569766bed0a994941ae5f7b3e101659,citation,https://arxiv.org/pdf/1806.06098.pdf,Unsupervised Training for 3D Morphable Model Regression,2018 -250,FERET,feret,42.3619407,-71.0904378,MIT CSAIL,edu,643d11703569766bed0a994941ae5f7b3e101659,citation,https://arxiv.org/pdf/1806.06098.pdf,Unsupervised Training for 3D Morphable Model Regression,2018 -251,FERET,feret,-29.8674219,30.9807272,University of KwaZulu-Natal,edu,fcfb48b19f37e531a56ae95186a214b05c0b94c7,citation,https://pdfs.semanticscholar.org/fcfb/48b19f37e531a56ae95186a214b05c0b94c7.pdf,FACE RECOGNITION WITH EIGENFACES – A DETAILED STUDY,2012 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Deep Learning Based Representation for Face Recognition,2016 -285,FERET,feret,40.8927159,29.37863323,Sabanci University,edu,d3d5d86afec84c0713ec868cf5ed41661fc96edc,citation,https://arxiv.org/pdf/1606.02894.pdf,A Comprehensive Analysis of Deep Learning Based Representation for Face Recognition,2016 -286,FERET,feret,32.7283683,-97.11201835,University of Texas at Arlington,edu,20100dbeb2dfebc7595d79755d737b21e75f39a6,citation,http://pdfs.semanticscholar.org/2010/0dbeb2dfebc7595d79755d737b21e75f39a6.pdf,Cluster Indicator Decomposition for Efficient Matrix Factorization,2011 -287,FERET,feret,37.4102193,-122.05965487,Carnegie Mellon University,edu,3ca9453d3c023bb81cce72ff2d633fc5075e1df6,citation,http://pdfs.semanticscholar.org/e36f/5fab8758194fcad043e23288330657fe7742.pdf,Generic vs. Person Specific Active Appearance Models,2004 -288,FERET,feret,28.59899755,-81.19712501,University of Central 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Approaches Excel on Face Recognition,2013 -292,FERET,feret,-34.9189226,138.60423668,University of Adelaide,edu,9887ab220254859ffc7354d5189083a87c9bca6e,citation,http://pdfs.semanticscholar.org/9887/ab220254859ffc7354d5189083a87c9bca6e.pdf,Generic Image Classification Approaches Excel on Face Recognition,2013 -293,FERET,feret,58.38131405,26.72078081,University of Tartu,edu,838ed2aae603dec5851ebf5e4bc64b54db7f34be,citation,http://pdfs.semanticscholar.org/838e/d2aae603dec5851ebf5e4bc64b54db7f34be.pdf,Real-Time Ensemble Based Face Recognition System for Humanoid Robots,2016 -294,FERET,feret,32.8536333,-117.2035286,Kyung Hee University,edu,6fe83b5fdeeb6d92f24af3aed6a34c5bf9ce8845,citation,http://pdfs.semanticscholar.org/6fe8/3b5fdeeb6d92f24af3aed6a34c5bf9ce8845.pdf,Face Recognition Based on Local Directional Pattern Variance (LDPv),2012 -295,FERET,feret,23.7289899,90.3982682,Institute of Information Technology,edu,6e177341d4412f9c9a639e33e6096344ef930202,citation,https://pdfs.semanticscholar.org/2e58/ec57d71b2b2a3e71086234dd7037559cc17e.pdf,A Gender Recognition System from Facial Image,2018 -296,FERET,feret,23.7316957,90.3965275,University of Dhaka,edu,6e177341d4412f9c9a639e33e6096344ef930202,citation,https://pdfs.semanticscholar.org/2e58/ec57d71b2b2a3e71086234dd7037559cc17e.pdf,A Gender Recognition System from Facial Image,2018 -297,FERET,feret,40.7423025,-74.17928172,New Jersey Institute of Technology,edu,327eab70296d39511d61e91c6839446d59f5e119,citation,https://pdfs.semanticscholar.org/327e/ab70296d39511d61e91c6839446d59f5e119.pdf,Roadmap for Reliable Ensemble Forecasting of the Sun-Earth System,2018 -298,FERET,feret,21.2982795,-157.8186923,University of Hawaii,edu,327eab70296d39511d61e91c6839446d59f5e119,citation,https://pdfs.semanticscholar.org/327e/ab70296d39511d61e91c6839446d59f5e119.pdf,Roadmap for Reliable Ensemble Forecasting of the Sun-Earth System,2018 -299,FERET,feret,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,327eab70296d39511d61e91c6839446d59f5e119,citation,https://pdfs.semanticscholar.org/327e/ab70296d39511d61e91c6839446d59f5e119.pdf,Roadmap for Reliable Ensemble Forecasting of the Sun-Earth System,2018 -300,FERET,feret,38.83133325,-77.30798839,George Mason University,edu,327eab70296d39511d61e91c6839446d59f5e119,citation,https://pdfs.semanticscholar.org/327e/ab70296d39511d61e91c6839446d59f5e119.pdf,Roadmap for Reliable Ensemble Forecasting of the Sun-Earth System,2018 -301,FERET,feret,34.13710185,-118.12527487,California Institute of Technology,edu,327eab70296d39511d61e91c6839446d59f5e119,citation,https://pdfs.semanticscholar.org/327e/ab70296d39511d61e91c6839446d59f5e119.pdf,Roadmap for Reliable Ensemble Forecasting of the Sun-Earth System,2018 -302,FERET,feret,42.2942142,-83.71003894,University of Michigan,edu,327eab70296d39511d61e91c6839446d59f5e119,citation,https://pdfs.semanticscholar.org/327e/ab70296d39511d61e91c6839446d59f5e119.pdf,Roadmap for Reliable Ensemble Forecasting of the Sun-Earth System,2018 -303,FERET,feret,41.7411504,-111.8122309,Utah State University,edu,327eab70296d39511d61e91c6839446d59f5e119,citation,https://pdfs.semanticscholar.org/327e/ab70296d39511d61e91c6839446d59f5e119.pdf,Roadmap for Reliable Ensemble Forecasting of the Sun-Earth System,2018 -304,FERET,feret,36.3697191,127.362537,Korea Advanced Institute of Science and Technology,edu,b29f348e8675f75ff160ec65ebeeb3f3979b65d8,citation,http://pdfs.semanticscholar.org/b29f/348e8675f75ff160ec65ebeeb3f3979b65d8.pdf,An objective and subjective evaluation of content-based privacy protection of face images in video surveillance systems using JPEG XR,2013 -305,FERET,feret,43.66333345,-79.39769975,University of Toronto,edu,b29f348e8675f75ff160ec65ebeeb3f3979b65d8,citation,http://pdfs.semanticscholar.org/b29f/348e8675f75ff160ec65ebeeb3f3979b65d8.pdf,An objective and subjective evaluation of content-based privacy protection of face images in video surveillance systems using JPEG XR,2013 -306,FERET,feret,39.9922379,116.30393816,Peking University,edu,1c2724243b27a18a2302f12dea79d9a1d4460e35,citation,http://read.pudn.com/downloads157/doc/697237/kfd/Fisher+Kernel%20criterion%20for%20discriminant%20analysis.pdf,Fisher+Kernel criterion for discriminant analysis,2005 -307,FERET,feret,31.83907195,117.26420748,University of Science and Technology of China,edu,1c2724243b27a18a2302f12dea79d9a1d4460e35,citation,http://read.pudn.com/downloads157/doc/697237/kfd/Fisher+Kernel%20criterion%20for%20discriminant%20analysis.pdf,Fisher+Kernel criterion for discriminant analysis,2005 -308,FERET,feret,22.42031295,114.20788644,Chinese University of Hong Kong,edu,1c2724243b27a18a2302f12dea79d9a1d4460e35,citation,http://read.pudn.com/downloads157/doc/697237/kfd/Fisher+Kernel%20criterion%20for%20discriminant%20analysis.pdf,Fisher+Kernel criterion for discriminant analysis,2005 -309,FERET,feret,50.74223495,-1.89433739,Bournemouth University,edu,d16f37a15f6385a6a189b06833745da5d524f69b,citation,https://pdfs.semanticscholar.org/d16f/37a15f6385a6a189b06833745da5d524f69b.pdf,Hebb repetition effects for non-verbal visual sequences: determinants of sequence acquisition.,2017 -310,FERET,feret,40.00229045,116.32098908,Tsinghua University,edu,13791aa7c1047724c4046eee94e66a506b211eb9,citation,http://pdfs.semanticscholar.org/1379/1aa7c1047724c4046eee94e66a506b211eb9.pdf,Real-time Gender Classification,2003 -311,FERET,feret,37.3003127,126.972123,SungKyunKwan University,edu,fa72e39971855dff6beb8174b5fa654e0ab7d324,citation,https://doi.org/10.1007/s11042-013-1793-1,"A depth video-based facial expression recognition system using radon transform, generalized discriminant analysis, and hidden Markov model",2013 -312,FERET,feret,24.7246403,46.62335012,King Saud University,edu,fa72e39971855dff6beb8174b5fa654e0ab7d324,citation,https://doi.org/10.1007/s11042-013-1793-1,"A depth video-based facial expression recognition system using radon transform, generalized discriminant analysis, and hidden Markov model",2013 -313,FERET,feret,27.18794105,31.17009498,Assiut University,edu,3843b8c4143e9f1e50c61eb462376e65861bbf24,citation,http://doi.ieeecomputersociety.org/10.1109/ICCVW.2017.359,Color Image Processing Using Reduced Biquaternions with Application to Face Recognition in a PCA Framework,2017 -314,FERET,feret,37.4102193,-122.05965487,Carnegie Mellon University,edu,0cc3c62f762d64cffcab4ac7fea3896cb22a3df9,citation,http://pdfs.semanticscholar.org/d30f/cc0e4c2c78cc5ff7bbd1227d3952d366a479.pdf,Preserving Privacy by De-identifying Facial Images,2003 -315,FERET,feret,53.8925662,-122.81471592,University of Northern British Columbia,edu,2cae2ca6221fbfa9655e41ac52e54631ada7ad2c,citation,http://pdfs.semanticscholar.org/ffd6/14925a326efcb27ef52accd5638a912b4792.pdf,Electoral College and Direct Popular Vote for Multi-Candidate Election,2010 -316,FERET,feret,34.2249827,-77.86907744,University of North Carolina at Wilmington,edu,328bfd1d0229bc4973277f893abd1eb288159fc9,citation,http://pdfs.semanticscholar.org/328b/fd1d0229bc4973277f893abd1eb288159fc9.pdf,A review of the literature on the aging adult skull and face: implications for forensic science research and applications.,2007 -317,FERET,feret,32.87935255,-117.23110049,"University of California, San Diego",edu,18b4e9e51ee14c9d816358fbe1af29f0771b7916,citation,http://pdfs.semanticscholar.org/18b4/e9e51ee14c9d816358fbe1af29f0771b7916.pdf,Intelligent environments and active camera networks,2000 -318,FERET,feret,40.8722825,-73.89489171,City University of New York,edu,0dde6981047067692793b71a2f7ad6a8708741d8,citation,http://pdfs.semanticscholar.org/0dde/6981047067692793b71a2f7ad6a8708741d8.pdf,MODELING PHYSICAL PERSONALITIES FOR VIRTUAL AGENTS BY MODELING TRAIT IMPRESSIONS OF THE FACE: A NEURAL NETWORK ANALYSIS by SHERYL BRAHNAM,2002 -319,FERET,feret,45.7413921,126.62552755,Harbin Institute of Technology,edu,20675281008211641d28ce0f2b6946537a8535c4,citation,http://pdfs.semanticscholar.org/2067/5281008211641d28ce0f2b6946537a8535c4.pdf,Multi-resolution Histograms of Local Variation Patterns (MHLVP) for Robust Face Recognition,2005 -320,FERET,feret,52.9387428,-1.20029569,University of Nottingham,edu,c22df6df55f5c6539e1a4d2e2d50dbaab34007a7,citation,http://pdfs.semanticscholar.org/c22d/f6df55f5c6539e1a4d2e2d50dbaab34007a7.pdf,Compact Binary Patterns (CBP) with Multiple Patch Classifiers for Fast and Accurate Face Recognition,2010 -321,FERET,feret,32.87935255,-117.23110049,"University of California, San Diego",edu,2e6e335e591da1e8899ff53f9a7ddb4c63520104,citation,http://pdfs.semanticscholar.org/528a/6698911ff30aa648af4d0a5cf0dd9ee90b5c.pdf,Is All Face Processing Holistic? The View from UCSD,2003 -322,FERET,feret,41.6659,-91.57310307,University of Iowa,edu,2e6e335e591da1e8899ff53f9a7ddb4c63520104,citation,http://pdfs.semanticscholar.org/528a/6698911ff30aa648af4d0a5cf0dd9ee90b5c.pdf,Is All Face Processing Holistic? The View from UCSD,2003 -323,FERET,feret,42.57054745,-88.55578627,University of Geneva,edu,9c1b132243e0dcacde1717ce1cfe730a74bd8cbc,citation,http://pdfs.semanticscholar.org/9c1b/132243e0dcacde1717ce1cfe730a74bd8cbc.pdf,Hippocampus Is Place of Interaction between Unconscious and Conscious Memories,2015 -324,FERET,feret,1.2962018,103.77689944,National University of Singapore,edu,4fb9f05dc03eb4983d8f9a815745bb47970f1b93,citation,http://pdfs.semanticscholar.org/f4ee/4f7ac7585f7ea0db3b27c5ad016dbfb0feac.pdf,"On Robust Face Recognition via Sparse Encoding: the Good, the Bad, and the Ugly",2013 -325,FERET,feret,-27.49741805,153.01316956,University of Queensland,edu,4fb9f05dc03eb4983d8f9a815745bb47970f1b93,citation,http://pdfs.semanticscholar.org/f4ee/4f7ac7585f7ea0db3b27c5ad016dbfb0feac.pdf,"On Robust Face Recognition via Sparse Encoding: the Good, the Bad, and the Ugly",2013 -326,FERET,feret,-27.47715625,153.02841004,Queensland University of Technology,edu,4fb9f05dc03eb4983d8f9a815745bb47970f1b93,citation,http://pdfs.semanticscholar.org/f4ee/4f7ac7585f7ea0db3b27c5ad016dbfb0feac.pdf,"On Robust Face Recognition via Sparse Encoding: the Good, the Bad, and the Ugly",2013 -327,FERET,feret,52.9387428,-1.20029569,University of Nottingham,edu,b9df25cc4be2f703b059da93823bad6e8e8c0659,citation,http://pdfs.semanticscholar.org/b9df/25cc4be2f703b059da93823bad6e8e8c0659.pdf,Local Gabor Binary Pattern Whitened PCA: A Novel Approach for Face Recognition from Single Image Per Person,2009 -328,FERET,feret,24.4399419,118.09301781,Xiamen University,edu,57ba4b6de23a6fc9d45ff052ed2563e5de00b968,citation,https://doi.org/10.1109/ICIP.2017.8296993,An efficient deep neural networks training framework for robust face recognition,2017 -329,FERET,feret,32.7283683,-97.11201835,University of Texas at Arlington,edu,90bd16caa44086db6f0e4bbc1dde7063cb71b7b8,citation,http://www.kdd.org/kdd2016/papers/files/rfp1162-wangA.pdf,Structured Doubly Stochastic Matrix for Graph Based Clustering: Structured Doubly Stochastic Matrix,2016 -330,FERET,feret,1.2962018,103.77689944,National University of Singapore,edu,15d1582c8b65dbab5ca027467718a2c286ddce7a,citation,http://pdfs.semanticscholar.org/15d1/582c8b65dbab5ca027467718a2c286ddce7a.pdf,"On robust face recognition via sparse coding: the good, the bad and the ugly",2014 -331,FERET,feret,-27.49741805,153.01316956,University of Queensland,edu,15d1582c8b65dbab5ca027467718a2c286ddce7a,citation,http://pdfs.semanticscholar.org/15d1/582c8b65dbab5ca027467718a2c286ddce7a.pdf,"On robust face recognition via sparse coding: the good, the bad and the ugly",2014 -332,FERET,feret,-27.47715625,153.02841004,Queensland University of Technology,edu,15d1582c8b65dbab5ca027467718a2c286ddce7a,citation,http://pdfs.semanticscholar.org/15d1/582c8b65dbab5ca027467718a2c286ddce7a.pdf,"On robust face recognition via sparse coding: the good, the bad and the ugly",2014 -333,FERET,feret,34.8452999,48.5596212,Islamic Azad University,edu,e19a4dadf60848309c8fd7445d97918da654df76,citation,https://pdfs.semanticscholar.org/e19a/4dadf60848309c8fd7445d97918da654df76.pdf,JPEG Compressed Domain Face Recognition : Different Stages and Different Features,2013 -334,FERET,feret,47.5612651,7.5752961,University of Basel,edu,d1633dc3706580c8b9d98c4c0dfa9f9a29360ca3,citation,https://arxiv.org/pdf/1712.01619.pdf,Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systems,2018 -335,FERET,feret,51.49887085,-0.17560797,Imperial College London,edu,e104e213faa97d9a9c8b8e1f15b7431c601cb250,citation,https://arxiv.org/pdf/1802.04636.pdf,Modeling of facial aging and kinship: A survey,2018 -336,FERET,feret,51.59029705,-0.22963221,Middlesex University,edu,e104e213faa97d9a9c8b8e1f15b7431c601cb250,citation,https://arxiv.org/pdf/1802.04636.pdf,Modeling of facial aging and kinship: A survey,2018 -337,FERET,feret,43.66333345,-79.39769975,University of Toronto,edu,da6696345d0d4ff6328c1c5916b0ca870d4cc6cf,citation,http://pdfs.semanticscholar.org/da66/96345d0d4ff6328c1c5916b0ca870d4cc6cf.pdf,Robust Contrast-Invariant EigenDetection,2002 -338,FERET,feret,52.2380139,6.8566761,University of Twente,edu,3b3550680136aa2fe3bd57c9faa3bfa0dfb3e748,citation,http://pdfs.semanticscholar.org/3b35/50680136aa2fe3bd57c9faa3bfa0dfb3e748.pdf,Forensic Face Recognition: a Survey,2010 -339,FERET,feret,31.30104395,121.50045497,Fudan University,edu,4ba3f9792954ee3ba894e1e330cd77da4668fa22,citation,http://pdfs.semanticscholar.org/4ba3/f9792954ee3ba894e1e330cd77da4668fa22.pdf,Nearest Neighbor Discriminant Analysis,2006 -340,FERET,feret,52.9387428,-1.20029569,University of Nottingham,edu,472ba8dd4ec72b34e85e733bccebb115811fd726,citation,http://pdfs.semanticscholar.org/472b/a8dd4ec72b34e85e733bccebb115811fd726.pdf,Cosine Similarity Metric Learning for Face Verification,2010 -341,FERET,feret,46.109237,7.08453549,IDIAP Research Institute,edu,ba9e967208976f24a09730af94086e7ae0417067,citation,http://pdfs.semanticscholar.org/f369/03d22a463876b895bbe37b5f9ad235a38edd.pdf,An Open Source Framework for Standardized Comparisons of Face Recognition Algorithms,2012 -342,FERET,feret,40.4319722,-86.92389368,Purdue University,edu,4d527974512083712c9adf26a923b44d7e426b44,citation,http://pdfs.semanticscholar.org/4d52/7974512083712c9adf26a923b44d7e426b44.pdf,Impact of Image Quality on Performance: Comparison of Young and Elderly Fingerprints,2006 -343,FERET,feret,-38.19928505,144.30365229,Deakin University,edu,e96ce25d11296fce4e2ecc2da03bd207dc118724,citation,https://doi.org/10.1007/s00138-007-0095-x,Classification of face images using local iterated function systems,2007 -344,FERET,feret,42.0551164,-87.67581113,Northwestern University,edu,fcd2fb1ada96218dcc2547efa040e76416cc7066,citation,http://pdfs.semanticscholar.org/fcd2/fb1ada96218dcc2547efa040e76416cc7066.pdf,Perceptual data mining: bootstrapping visual intelligence from tracking behavior,2002 -345,FERET,feret,42.3583961,-71.09567788,MIT,edu,fcd2fb1ada96218dcc2547efa040e76416cc7066,citation,http://pdfs.semanticscholar.org/fcd2/fb1ada96218dcc2547efa040e76416cc7066.pdf,Perceptual data mining: bootstrapping visual intelligence from tracking behavior,2002 -346,FERET,feret,37.3351908,-121.88126008,San Jose State University,edu,97930609f1a5066fd437ed8a4e57abbfb1ae4b12,citation,http://pdfs.semanticscholar.org/bef4/03c136beaa6fd43fc3184d4666512daaf9e5.pdf,Best Practices in Testing and Reporting Performance of Biometric Devices,2002 -347,FERET,feret,47.5612651,7.5752961,University of Basel,edu,985dc9b8b003483f6df363a8ce07dd8c89ced903,citation,http://pdfs.semanticscholar.org/985d/c9b8b003483f6df363a8ce07dd8c89ced903.pdf,"3D Morphable Face Model, a Unified Approach for Analysis and Synthesis of Images",0 -348,FERET,feret,34.2375581,-77.9270129,University of North Carolina Wilmington,edu,1057137d8ebbbfc4e816d74edd7ab04f61a893f8,citation,https://pdfs.semanticscholar.org/1057/137d8ebbbfc4e816d74edd7ab04f61a893f8.pdf,Craniofacial Aging,2008 -349,FERET,feret,37.548215,-77.45306424,Virginia Commonwealth University,edu,1057137d8ebbbfc4e816d74edd7ab04f61a893f8,citation,https://pdfs.semanticscholar.org/1057/137d8ebbbfc4e816d74edd7ab04f61a893f8.pdf,Craniofacial Aging,2008 -350,FERET,feret,37.4102193,-122.05965487,Carnegie Mellon University,edu,005d818ff8517669d62ba7b536e76b56698fa135,citation,http://pdfs.semanticscholar.org/4d7e/e94f164cce28a8bfef4417e9a99265b02b54.pdf,Neural Network-Based Face Detection,1996 -351,FERET,feret,39.9492344,-75.19198985,University of Pennsylvania,edu,0c85d1b384bb6e2d5d6e4db5461a7101ceed6808,citation,http://pdfs.semanticscholar.org/0ff8/d39a962ed902e1c995815ade265ea903d218.pdf,Engineering Privacy in Public: Confounding Face Recognition,2003 -352,FERET,feret,37.21872455,-80.42542519,Virginia Polytechnic Institute and State University,edu,9107543d9a9d915c92fe4139932c5d818cfc187d,citation,http://pdfs.semanticscholar.org/9107/543d9a9d915c92fe4139932c5d818cfc187d.pdf,Investigation of New Techniques for Face Detection,2007 -353,FERET,feret,37.4102193,-122.05965487,Carnegie Mellon University,edu,b3e856729f89b082b4108561479ff09394bb6553,citation,http://pdfs.semanticscholar.org/b3e8/56729f89b082b4108561479ff09394bb6553.pdf,Pose Robust Video - Based Face Recognition,2004 -354,FERET,feret,34.0224149,-118.28634407,University of Southern California,edu,d1836e137787fadb28d3418e029534765bcf1dae,citation,http://pdfs.semanticscholar.org/d183/6e137787fadb28d3418e029534765bcf1dae.pdf,"Analysis , Synthesis and Recognition of Human Faces with Pose Variations",2001 -355,FERET,feret,37.4102193,-122.05965487,Carnegie Mellon University,edu,2fd1c99edbb3d22cec4adc9ba9319cfc2360e903,citation,http://pdfs.semanticscholar.org/98c8/ca05ed5baff5b217c571ab5c5a0ee0706e27.pdf,Rotation Invariant Neural Network-Based Face Detection,1998 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University,edu,42fe5666599f35b805657e829e8f9093ee95b908,citation,http://pdfs.semanticscholar.org/42fe/5666599f35b805657e829e8f9093ee95b908.pdf,Pose-Tolerant Face Recognition,2015 -360,FERET,feret,42.3583961,-71.09567788,MIT,edu,29c7dfbbba7a74e9aafb6a6919629b0a7f576530,citation,http://pdfs.semanticscholar.org/29c7/dfbbba7a74e9aafb6a6919629b0a7f576530.pdf,Automatic Facial Expression Analysis and Emotional Classification,2004 -361,FERET,feret,34.0224149,-118.28634407,University of Southern California,edu,f6a65be9a3790e8fd3b5116450a47a8e48a54d63,citation,http://pdfs.semanticscholar.org/f6a6/5be9a3790e8fd3b5116450a47a8e48a54d63.pdf,Parametric Piecewise Linear Subspace Method for Processing Facial Images with 3D Pose Variations,0 -362,FERET,feret,38.8964679,-104.8050594,University of Colorado at Colorado Springs,edu,07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1,citation,http://www.vast.uccs.edu/~tboult/PAPERS/BTAS13-Sapkota-Boult-UCCSFaceDB.pdf,Large scale unconstrained open set face 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Technology,edu,ca458f189c1167e42d3a5aaf81efc92a4c008976,citation,https://doi.org/10.1109/TIP.2012.2202678,Double Shrinking Sparse Dimension Reduction,2013 -374,FERET,feret,35.14479945,33.90492318,Eastern Mediterranean University,edu,b20a8fc556aed9ab798fcf31e4f971dbc67a9edf,citation,http://pdfs.semanticscholar.org/b20a/8fc556aed9ab798fcf31e4f971dbc67a9edf.pdf,An Adept Segmentation Algorithm and Its Application to the Extraction of Local Regions Containing Fiducial Points,2006 -375,FERET,feret,51.0784038,-114.1287077,University of Calgary,edu,80290f2a38741e20a38de7c00d80353604343ef8,citation,http://pdfs.semanticscholar.org/8029/0f2a38741e20a38de7c00d80353604343ef8.pdf,Eigenfeature Optimization for Face Detection,2004 -376,FERET,feret,22.304572,114.17976285,Hong Kong Polytechnic University,edu,4a24d41aef0041ef82916d2316eea86f6c45c47f,citation,http://pdfs.semanticscholar.org/4a24/d41aef0041ef82916d2316eea86f6c45c47f.pdf,Impact of Full Rank Principal Component Analysis on Classification 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Ottawa,edu,a94cae786d515d3450d48267e12ca954aab791c4,citation,http://www.site.uottawa.ca/~shervin/pubs/CogniVue-Dataset-ACM-MMSys2014.pdf,YawDD: a yawning detection dataset,2014 -405,FERET,feret,34.7361066,10.7427275,"University of Sfax, Tunisia",edu,8a3bb63925ac2cdf7f9ecf43f71d65e210416e17,citation,https://www.math.uh.edu/~dlabate/ShearFace_ICPR2014.pdf,ShearFace: Efficient Extraction of Anisotropic Features for Face Recognition,2014 -406,FERET,feret,16.46007565,102.81211798,Khon Kaen University,edu,31dd6bafd6e7c6095eb8d0591abac3b0106a75e3,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8457336,Face Recognition In Unconstrained Environment,2018 -407,FERET,feret,51.24303255,-0.59001382,University of Surrey,edu,beea33ccd9423d48d6cfb928469bbe7841e63e73,citation,http://pdfs.semanticscholar.org/beea/33ccd9423d48d6cfb928469bbe7841e63e73.pdf,DIARETDB1 diabetic retinopathy database and evaluation protocol,2007 -408,FERET,feret,22.3386304,114.2620337,Hong Kong University of Science and Technology,edu,4cf0c6d3da8e20d6f184a4eaa6865d61680982b8,citation,http://pdfs.semanticscholar.org/4cf0/c6d3da8e20d6f184a4eaa6865d61680982b8.pdf,Face recognition based on 3D mesh model,2004 -409,FERET,feret,-33.95828745,18.45997349,University of Cape Town,edu,ba6082291b018b14f8da4f96afc631918bad3a1b,citation,https://pdfs.semanticscholar.org/3f5b/0cf2ed392045026ea0d1d67145d0400e516f.pdf,"Calibration , Recognition , and Shape from Silhouettes of Stones",2007 -410,FERET,feret,39.1254938,-77.22293475,National Institute of Standards and Technology,edu,b1e218046a28d10ec0be3272809608dea378eddc,citation,https://pdfs.semanticscholar.org/12c5/66e2eee7bbaf45b894e7282f87f00f1db20a.pdf,Overview of the Multiple Biometrics Grand Challenge,2009 -411,FERET,feret,39.9922379,116.30393816,Peking University,edu,15122ef718265beb4cb1a74e5d1f41c5edcb4ba5,citation,http://doi.ieeecomputersociety.org/10.1109/TPAMI.2005.165,On the Euclidean distance of images,2005 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Technology,edu,88ed558bff3600f5354963d1abe762309f66111e,citation,https://doi.org/10.1109/TIFS.2015.2393553,Real-World and Rapid Face Recognition Toward Pose and Expression Variations via Feature Library Matrix,2015 -416,FERET,feret,35.6037444,53.43445877,Semnan University,edu,88ed558bff3600f5354963d1abe762309f66111e,citation,https://doi.org/10.1109/TIFS.2015.2393553,Real-World and Rapid Face Recognition Toward Pose and Expression Variations via Feature Library Matrix,2015 -417,FERET,feret,45.7413921,126.62552755,Harbin Institute of Technology,edu,016a8ed8f6ba49bc669dbd44de4ff31a79963078,citation,https://doi.org/10.1109/ICASSP.2004.1327215,Face relighting for face recognition under generic illumination,2004 -418,FERET,feret,23.09461185,113.28788994,Sun Yat-Sen University,edu,44f48a4b1ef94a9104d063e53bf88a69ff0f55f3,citation,http://pdfs.semanticscholar.org/44f4/8a4b1ef94a9104d063e53bf88a69ff0f55f3.pdf,Automatically Building Face Datasets of New Domains from Weakly Labeled Data with 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Indefinite Kernels,2012 -426,FERET,feret,37.4102193,-122.05965487,Carnegie Mellon University,edu,521c2e9892eb22f65ba5b0d4c8d2f4c096d9fdf3,citation,http://www.ri.cmu.edu/pub_files/pub4/gross_ralph_2006_2/gross_ralph_2006_2.pdf,Model-Based Face De-Identification,2006 -427,FERET,feret,13.65450525,100.49423171,Robotics Institute,edu,521c2e9892eb22f65ba5b0d4c8d2f4c096d9fdf3,citation,http://www.ri.cmu.edu/pub_files/pub4/gross_ralph_2006_2/gross_ralph_2006_2.pdf,Model-Based Face De-Identification,2006 -428,FERET,feret,25.01682835,121.53846924,National Taiwan University,edu,91e507d2d8375bf474f6ffa87788aa3e742333ce,citation,http://pdfs.semanticscholar.org/91e5/07d2d8375bf474f6ffa87788aa3e742333ce.pdf,Robust Face Recognition Using Probabilistic Facial Trait Code,2010 -429,FERET,feret,39.2899685,-76.62196103,University of Maryland,edu,744b794f0047b008c517752fc9bb1100e5f120cc,citation,http://doi.ieeecomputersociety.org/10.1109/ICPR.2004.1333736,Multiple-exemplar discriminant analysis for face 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Sciences,edu,7b47eb8faaf9c2275cdc70299b850ed649ceec62,citation,http://pdfs.semanticscholar.org/7b47/eb8faaf9c2275cdc70299b850ed649ceec62.pdf,1D-LDA vs. 2D-LDA: When is vector-based linear discriminant analysis better than matrix-based?,2008 -506,FERET,feret,35.0116363,135.7680294,"OMRON Corporation, Kyoto, Japan",company,38e7f3fe450b126367ec358be9b4cc04e82fa8c7,citation,https://doi.org/10.1109/TIP.2014.2351265,Maximal Likelihood Correspondence Estimation for Face Recognition Across Pose,2014 -507,FERET,feret,40.0044795,116.370238,Chinese Academy of Sciences,edu,38e7f3fe450b126367ec358be9b4cc04e82fa8c7,citation,https://doi.org/10.1109/TIP.2014.2351265,Maximal Likelihood Correspondence Estimation for Face Recognition Across Pose,2014 -508,FERET,feret,33.776033,-84.39884086,Georgia Institute of Technology,edu,ffa23a8c988e57cf5fc21b56b522a4ee68f2f362,citation,https://pdfs.semanticscholar.org/ffa2/3a8c988e57cf5fc21b56b522a4ee68f2f362.pdf,Social game retrieval from unstructured videos,2010 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Campinas,edu,3514f66f155c271981a734f1523572edcd8fd10e,citation,http://www.umiacs.umd.edu/~jhchoi/paper/wacv2012_slide.pdf,A complementary local feature descriptor for face identification,2012 -531,FERET,feret,40.5709358,-105.08655256,Colorado State University,edu,aa4d1ad6fd2dbc05139b8121b500c2b1f6b35bec,citation,http://pdfs.semanticscholar.org/aa4d/1ad6fd2dbc05139b8121b500c2b1f6b35bec.pdf,Grassmann Registration Manifolds for Face Recognition,2008 -532,FERET,feret,51.24303255,-0.59001382,University of Surrey,edu,c79cf7f61441195404472102114bcf079a72138a,citation,https://pdfs.semanticscholar.org/9704/8d901389535b122f82a6a949bd8f596790f2.pdf,Pose-Invariant 2 D Face Recognition by Matching Using Graphical Models,2010 -533,FERET,feret,40.0044795,116.370238,Chinese Academy of Sciences,edu,1acf8970598bb2443fd2dd42ceeca1eb3f2fc613,citation,https://pdfs.semanticscholar.org/1acf/8970598bb2443fd2dd42ceeca1eb3f2fc613.pdf,Boosting Statistical Local Feature Based Classifiers for Face Recognition,2005 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Distributed System for Supporting Spatio-temporal Analysis on Large-scale Camera Networks,2012 -569,FERET,feret,48.9095338,9.1831892,University of Stuttgart,edu,987feaa36f3bb663ac9fa767718c6a90ea0dab3f,citation,https://pdfs.semanticscholar.org/987f/eaa36f3bb663ac9fa767718c6a90ea0dab3f.pdf,A Distributed System for Supporting Spatio-temporal Analysis on Large-scale Camera Networks,2012 -570,FERET,feret,42.9336278,-78.88394479,SUNY Buffalo,edu,987feaa36f3bb663ac9fa767718c6a90ea0dab3f,citation,https://pdfs.semanticscholar.org/987f/eaa36f3bb663ac9fa767718c6a90ea0dab3f.pdf,A Distributed System for Supporting Spatio-temporal Analysis on Large-scale Camera Networks,2012 -571,FERET,feret,-33.3578899,151.37834708,University of Newcastle,edu,2feb7c57d51df998aafa6f3017662263a91625b4,citation,https://pdfs.semanticscholar.org/d344/9eaaf392fd07b676e744410049f4095b4b5c.pdf,Feature Selection for Intelligent Transportation Systems,2014 -572,FERET,feret,22.1240187,113.54510901,University of Macau,edu,c3558f67b3f4b618e6b53ce844faf38240ee7cd7,citation,https://arxiv.org/pdf/1802.07589.pdf,Collaboratively Weighting Deep and Classic Representation via $l_2$ Regularization for Image Classification,2018 -573,FERET,feret,50.89273635,-1.39464295,University of Southampton,edu,c3558f67b3f4b618e6b53ce844faf38240ee7cd7,citation,https://arxiv.org/pdf/1802.07589.pdf,Collaboratively Weighting Deep and Classic Representation via $l_2$ Regularization for Image Classification,2018 -574,FERET,feret,32.20302965,119.50968362,Jiangsu University,edu,c3558f67b3f4b618e6b53ce844faf38240ee7cd7,citation,https://arxiv.org/pdf/1802.07589.pdf,Collaboratively Weighting Deep and Classic Representation via $l_2$ Regularization for Image Classification,2018 -575,FERET,feret,35.9990522,-78.9290629,Duke University,edu,a7678cce6bfca4a34feee5564c87c80fe192a0fd,citation,http://pdfs.semanticscholar.org/a767/8cce6bfca4a34feee5564c87c80fe192a0fd.pdf,The Weakly Identifying System for Doorway Monitoring,2007 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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 -587,FERET,feret,52.9387428,-1.20029569,University of Nottingham,edu,9fbcf40b0649c03ba0f38f940c34e7e6c9e04c03,citation,https://doi.org/10.1007/s10044-006-0033-y,A review on Gabor wavelets for face recognition,2006 -588,FERET,feret,35.9542493,-83.9307395,University of Tennessee,edu,d103df0381582003c7a8930b68047b4f26d9b613,citation,http://pdfs.semanticscholar.org/d103/df0381582003c7a8930b68047b4f26d9b613.pdf,Quality Assessment and Restoration of Face Images in Long Range/High Zoom Video,2006 -589,FERET,feret,13.0222347,77.56718325,Indian Institute of Science Bangalore,edu,56fb30b24e7277b47d366ca2c491749eee4d6bb1,citation,https://doi.org/10.1109/ICAPR.2015.7050658,Using Bayesian statistics and Gabor Wavelets for recognition of human faces,2015 -590,FERET,feret,39.2899685,-76.62196103,University of 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mistakes: How visual search accuracy influences evaluation of stimuli.,2015 -601,FERET,feret,51.5231607,-0.1282037,University College London,edu,533d70c914a4b84ec7f35ef6c74bb3acba4c26fc,citation,http://pdfs.semanticscholar.org/533d/70c914a4b84ec7f35ef6c74bb3acba4c26fc.pdf,Blaming the victims of your own mistakes: How visual search accuracy influences evaluation of stimuli.,2015 -602,FERET,feret,40.0141905,-83.0309143,University of Electronic Science and Technology of China,edu,ccd7a6b9f23e983a3fc6a70cc3b9c9673d70bf2c,citation,http://pdfs.semanticscholar.org/ccd7/a6b9f23e983a3fc6a70cc3b9c9673d70bf2c.pdf,Symmetrical Two-Dimensional PCA with Image Measures in Face Recognition,2012 -603,FERET,feret,35.9113971,-79.0504529,University of North Carolina at Chapel Hill,edu,60a006bdfe5b8bf3243404fae8a5f4a9d58fa892,citation,http://alumni.cs.ucr.edu/~mkafai/papers/Paper_bwild.pdf,A reference-based framework for pose invariant face recognition,2015 -604,FERET,feret,32.0565957,118.77408833,Nanjing 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Ljubljana,edu,86274e426bfe962d5cb994d5d9c6829f64410c32,citation,http://pdfs.semanticscholar.org/8627/4e426bfe962d5cb994d5d9c6829f64410c32.pdf,Face Recognition in Different Subspaces: A Comparative Study,2006 -619,FERET,feret,40.8419836,-73.94368971,Columbia University,edu,4c170a0dcc8de75587dae21ca508dab2f9343974,citation,http://pdfs.semanticscholar.org/73a8/1d311eedac8dea3ca24dc15b6990fa4a725e.pdf,FaceTracer: A Search Engine for Large Collections of Images with Faces,2008 -620,FERET,feret,39.2899685,-76.62196103,University of Maryland,edu,4276eb27e2e4fc3e0ceb769eca75e3c73b7f2e99,citation,http://pdfs.semanticscholar.org/4276/eb27e2e4fc3e0ceb769eca75e3c73b7f2e99.pdf,Face Recognition From Video,2008 -621,FERET,feret,45.7413921,126.62552755,Harbin Institute of Technology,edu,63f9f3f0e1daede934d6dde1a84fb7994f8929f0,citation,http://www.jdl.ac.cn/user/sgshan/pub/ICCV2005-ZhangShan-LGBP.pdf,Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition,2005 -622,FERET,feret,37.4102193,-122.05965487,Carnegie Mellon University,edu,39e1fb5539737a17ae5fc25de30377dfaecfa100,citation,https://www.ri.cmu.edu/pub_files/pub4/gross_ralph_2004_1/gross_ralph_2004_1.pdf,Appearance-based face recognition and light-fields,2004 -623,FERET,feret,13.0105838,80.2353736,Anna University,edu,e19ba2a6ce70fb94d31bb0b39387aa734e6860b0,citation,http://pdfs.semanticscholar.org/e19b/a2a6ce70fb94d31bb0b39387aa734e6860b0.pdf,A Different Approach to Appearance –based Statistical Method for Face Recognition Using Median,2007 -624,FERET,feret,32.87935255,-117.23110049,"University of California, San Diego",edu,528a6698911ff30aa648af4d0a5cf0dd9ee90b5c,citation,https://pdfs.semanticscholar.org/528a/6698911ff30aa648af4d0a5cf0dd9ee90b5c.pdf,Is All Face Processing Holistic ? The View from UCSD,2003 -625,FERET,feret,41.6659,-91.57310307,University of Iowa,edu,528a6698911ff30aa648af4d0a5cf0dd9ee90b5c,citation,https://pdfs.semanticscholar.org/528a/6698911ff30aa648af4d0a5cf0dd9ee90b5c.pdf,Is All Face Processing Holistic ? 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Learning for Unconstrained Face Identification,2017 -34,IJB-A,ijb_c,-35.23656905,149.08446994,University of Canberra,edu,0c1d85a197a1f5b7376652a485523e616a406273,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2017.169,Joint Registration and Representation Learning for Unconstrained Face Identification,2017 -35,IJB-A,ijb_c,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 -36,IJB-A,ijb_c,42.718568,-84.47791571,Michigan State University,edu,450c6a57f19f5aa45626bb08d7d5d6acdb863b4b,citation,https://arxiv.org/pdf/1805.00611.pdf,Towards Interpretable Face Recognition,2018 -37,IJB-A,ijb_c,51.7534538,-1.25400997,University of Oxford,edu,30180f66d5b4b7c0367e4b43e2b55367b72d6d2a,citation,http://www.robots.ox.ac.uk/~vgg/publications/2017/Crosswhite17/crosswhite17.pdf,Template Adaptation for Face Verification and Identification,2017 -38,IJB-A,ijb_c,29.7207902,-95.34406271,University of Houston,edu,8334da483f1986aea87b62028672836cb3dc6205,citation,https://arxiv.org/pdf/1805.06306.pdf,Fully Associative Patch-Based 1-to-N Matcher for Face Recognition,2018 -39,IJB-A,ijb_c,-33.8809651,151.20107299,University of Technology Sydney,edu,3b64efa817fd609d525c7244a0e00f98feacc8b4,citation,http://doi.acm.org/10.1145/2845089,A Comprehensive Survey on Pose-Invariant Face Recognition,2016 -40,IJB-A,ijb_c,40.9153196,-73.1270626,Stony Brook University,edu,6fbb179a4ad39790f4558dd32316b9f2818cd106,citation,http://pdfs.semanticscholar.org/6fbb/179a4ad39790f4558dd32316b9f2818cd106.pdf,Input Aggregated Network for Face Video Representation,2016 -41,IJB-A,ijb_c,38.8920756,-104.79716389,"University of Colorado, Colorado Springs",edu,d4f1eb008eb80595bcfdac368e23ae9754e1e745,citation,https://arxiv.org/pdf/1708.02337.pdf,Unconstrained Face Detection and Open-Set Face Recognition Challenge,2017 -42,IJB-A,ijb_c,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 -43,IJB-A,ijb_c,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 -44,IJB-A,ijb_c,34.0224149,-118.28634407,University of Southern California,edu,d28d32af7ef9889ef9cb877345a90ea85e70f7f1,citation,http://doi.ieeecomputersociety.org/10.1109/FG.2017.84,Local-Global Landmark Confidences for Face Recognition,2017 -45,IJB-A,ijb_c,37.4102193,-122.05965487,Carnegie Mellon University,edu,d28d32af7ef9889ef9cb877345a90ea85e70f7f1,citation,http://doi.ieeecomputersociety.org/10.1109/FG.2017.84,Local-Global Landmark Confidences for Face Recognition,2017 -46,IJB-A,ijb_c,51.5247272,-0.03931035,Queen Mary University of London,edu,a29566375836f37173ccaffa47dea25eb1240187,citation,https://arxiv.org/pdf/1809.09409.pdf,Vehicle Re-Identification in Context,2018 -47,IJB-A,ijb_c,34.0224149,-118.28634407,University of Southern California,edu,29f298dd5f806c99951cb434834bc8dcc765df18,citation,https://doi.org/10.1109/ICPR.2016.7899837,Computationally efficient template-based face recognition,2016 -48,IJB-A,ijb_c,51.49887085,-0.17560797,Imperial College London,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017 -49,IJB-A,ijb_c,51.59029705,-0.22963221,Middlesex University,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017 -50,IJB-A,ijb_c,50.8142701,8.771435,Philipps-Universität Marburg,edu,5981c309bd0ffd849c51b1d8a2ccc481a8ec2f5c,citation,https://doi.org/10.1109/ICT.2017.7998256,SmartFace: Efficient face detection on smartphones for wireless on-demand emergency networks,2017 -51,IJB-A,ijb_c,42.718568,-84.47791571,Michigan State University,edu,a2b4a6c6b32900a066d0257ae6d4526db872afe2,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8272466,Learning Face Image Quality From Human Assessments,2018 -52,IJB-A,ijb_c,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,3dfb822e16328e0f98a47209d7ecd242e4211f82,citation,https://arxiv.org/pdf/1708.08197.pdf,Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments,2017 -53,IJB-A,ijb_c,47.6423318,-122.1369302,Microsoft,company,291265db88023e92bb8c8e6390438e5da148e8f5,citation,http://pdfs.semanticscholar.org/4603/cb8e05258bb0572ae912ad20903b8f99f4b1.pdf,MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition,2016 -54,IJB-A,ijb_c,42.718568,-84.47791571,Michigan State University,edu,d29eec5e047560627c16803029d2eb8a4e61da75,citation,http://pdfs.semanticscholar.org/d29e/ec5e047560627c16803029d2eb8a4e61da75.pdf,Feature Transfer Learning for Deep Face Recognition with Long-Tail Data,2018 -55,IJB-A,ijb_c,36.20304395,117.05842113,Tianjin University,edu,5180df9d5eb26283fb737f491623395304d57497,citation,https://arxiv.org/pdf/1804.10899.pdf,Scalable Angular Discriminative Deep Metric Learning for Face Recognition,2018 -56,IJB-A,ijb_c,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 -57,IJB-A,ijb_c,28.5456282,77.2731505,"IIIT Delhi, India",edu,3cf1f89d73ca4b25399c237ed3e664a55cd273a2,citation,https://arxiv.org/pdf/1710.02914.pdf,Face Sketch Matching via Coupled Deep Transform Learning,2017 -58,IJB-A,ijb_c,-27.49741805,153.01316956,University of Queensland,edu,f27fd2a1bc229c773238f1912db94991b8bf389a,citation,https://doi.org/10.1109/IVCNZ.2016.7804414,How do you develop a face detector for the unconstrained environment?,2016 -59,IJB-A,ijb_c,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 -60,IJB-A,ijb_c,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 -61,IJB-A,ijb_c,28.2290209,112.99483204,"National University of Defense Technology, China",edu,c1cc2a2a1ab66f6c9c6fabe28be45d1440a57c3d,citation,https://pdfs.semanticscholar.org/aae7/a5182e59f44b7bb49f61999181ce011f800b.pdf,Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis,2017 -62,IJB-A,ijb_c,1.2962018,103.77689944,National University of Singapore,edu,c1cc2a2a1ab66f6c9c6fabe28be45d1440a57c3d,citation,https://pdfs.semanticscholar.org/aae7/a5182e59f44b7bb49f61999181ce011f800b.pdf,Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis,2017 -63,IJB-A,ijb_c,17.4454957,78.34854698,International Institute of Information Technology,edu,f5eb411217f729ad7ae84bfd4aeb3dedb850206a,citation,https://pdfs.semanticscholar.org/f5eb/411217f729ad7ae84bfd4aeb3dedb850206a.pdf,Tackling Low Resolution for Better Scene Understanding,2018 -64,IJB-A,ijb_c,40.51865195,-74.44099801,State University of New Jersey,edu,96e731e82b817c95d4ce48b9e6b08d2394937cf8,citation,http://arxiv.org/pdf/1508.01722v2.pdf,Unconstrained face verification using deep CNN features,2016 -65,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,96e731e82b817c95d4ce48b9e6b08d2394937cf8,citation,http://arxiv.org/pdf/1508.01722v2.pdf,Unconstrained face verification using deep CNN features,2016 -66,IJB-A,ijb_c,32.77824165,34.99565673,Open University of Israel,edu,870433ba89d8cab1656e57ac78f1c26f4998edfb,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2017.163,Regressing Robust and Discriminative 3D Morphable Models with a Very Deep Neural Network,2017 -67,IJB-A,ijb_c,55.6801502,12.572327,University of Copenhagen,edu,3dfd94d3fad7e17f52a8ae815eb9cc5471172bc0,citation,http://pdfs.semanticscholar.org/3dfd/94d3fad7e17f52a8ae815eb9cc5471172bc0.pdf,Face2Text: Collecting an Annotated Image Description Corpus for the Generation of Rich Face Descriptions,2018 -68,IJB-A,ijb_c,35.9023226,14.4834189,University of Malta,edu,3dfd94d3fad7e17f52a8ae815eb9cc5471172bc0,citation,http://pdfs.semanticscholar.org/3dfd/94d3fad7e17f52a8ae815eb9cc5471172bc0.pdf,Face2Text: Collecting an Annotated Image Description Corpus for the Generation of Rich Face Descriptions,2018 -69,IJB-A,ijb_c,34.0224149,-118.28634407,University of Southern California,edu,6341274aca0c2977c3e1575378f4f2126aa9b050,citation,http://arxiv.org/pdf/1609.03536v1.pdf,A multi-scale cascade fully convolutional network face detector,2016 -70,IJB-A,ijb_c,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 -71,IJB-A,ijb_c,37.4102193,-122.05965487,Carnegie Mellon University,edu,a0b1990dd2b4cd87e4fd60912cc1552c34792770,citation,https://pdfs.semanticscholar.org/a0b1/990dd2b4cd87e4fd60912cc1552c34792770.pdf,Deep Constrained Local Models for Facial Landmark Detection,2016 -72,IJB-A,ijb_c,30.642769,104.06751175,"Sichuan University, Chengdu",edu,772474b5b0c90629f4d9c223fd9c1ef45e1b1e66,citation,https://doi.org/10.1109/BTAS.2017.8272716,Multi-dim: A multi-dimensional face database towards the application of 3D technology in real-world scenarios,2017 -73,IJB-A,ijb_c,38.8920756,-104.79716389,"University of Colorado, Colorado Springs",edu,4b3f425274b0c2297d136f8833a31866db2f2aec,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2017.85,Toward Open-Set Face Recognition,2017 -74,IJB-A,ijb_c,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 -75,IJB-A,ijb_c,37.3351908,-121.88126008,San Jose State University,edu,14b016c7a87d142f4b9a0e6dc470dcfc073af517,citation,http://ws680.nist.gov/publication/get_pdf.cfm?pub_id=918912,Modest proposals for improving biometric recognition papers,2015 -76,IJB-A,ijb_c,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 -77,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,def2983576001bac7d6461d78451159800938112,citation,https://arxiv.org/pdf/1705.07426.pdf,The Do’s and Don’ts for CNN-Based Face Verification,2017 -78,IJB-A,ijb_c,42.718568,-84.47791571,Michigan State University,edu,4b605e6a9362485bfe69950432fa1f896e7d19bf,citation,http://biometrics.cse.msu.edu/Publications/Face/BlantonAllenMillerKalkaJain_CVPRWB2016_HID.pdf,A Comparison of Human and Automated Face Verification Accuracy on Unconstrained Image Sets,2016 -79,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,8d3e95c31c93548b8c71dbeee2e9f7180067a888,citation,https://doi.org/10.1109/ICPR.2016.7899841,Template regularized sparse coding for face verification,2016 -80,IJB-A,ijb_c,42.8271556,-73.8780481,GE Global Research,company,8d3e95c31c93548b8c71dbeee2e9f7180067a888,citation,https://doi.org/10.1109/ICPR.2016.7899841,Template regularized sparse coding for face verification,2016 -81,IJB-A,ijb_c,25.0410728,121.6147562,Institute of Information Science,edu,337dd4aaca2c5f9b5d2de8e0e2401b5a8feb9958,citation,https://arxiv.org/pdf/1810.11160.pdf,Data-specific Adaptive Threshold for Face Recognition and Authentication,2018 -82,IJB-A,ijb_c,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,0aeb5020003e0c89219031b51bd30ff1bceea363,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.525,Sparsifying Neural Network Connections for Face Recognition,2016 -83,IJB-A,ijb_c,22.42031295,114.20788644,Chinese University of Hong Kong,edu,0aeb5020003e0c89219031b51bd30ff1bceea363,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.525,Sparsifying Neural Network Connections for Face Recognition,2016 -84,IJB-A,ijb_c,42.718568,-84.47791571,Michigan State University,edu,99daa2839213f904e279aec7cef26c1dfb768c43,citation,https://arxiv.org/pdf/1805.02283.pdf,DocFace: Matching ID Document Photos to Selfies,2018 -85,IJB-A,ijb_c,43.7776426,11.259765,University of Florence,edu,71ca8b6e84c17b3e68f980bfb8cddc837100f8bf,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7899774,Effective 3D based frontalization for unconstrained face recognition,2016 -86,IJB-A,ijb_c,51.49887085,-0.17560797,Imperial College London,edu,c43ed9b34cad1a3976bac7979808eb038d88af84,citation,https://arxiv.org/pdf/1804.03675.pdf,Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model,2018 -87,IJB-A,ijb_c,51.24303255,-0.59001382,University of Surrey,edu,c43ed9b34cad1a3976bac7979808eb038d88af84,citation,https://arxiv.org/pdf/1804.03675.pdf,Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model,2018 -88,IJB-A,ijb_c,37.3936717,-122.0807262,Facebook,company,628a3f027b7646f398c68a680add48c7969ab1d9,citation,https://pdfs.semanticscholar.org/628a/3f027b7646f398c68a680add48c7969ab1d9.pdf,Plan for Final Year Project : HKU-Face : A Large Scale Dataset for Deep Face Recognition,2017 -89,IJB-A,ijb_c,40.2773077,-7.5095801,University of Beira Interior,edu,61262450d4d814865a4f9a84299c24daa493f66e,citation,http://doi.org/10.1007/s10462-016-9474-x,Biometric recognition in surveillance scenarios: a survey,2016 -90,IJB-A,ijb_c,-31.95040445,115.79790037,University of Western Australia,edu,626913b8fcbbaee8932997d6c4a78fe1ce646127,citation,https://arxiv.org/pdf/1711.05942.pdf,Learning from Millions of 3D Scans for Large-scale 3D Face Recognition,2017 -91,IJB-A,ijb_c,35.9023226,14.4834189,University of Malta,edu,4efd58102ff46b7435c9ec6d4fc3dd21d93b15b4,citation,https://doi.org/10.1109/TIFS.2017.2788002,"Matching Software-Generated Sketches to Face Photographs With a Very Deep CNN, Morphed Faces, and Transfer Learning",2018 -92,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,b6f758be954d34817d4ebaa22b30c63a4b8ddb35,citation,http://arxiv.org/abs/1703.04835,A Proximity-Aware Hierarchical Clustering of Faces,2017 -93,IJB-A,ijb_c,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 -94,IJB-A,ijb_c,40.2773077,-7.5095801,University of Beira Interior,edu,84ae55603bffda40c225fe93029d39f04793e01f,citation,https://doi.org/10.1109/ICB.2016.7550066,ICB-RW 2016: International challenge on biometric recognition in the wild,2016 -95,IJB-A,ijb_c,41.70456775,-86.23822026,University of Notre Dame,edu,73ea06787925157df519a15ee01cc3dc1982a7e0,citation,https://arxiv.org/pdf/1811.01474.pdf,Fast Face Image Synthesis with Minimal Training,2018 -96,IJB-A,ijb_c,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 -97,IJB-A,ijb_c,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 -98,IJB-A,ijb_c,40.47913175,-74.43168868,Rutgers University,edu,eee06d68497be8bf3a8aba4fde42a13aa090b301,citation,https://arxiv.org/pdf/1806.11191.pdf,CR-GAN: Learning Complete Representations for Multi-view Generation,2018 -99,IJB-A,ijb_c,35.3103441,-80.73261617,University of North Carolina at Charlotte,edu,eee06d68497be8bf3a8aba4fde42a13aa090b301,citation,https://arxiv.org/pdf/1806.11191.pdf,CR-GAN: Learning Complete Representations for Multi-view Generation,2018 -100,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,a3201e955d6607d383332f3a12a7befa08c5a18c,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7900276,VLAD encoded Deep Convolutional features for unconstrained face verification,2016 -101,IJB-A,ijb_c,40.47913175,-74.43168868,Rutgers University,edu,a3201e955d6607d383332f3a12a7befa08c5a18c,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7900276,VLAD encoded Deep Convolutional features for unconstrained face verification,2016 -102,IJB-A,ijb_c,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 -103,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,19458454308a9f56b7de76bf7d8ff8eaa52b0173,citation,https://pdfs.semanticscholar.org/1945/8454308a9f56b7de76bf7d8ff8eaa52b0173.pdf,Deep Features for Recognizing Disguised Faces in the Wild,0 -104,IJB-A,ijb_c,43.7776426,11.259765,University of Florence,edu,746c0205fdf191a737df7af000eaec9409ede73f,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8423119,Investigating Nuisances in DCNN-Based Face Recognition,2018 -105,IJB-A,ijb_c,47.5612651,7.5752961,University of Basel,edu,0081e2188c8f34fcea3e23c49fb3e17883b33551,citation,http://pdfs.semanticscholar.org/0081/e2188c8f34fcea3e23c49fb3e17883b33551.pdf,Training Deep Face Recognition Systems with Synthetic Data,2018 -106,IJB-A,ijb_c,37.4102193,-122.05965487,Carnegie Mellon University,edu,2b869d5551b10f13bf6fcdb8d13f0aa4d1f59fc4,citation,https://arxiv.org/pdf/1803.00130.pdf,Ring loss: Convex Feature Normalization for Face Recognition,2018 -107,IJB-A,ijb_c,28.2290209,112.99483204,"National University of Defense Technology, China",edu,5f771fed91c8e4b666489ba2384d0705bcf75030,citation,https://arxiv.org/pdf/1804.03287.pdf,Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing,2018 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Discovery in Human Events,2013 -4,Images of Groups,images_of_groups,37.43131385,-122.16936535,Stanford University,edu,d84230a2fc9950fccfd37f0291d65e634b5ffc32,citation,http://pdfs.semanticscholar.org/d842/30a2fc9950fccfd37f0291d65e634b5ffc32.pdf,Historical and Modern Image-to-Image Translation with Generative Adversarial Networks,2017 -5,Images of Groups,images_of_groups,25.01682835,121.53846924,National Taiwan University,edu,046865a5f822346c77e2865668ec014ec3282033,citation,http://www.csie.ntu.edu.tw/~winston/papers/chen12discovering.pdf,Discovering informative social subgraphs and predicting pairwise relationships from group photos,2012 -6,Images of Groups,images_of_groups,28.59899755,-81.19712501,University of Central Florida,edu,0aa303109a3402aa5a203877847d549c4a24d933,citation,http://crcv-web.eecs.ucf.edu/papers/cvpr2014/Resemblance_CVPR14.pdf,Who Do I Look Like? Determining Parent-Offspring Resemblance via Gated Autoencoders,2014 -7,Images of Groups,images_of_groups,37.4102193,-122.05965487,Carnegie Mellon University,edu,c6096986b4d6c374ab2d20031e026b581e7bf7e9,citation,http://pdfs.semanticscholar.org/c609/6986b4d6c374ab2d20031e026b581e7bf7e9.pdf,A Framework for Using Context to Understand Images of People,2009 -8,Images of Groups,images_of_groups,51.5231607,-0.1282037,University College London,edu,6aaa77e241fe55ae0c4ad281e27886ea778f9e23,citation,http://pdfs.semanticscholar.org/b562/ad2ae12920cb318c5309a35000b4d5eb27b8.pdf,F-Formation Detection: Individuating Free-Standing Conversational Groups in Images,2015 -9,Images of Groups,images_of_groups,43.7743911,-79.50481085,York University,edu,ffe4bb47ec15f768e1744bdf530d5796ba56cfc1,citation,https://arxiv.org/pdf/1706.04277.pdf,AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces,2017 -10,Images of Groups,images_of_groups,27.18794105,31.17009498,Assiut University,edu,ffe4bb47ec15f768e1744bdf530d5796ba56cfc1,citation,https://arxiv.org/pdf/1706.04277.pdf,AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces,2017 -11,Images of Groups,images_of_groups,40.9153196,-73.1270626,Stony Brook University,edu,14e9158daf17985ccbb15c9cd31cf457e5551990,citation,http://pdfs.semanticscholar.org/14e9/158daf17985ccbb15c9cd31cf457e5551990.pdf,ConvNets with Smooth Adaptive Activation Functions for Regression,2017 -12,Images of Groups,images_of_groups,40.90826665,-73.11520891,Stony Brook University Hospital,edu,14e9158daf17985ccbb15c9cd31cf457e5551990,citation,http://pdfs.semanticscholar.org/14e9/158daf17985ccbb15c9cd31cf457e5551990.pdf,ConvNets with Smooth Adaptive Activation Functions for Regression,2017 -13,Images of Groups,images_of_groups,-22.8148374,-47.0647708,University of Campinas (UNICAMP),edu,b161d261fabb507803a9e5834571d56a3b87d147,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8122913,Gender recognition from face images using a geometric descriptor,2017 -14,Images of Groups,images_of_groups,40.9153196,-73.1270626,Stony Brook University,edu,1190cba0cae3c8bb81bf80d6a0a83ae8c41240bc,citation,https://pdfs.semanticscholar.org/1190/cba0cae3c8bb81bf80d6a0a83ae8c41240bc.pdf,Squared Earth Mover ’ s Distance Loss for Training Deep Neural Networks on Ordered-Classes,2017 -15,Images of Groups,images_of_groups,24.94314825,121.36862979,National Taipei University,edu,30cc1ddd7a9b4878cca7783a59086bdc49dc4044,citation,https://doi.org/10.1007/s11042-015-2599-0,Intensity contrast masks for gender classification,2015 -16,Images of Groups,images_of_groups,-35.2776999,149.118527,Australian National University,edu,49e541e0bbc7a082e5c952fc70716e66e5713080,citation,http://ieeexplore.ieee.org/document/6460925/,Group expression intensity estimation in videos via Gaussian Processes,2012 -17,Images of Groups,images_of_groups,50.3755269,-4.13937687,Plymouth University,edu,8bed7ff2f75d956652320270eaf331e1f73efb35,citation,https://arxiv.org/pdf/1709.03820.pdf,Emotion recognition in the wild using deep neural networks and Bayesian classifiers,2017 -18,Images of Groups,images_of_groups,39.3650216,16.2257177,University of Calabria,edu,8bed7ff2f75d956652320270eaf331e1f73efb35,citation,https://arxiv.org/pdf/1709.03820.pdf,Emotion recognition in the wild using deep neural networks and Bayesian classifiers,2017 -19,Images of Groups,images_of_groups,51.7534538,-1.25400997,University of Oxford,edu,0be8b12f194fb604be69c139a195799e8ab53fd3,citation,http://www.robots.ox.ac.uk/~vgg/publications/2014/Hoai14/poster.pdf,Talking Heads: Detecting Humans and Recognizing Their Interactions,2014 -20,Images of Groups,images_of_groups,-35.2776999,149.118527,Australian National University,edu,0d3068b352c3733c9e1cc75e449bf7df1f7b10a4,citation,http://doi.ieeecomputersociety.org/10.1109/ACII.2013.111,Context Based Facial Expression Analysis in the Wild,2013 -21,Images of Groups,images_of_groups,45.42580475,-75.68740118,University of Ottawa,edu,16820ccfb626dcdc893cc7735784aed9f63cbb70,citation,http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W12/papers/Azarmehr_Real-Time_Embedded_Age_2015_CVPR_paper.pdf,Real-time embedded age and gender classification in unconstrained video,2015 -22,Images of Groups,images_of_groups,51.5247272,-0.03931035,Queen Mary University of London,edu,fcc82154067dfe778423c2df4ed69f0bec6e1534,citation,https://pdfs.semanticscholar.org/fcc8/2154067dfe778423c2df4ed69f0bec6e1534.pdf,Automatic Analysis of Affect and Membership in Group Settings,2017 -23,Images of Groups,images_of_groups,52.17638955,0.14308882,University of Cambridge,edu,fcc82154067dfe778423c2df4ed69f0bec6e1534,citation,https://pdfs.semanticscholar.org/fcc8/2154067dfe778423c2df4ed69f0bec6e1534.pdf,Automatic Analysis of Affect and Membership in Group Settings,2017 -24,Images of Groups,images_of_groups,30.284151,-97.73195598,University of Texas at Austin,edu,45513d0f2f5c0dac5b61f9ff76c7e46cce62f402,citation,http://pdfs.semanticscholar.org/4551/3d0f2f5c0dac5b61f9ff76c7e46cce62f402.pdf,Face Discovery with Social Context,2011 -25,Images of Groups,images_of_groups,37.26728,126.9841151,Seoul National University,edu,282503fa0285240ef42b5b4c74ae0590fe169211,citation,http://pdfs.semanticscholar.org/2825/03fa0285240ef42b5b4c74ae0590fe169211.pdf,Feeding Hand-Crafted Features for Enhancing the Performance of Convolutional Neural Networks,2018 -26,Images of Groups,images_of_groups,-35.2776999,149.118527,Australian National University,edu,1ab881ec87167af9071b2ad8ff6d4ce3eee38477,citation,http://pdfs.semanticscholar.org/1ab8/81ec87167af9071b2ad8ff6d4ce3eee38477.pdf,Finding Happiest Moments in a Social Context,2012 -27,Images of Groups,images_of_groups,-35.23656905,149.08446994,University of Canberra,edu,1ab881ec87167af9071b2ad8ff6d4ce3eee38477,citation,http://pdfs.semanticscholar.org/1ab8/81ec87167af9071b2ad8ff6d4ce3eee38477.pdf,Finding Happiest Moments in a Social Context,2012 -28,Images of Groups,images_of_groups,-35.23656905,149.08446994,University of Canberra,edu,572dbaee6648eefa4c9de9b42551204b985ff863,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7163151,The more the merrier: Analysing the affect of a group of people in images,2015 -29,Images of Groups,images_of_groups,32.87935255,-117.23110049,"University of California, San Diego",edu,572dbaee6648eefa4c9de9b42551204b985ff863,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7163151,The more the merrier: Analysing the affect of a group of people in images,2015 -30,Images of Groups,images_of_groups,46.0658836,11.1159894,University of Trento,edu,572dbaee6648eefa4c9de9b42551204b985ff863,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7163151,The more the merrier: Analysing the affect of a group of people in images,2015 -31,Images of Groups,images_of_groups,35.90503535,-79.04775327,University of North Carolina,edu,dbf6d2619bd41ce4c36488e15d114a2da31b51c9,citation,https://arxiv.org/pdf/1810.00028.pdf,Data-Driven Modeling of Group Entitativity in Virtual Environments,2018 -32,Images of Groups,images_of_groups,39.2899685,-76.62196103,University of Maryland,edu,dbf6d2619bd41ce4c36488e15d114a2da31b51c9,citation,https://arxiv.org/pdf/1810.00028.pdf,Data-Driven Modeling of Group Entitativity in Virtual Environments,2018 -33,Images of Groups,images_of_groups,37.4102193,-122.05965487,Carnegie Mellon University,edu,b593f13f974cf444a5781bbd487e1c69e056a1f7,citation,https://pdfs.semanticscholar.org/b593/f13f974cf444a5781bbd487e1c69e056a1f7.pdf,Query Image Query Image Retrievals Retrievals Transferred Poses Transferred Poses,2018 -34,Images of Groups,images_of_groups,43.7776426,11.259765,University of Florence,edu,02cc96ad997102b7c55e177ac876db3b91b4e72c,citation,http://www.micc.unifi.it/wp-content/uploads/2015/12/2015_museum-visitors-dataset.pdf,"MuseumVisitors: A dataset for pedestrian and group detection, gaze estimation and behavior understanding",2015 -35,Images of Groups,images_of_groups,40.8419836,-73.94368971,Columbia University,edu,02cc96ad997102b7c55e177ac876db3b91b4e72c,citation,http://www.micc.unifi.it/wp-content/uploads/2015/12/2015_museum-visitors-dataset.pdf,"MuseumVisitors: A dataset for pedestrian and group detection, gaze estimation and behavior understanding",2015 -36,Images of Groups,images_of_groups,58.38131405,26.72078081,University of Tartu,edu,1b248ed8e7c9514648cd598960fadf9ab17e7fe8,citation,https://pdfs.semanticscholar.org/1b24/8ed8e7c9514648cd598960fadf9ab17e7fe8.pdf,"From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation",0 -37,Images of Groups,images_of_groups,41.3868913,2.16352385,University of Barcelona,edu,1b248ed8e7c9514648cd598960fadf9ab17e7fe8,citation,https://pdfs.semanticscholar.org/1b24/8ed8e7c9514648cd598960fadf9ab17e7fe8.pdf,"From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation",0 -38,Images of Groups,images_of_groups,51.49887085,-0.17560797,Imperial College London,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017 -39,Images of Groups,images_of_groups,51.59029705,-0.22963221,Middlesex University,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017 -40,Images of Groups,images_of_groups,39.2899685,-76.62196103,University of Maryland,edu,3b092733f428b12f1f920638f868ed1e8663fe57,citation,http://www.math.jhu.edu/~data/RamaPapers/PerformanceBounds.pdf,On the size of Convolutional Neural Networks and generalization performance,2016 -41,Images of Groups,images_of_groups,33.6431901,-117.84016494,"University of California, Irvine",edu,3991223b1dc3b87883cec7af97cf56534178f74a,citation,http://doi.acm.org/10.1145/2461466.2461469,A unified framework for context assisted face clustering,2013 -42,Images of Groups,images_of_groups,65.0592157,25.46632601,University of Oulu,edu,1e516273554d87bbe1902fa0298179c493299035,citation,http://www.ee.oulu.fi/~hadid/Age-ICPR2012.pdf,Age Classification in Unconstrained Conditions Using LBP Variants,2012 -43,Images of Groups,images_of_groups,50.89273635,-1.39464295,University of Southampton,edu,fd67d0efbd94c9d8f9d2f0a972edd7320bc7604f,citation,http://pdfs.semanticscholar.org/fd67/d0efbd94c9d8f9d2f0a972edd7320bc7604f.pdf,Real-Time Semantic Clothing Segmentation,2012 -44,Images of Groups,images_of_groups,47.6543238,-122.30800894,University of Washington,edu,f2c30594d917ea915028668bc2a481371a72a14d,citation,http://pdfs.semanticscholar.org/f2c3/0594d917ea915028668bc2a481371a72a14d.pdf,Scene Understanding Using Internet Photo Collections,2010 -45,Images of Groups,images_of_groups,40.47913175,-74.43168868,Rutgers University,edu,31f1e711fcf82c855f27396f181bf5e565a2f58d,citation,http://doi.ieeecomputersociety.org/10.1109/ICCVW.2015.54,Unconstrained Age Estimation with Deep Convolutional Neural Networks,2015 -46,Images of Groups,images_of_groups,39.2899685,-76.62196103,University of Maryland,edu,31f1e711fcf82c855f27396f181bf5e565a2f58d,citation,http://doi.ieeecomputersociety.org/10.1109/ICCVW.2015.54,Unconstrained Age Estimation with Deep Convolutional Neural Networks,2015 -47,Images of Groups,images_of_groups,35.93006535,-84.31240032,Oak Ridge National Laboratory,edu,2cf3564d7421b661e84251d280d159d4b3ebb336,citation,https://doi.org/10.1109/BTAS.2014.6996287,Discriminating projections for estimating face age in wild images,2014 -48,Images of Groups,images_of_groups,34.2239869,-77.8701325,"UNCW, USA",edu,2cf3564d7421b661e84251d280d159d4b3ebb336,citation,https://doi.org/10.1109/BTAS.2014.6996287,Discriminating projections for estimating face age in wild images,2014 -49,Images of Groups,images_of_groups,34.2249827,-77.86907744,University of North Carolina at Wilmington,edu,2cf3564d7421b661e84251d280d159d4b3ebb336,citation,https://doi.org/10.1109/BTAS.2014.6996287,Discriminating projections for estimating face age in wild images,2014 -50,Images of Groups,images_of_groups,41.3868913,2.16352385,University of Barcelona,edu,500fbe18afd44312738cab91b4689c12b4e0eeee,citation,http://www.maia.ub.es/~sergio/linked/ijcnn_age_and_cultural_2015.pdf,ChaLearn looking at people 2015 new competitions: Age estimation and cultural event recognition,2015 -51,Images of Groups,images_of_groups,45.4312742,12.3265377,University of Venezia,edu,500fbe18afd44312738cab91b4689c12b4e0eeee,citation,http://www.maia.ub.es/~sergio/linked/ijcnn_age_and_cultural_2015.pdf,ChaLearn looking at people 2015 new competitions: Age estimation and cultural event recognition,2015 -52,Images of Groups,images_of_groups,42.4505507,-76.4783513,Cornell University,edu,0d57d3d2d04fc96d731cac99a7a8ef79050dac75,citation,http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/IEEE_CVPR2013/data/Papers/Workshops/4990a269.pdf,Not Everybody's Special: Using Neighbors in Referring Expressions with Uncertain Attributes,2013 -53,Images of Groups,images_of_groups,42.4505507,-76.4783513,Cornell University,edu,fbc9ba70e36768efff130c7d970ce52810b044ff,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6738500,Face-graph matching for classifying groups of people,2013 -54,Images of Groups,images_of_groups,37.43131385,-122.16936535,Stanford University,edu,fbc9ba70e36768efff130c7d970ce52810b044ff,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6738500,Face-graph matching for classifying groups of people,2013 -55,Images of Groups,images_of_groups,1.340216,103.965089,Singapore University of Technology and Design,edu,00823e6c0b6f1cf22897b8d0b2596743723ec51c,citation,https://arxiv.org/pdf/1708.07689.pdf,Understanding and Comparing Deep Neural Networks for Age and Gender Classification,2017 -56,Images of Groups,images_of_groups,41.1664858,-73.1920564,University of Bridgeport,edu,ac9a331327cceda4e23f9873f387c9fd161fad76,citation,http://pdfs.semanticscholar.org/ac9a/331327cceda4e23f9873f387c9fd161fad76.pdf,Deep Convolutional Neural Network for Age Estimation based on VGG-Face Model,2017 -57,Images of Groups,images_of_groups,42.4505507,-76.4783513,Cornell University,edu,5aad56cfa2bac5d6635df4184047e809f8fecca2,citation,http://chenlab.ece.cornell.edu/people/Amir/publications/picture_password.pdf,A visual dictionary attack on Picture Passwords,2013 -58,Images of Groups,images_of_groups,42.9336278,-78.88394479,SUNY Buffalo,edu,4793f11fbca4a7dba898b9fff68f70d868e2497c,citation,http://pdfs.semanticscholar.org/4793/f11fbca4a7dba898b9fff68f70d868e2497c.pdf,Kinship Verification through Transfer Learning,2011 -59,Images of Groups,images_of_groups,37.4102193,-122.05965487,Carnegie Mellon University,edu,eddc4989cdb20c8cdfb22e989bdb2cb9031d0439,citation,https://arxiv.org/pdf/1804.03080.pdf,Binge Watching: Scaling Affordance Learning from Sitcoms,2017 -60,Images of Groups,images_of_groups,42.3383668,-71.08793524,Northeastern University,edu,090e4713bcccff52dcd0c01169591affd2af7e76,citation,http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Shao_What_Do_You_2013_ICCV_paper.pdf,What Do You Do? Occupation Recognition in a Photo via Social Context,2013 -61,Images of Groups,images_of_groups,53.21967825,6.56251482,University of Groningen,edu,4ff4c27e47b0aa80d6383427642bb8ee9d01c0ac,citation,https://doi.org/10.1109/SSCI.2015.37,Deep Convolutional Neural Networks and Support Vector Machines for Gender Recognition,2015 -62,Images of Groups,images_of_groups,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,291f527598c589fb0519f890f1beb2749082ddfd,citation,http://pdfs.semanticscholar.org/3215/ceb94227451a958bcf6b1205c710d17e53f5.pdf,Seeing People in Social Context: Recognizing People and Social Relationships,2010 -63,Images of Groups,images_of_groups,42.4505507,-76.4783513,Cornell University,edu,28d06fd508d6f14cd15f251518b36da17909b79e,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Chen_Whats_in_a_2013_CVPR_paper.pdf,What's in a Name? First Names as Facial Attributes,2013 -64,Images of Groups,images_of_groups,37.43131385,-122.16936535,Stanford University,edu,28d06fd508d6f14cd15f251518b36da17909b79e,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Chen_Whats_in_a_2013_CVPR_paper.pdf,What's in a Name? First Names as Facial Attributes,2013 -65,Images of Groups,images_of_groups,47.0570222,21.922709,Queen Mary University,edu,34022637860443c052375c45c4f700afcb438cd0,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2016.185,Automatic Recognition of Emotions and Membership in Group Videos,2016 -66,Images of Groups,images_of_groups,52.17638955,0.14308882,University of Cambridge,edu,34022637860443c052375c45c4f700afcb438cd0,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2016.185,Automatic Recognition of Emotions and Membership in Group Videos,2016 -67,Images of Groups,images_of_groups,38.8964679,-104.8050594,University of Colorado at Colorado Springs,edu,e3e2c106ccbd668fb9fca851498c662add257036,citation,http://www.vast.uccs.edu/~tboult/PAPERS/BTAS13-Sapkota-et-al-Ensembles.pdf,"Appearance, context and co-occurrence ensembles for identity recognition in personal photo collections",2013 -68,Images of Groups,images_of_groups,25.01682835,121.53846924,National Taiwan University,edu,8ba67f45fbb1ce47a90df38f21834db37c840079,citation,http://www.cmlab.csie.ntu.edu.tw/~yanying/paper/dsp006-chen.pdf,People search and activity mining in large-scale community-contributed photos,2012 -69,Images of Groups,images_of_groups,1.2962018,103.77689944,National University of Singapore,edu,a5219fff98dfe3ec81dee95c4ead69a8e24cc802,citation,https://arxiv.org/pdf/1708.00634.pdf,Dual-Glance Model for Deciphering Social Relationships,2017 -70,Images of Groups,images_of_groups,44.97308605,-93.23708813,University of Minnesota,edu,a5219fff98dfe3ec81dee95c4ead69a8e24cc802,citation,https://arxiv.org/pdf/1708.00634.pdf,Dual-Glance Model for Deciphering Social Relationships,2017 -71,Images of Groups,images_of_groups,40.742252,-74.0270949,Stevens Institute of Technology,edu,1e1d7cbbef67e9e042a3a0a9a1bcefcc4a9adacf,citation,http://personal.stevens.edu/~hli18//data/papers/CVPR2016_CameraReady.pdf,A Multi-level Contextual Model for Person Recognition in Photo Albums,2016 -72,Images of Groups,images_of_groups,-34.9189226,138.60423668,University of Adelaide,edu,3d24b386d003bee176a942c26336dbe8f427aadd,citation,http://arxiv.org/abs/1611.09967,Sequential Person Recognition in Photo Albums with a Recurrent Network,2017 -73,Images of Groups,images_of_groups,37.43131385,-122.16936535,Stanford University,edu,111ae23b60284927f2545dfc59b0147bb3423792,citation,https://pdfs.semanticscholar.org/111a/e23b60284927f2545dfc59b0147bb3423792.pdf,Classroom Data Collection and Analysis using Computer Vision,2016 -74,Images of Groups,images_of_groups,51.99882735,4.37396037,Delft University of Technology,edu,dfbf941adeea19f5dff4a70a466ddd1b77f3b727,citation,https://pdfs.semanticscholar.org/dfbf/941adeea19f5dff4a70a466ddd1b77f3b727.pdf,Models for supervised learning in sequence data,2018 -75,Images of Groups,images_of_groups,40.8419836,-73.94368971,Columbia University,edu,774cbb45968607a027ae4729077734db000a1ec5,citation,http://pdfs.semanticscholar.org/774c/bb45968607a027ae4729077734db000a1ec5.pdf,From Bikers to Surfers: Visual Recognition of Urban Tribes,2013 -76,Images of Groups,images_of_groups,32.87935255,-117.23110049,"University of California, San Diego",edu,774cbb45968607a027ae4729077734db000a1ec5,citation,http://pdfs.semanticscholar.org/774c/bb45968607a027ae4729077734db000a1ec5.pdf,From Bikers to Surfers: Visual Recognition of Urban Tribes,2013 -77,Images of Groups,images_of_groups,47.6543238,-122.30800894,University of Washington,edu,5b2bc289b607ca1a0634555158464f28fe68a6d3,citation,http://vision.ics.uci.edu/papers/GargRSS_CVPR_2011/GargRSS_CVPR_2011.pdf,Where's Waldo: Matching people in images of crowds,2011 -78,Images of Groups,images_of_groups,42.4505507,-76.4783513,Cornell University,edu,5b2bc289b607ca1a0634555158464f28fe68a6d3,citation,http://vision.ics.uci.edu/papers/GargRSS_CVPR_2011/GargRSS_CVPR_2011.pdf,Where's Waldo: 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unconstrained faces,2016 -36,IMDB,imdb_wiki,39.2899685,-76.62196103,University of Maryland,edu,d00e9a6339e34c613053d3b2c132fccbde547b56,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7791154,A cascaded convolutional neural network for age estimation of unconstrained faces,2016 -37,IMDB,imdb_wiki,37.2830003,127.04548469,Ajou University,edu,c43dc4ae68a317b34a79636fadb3bcc4d1ccb61c,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8369763,Age and gender estimation using deep residual learning network,2018 -38,IMDB,imdb_wiki,37.403917,127.159786,Korea Electronics Technology Institute,edu,c43dc4ae68a317b34a79636fadb3bcc4d1ccb61c,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8369763,Age and gender estimation using deep residual learning network,2018 -39,IMDB,imdb_wiki,37.26728,126.9841151,Seoul National University,edu,c43dc4ae68a317b34a79636fadb3bcc4d1ccb61c,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8369763,Age and gender estimation using deep residual learning network,2018 -40,IMDB,imdb_wiki,1.2962018,103.77689944,National University of Singapore,edu,5f94969b9491db552ffebc5911a45def99026afe,citation,https://pdfs.semanticscholar.org/5f94/969b9491db552ffebc5911a45def99026afe.pdf,Multimodal Learning and Reasoning for Visual Question Answering,2017 -41,IMDB,imdb_wiki,42.357757,-83.06286711,Wayne State University,edu,28d99dc2d673d62118658f8375b414e5192eac6f,citation,http://www.cs.wayne.edu/~mdong/cvpr17.pdf,Using Ranking-CNN for Age Estimation,2017 -42,IMDB,imdb_wiki,49.2767454,-122.91777375,Simon Fraser University,edu,975978ee6a32383d6f4f026b944099e7739e5890,citation,https://pdfs.semanticscholar.org/9759/78ee6a32383d6f4f026b944099e7739e5890.pdf,Privacy-Preserving Age Estimation for Content Rating,2018 -43,IMDB,imdb_wiki,49.8091536,-97.13304179,University of Manitoba,edu,975978ee6a32383d6f4f026b944099e7739e5890,citation,https://pdfs.semanticscholar.org/9759/78ee6a32383d6f4f026b944099e7739e5890.pdf,Privacy-Preserving Age Estimation for Content Rating,2018 -44,IMDB,imdb_wiki,43.66333345,-79.39769975,University of Toronto,edu,36a3a96ef54000a0cd63de867a5eb7e84396de09,citation,http://www.cs.toronto.edu/~guerzhoy/oriviz/crv17.pdf,Automatic Photo Orientation Detection with Convolutional Neural Networks,2017 -45,IMDB,imdb_wiki,31.32235655,121.38400941,Shanghai University,edu,5f0d4a0b5f72d8700cdf8cb179263a8fa866b59b,citation,https://pdfs.semanticscholar.org/5f0d/4a0b5f72d8700cdf8cb179263a8fa866b59b.pdf,Memo No . 85 06 / 2018 Deep Regression Forests for Age Estimation,2018 -46,IMDB,imdb_wiki,51.5247272,-0.03931035,Queen Mary University of London,edu,6cefb70f4668ee6c0bf0c18ea36fd49dd60e8365,citation,http://pdfs.semanticscholar.org/6cef/b70f4668ee6c0bf0c18ea36fd49dd60e8365.pdf,Privacy-Preserving Deep Inference for Rich User Data on The Cloud,2017 -47,IMDB,imdb_wiki,35.7036227,51.35125097,Sharif University of Technology,edu,6cefb70f4668ee6c0bf0c18ea36fd49dd60e8365,citation,http://pdfs.semanticscholar.org/6cef/b70f4668ee6c0bf0c18ea36fd49dd60e8365.pdf,Privacy-Preserving Deep Inference for Rich User Data on The Cloud,2017 -48,IMDB,imdb_wiki,51.99882735,4.37396037,Delft University of Technology,edu,dfbf941adeea19f5dff4a70a466ddd1b77f3b727,citation,https://pdfs.semanticscholar.org/dfbf/941adeea19f5dff4a70a466ddd1b77f3b727.pdf,Models for supervised learning in sequence data,2018 -49,IMDB,imdb_wiki,36.3697191,127.362537,Korea Advanced Institute of Science and Technology,edu,cb27b45329d61f5f95ed213798d4b2a615e76be2,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8329236,Deep Facial Age Estimation Using Conditional Multitask Learning With Weak Label Expansion,2018 -50,IMDB,imdb_wiki,37.2520226,127.0555019,"Samsung SAIT, Korea",company,cb27b45329d61f5f95ed213798d4b2a615e76be2,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8329236,Deep Facial Age Estimation Using Conditional Multitask Learning With Weak Label Expansion,2018 -51,IMDB,imdb_wiki,35.9042272,-78.85565763,"IBM Research, North Carolina",company,00a967cb2d18e1394226ad37930524a31351f6cf,citation,https://arxiv.org/pdf/1611.05377v1.pdf,Fully-Adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification,2017 -52,IMDB,imdb_wiki,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 -53,IMDB,imdb_wiki,-35.0636071,147.3552234,Charles Sturt University,edu,2e231f1e7e641dd3619bec59e14d02e91360ac01,citation,https://arxiv.org/pdf/1807.10421.pdf,Fusion Network for Face-Based Age Estimation,2018 -54,IMDB,imdb_wiki,51.3791442,-2.3252332,University of Bath,edu,2e231f1e7e641dd3619bec59e14d02e91360ac01,citation,https://arxiv.org/pdf/1807.10421.pdf,Fusion Network for Face-Based Age Estimation,2018 -55,IMDB,imdb_wiki,1.340216,103.965089,Singapore University of Technology and Design,edu,00823e6c0b6f1cf22897b8d0b2596743723ec51c,citation,https://arxiv.org/pdf/1708.07689.pdf,Understanding and Comparing Deep Neural Networks for Age and Gender Classification,2017 -56,IMDB,imdb_wiki,31.2284923,121.40211389,East China Normal University,edu,5364e58ba1f4cdfcffb247c2421e8f56a75fad8d,citation,https://doi.org/10.1109/VCIP.2017.8305113,Facial age estimation through self-paced learning,2017 -57,IMDB,imdb_wiki,61.44964205,23.85877462,Tampere University of Technology,edu,7f21a7441c6ded38008c1fd0b91bdd54425d3f80,citation,https://arxiv.org/pdf/1809.05474.pdf,Real Time System for Facial Analysis,2018 -58,IMDB,imdb_wiki,55.94951105,-3.19534913,University of Edinburgh,edu,f5fae7810a33ed67852ad6a3e0144cb278b24b41,citation,https://pdfs.semanticscholar.org/f5fa/e7810a33ed67852ad6a3e0144cb278b24b41.pdf,Multilingual Gender Classification with Multi-view Deep Learning: Notebook for PAN at CLEF 2018,2018 -59,IMDB,imdb_wiki,40.9153196,-73.1270626,Stony Brook University,edu,1190cba0cae3c8bb81bf80d6a0a83ae8c41240bc,citation,https://pdfs.semanticscholar.org/1190/cba0cae3c8bb81bf80d6a0a83ae8c41240bc.pdf,Squared Earth Mover ’ s Distance Loss for Training Deep Neural Networks on Ordered-Classes,2017 -60,IMDB,imdb_wiki,26.88111275,112.62850666,Hunan University,edu,86d0127e1fd04c3d8ea78401c838af621647dc95,citation,https://arxiv.org/pdf/1804.02810.pdf,A Novel Multi-Task Tensor Correlation Neural Network for Facial Attribute Prediction,2018 -61,IMDB,imdb_wiki,28.2290209,112.99483204,"National University of Defense Technology, China",edu,86d0127e1fd04c3d8ea78401c838af621647dc95,citation,https://arxiv.org/pdf/1804.02810.pdf,A Novel Multi-Task Tensor Correlation Neural Network for Facial Attribute Prediction,2018 -62,IMDB,imdb_wiki,29.58333105,-98.61944505,University of Texas at San Antonio,edu,86d0127e1fd04c3d8ea78401c838af621647dc95,citation,https://arxiv.org/pdf/1804.02810.pdf,A Novel Multi-Task Tensor Correlation Neural Network for Facial Attribute Prediction,2018 -63,IMDB,imdb_wiki,22.42031295,114.20788644,Chinese University of Hong Kong,edu,d80a3d1f3a438e02a6685e66ee908446766fefa9,citation,https://arxiv.org/pdf/1708.09687.pdf,Quantifying Facial Age by Posterior of Age Comparisons,2017 -64,IMDB,imdb_wiki,51.49887085,-0.17560797,Imperial College London,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017 -65,IMDB,imdb_wiki,51.59029705,-0.22963221,Middlesex University,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017 -66,IMDB,imdb_wiki,40.00229045,116.32098908,Tsinghua University,edu,493c8591d6a1bef5d7b84164a73761cefb9f5a25,citation,http://dl.acm.org/citation.cfm?id=3159691,User Profiling through Deep Multimodal Fusion,2018 -67,IMDB,imdb_wiki,47.6543238,-122.30800894,University of Washington,edu,493c8591d6a1bef5d7b84164a73761cefb9f5a25,citation,http://dl.acm.org/citation.cfm?id=3159691,User Profiling through Deep Multimodal Fusion,2018 -68,IMDB,imdb_wiki,30.44235995,-84.29747867,Florida State University,edu,b8c08c1330779283b3fbf06d133faf8bd55ea941,citation,https://arxiv.org/pdf/1803.11521.pdf,Online Regression with Feature Selection in Stochastic Data Streams,2018 -69,IMDB,imdb_wiki,30.44235995,-84.29747867,Florida State University,edu,1cfca6b71b0ead87bbb79a8614ddec3a10100faa,citation,https://arxiv.org/pdf/1809.05465.pdf,Are screening methods useful in feature selection? An empirical study,2018 -70,IMDB,imdb_wiki,51.49887085,-0.17560797,Imperial College London,edu,a06b6d30e2b31dc600f622ab15afe5e2929581a7,citation,https://ibug.doc.ic.ac.uk/media/uploads/documents/2209.pdf,Robust Joint and Individual Variance Explained,2017 -71,IMDB,imdb_wiki,51.59029705,-0.22963221,Middlesex University,edu,a06b6d30e2b31dc600f622ab15afe5e2929581a7,citation,https://ibug.doc.ic.ac.uk/media/uploads/documents/2209.pdf,Robust Joint and Individual Variance Explained,2017 -72,IMDB,imdb_wiki,32.0575279,118.78682252,Southeast University,edu,8ff8c64288a2f7e4e8bf8fda865820b04ab3dbe8,citation,https://pdfs.semanticscholar.org/0056/92b9fa6728df3a7f14578c43410867bba425.pdf,Age Estimation Using Expectation of Label Distribution Learning,2018 -73,IMDB,imdb_wiki,32.0565957,118.77408833,Nanjing University,edu,8ff8c64288a2f7e4e8bf8fda865820b04ab3dbe8,citation,https://pdfs.semanticscholar.org/0056/92b9fa6728df3a7f14578c43410867bba425.pdf,Age Estimation Using Expectation of Label Distribution Learning,2018 -74,IMDB,imdb_wiki,58.38131405,26.72078081,University of Tartu,edu,1b248ed8e7c9514648cd598960fadf9ab17e7fe8,citation,https://pdfs.semanticscholar.org/1b24/8ed8e7c9514648cd598960fadf9ab17e7fe8.pdf,"From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation",0 -75,IMDB,imdb_wiki,41.3868913,2.16352385,University of Barcelona,edu,1b248ed8e7c9514648cd598960fadf9ab17e7fe8,citation,https://pdfs.semanticscholar.org/1b24/8ed8e7c9514648cd598960fadf9ab17e7fe8.pdf,"From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation",0 -76,IMDB,imdb_wiki,35.9542493,-83.9307395,University of Tennessee,edu,7fab17ef7e25626643f1d55257a3e13348e435bd,citation,https://arxiv.org/pdf/1702.08423.pdf,Age Progression/Regression by Conditional Adversarial Autoencoder,2017 -77,IMDB,imdb_wiki,37.4102193,-122.05965487,Carnegie Mellon University,edu,ec05078be14a11157ac0e1c6b430ac886124589b,citation,http://pdfs.semanticscholar.org/ec05/078be14a11157ac0e1c6b430ac886124589b.pdf,Longitudinal Face Aging in the Wild - Recent Deep Learning Approaches,2018 -78,IMDB,imdb_wiki,45.57022705,-122.63709346,Concordia University,edu,ec05078be14a11157ac0e1c6b430ac886124589b,citation,http://pdfs.semanticscholar.org/ec05/078be14a11157ac0e1c6b430ac886124589b.pdf,Longitudinal Face Aging in the Wild - Recent Deep Learning Approaches,2018 -79,IMDB,imdb_wiki,40.00229045,116.32098908,Tsinghua University,edu,2149d49c84a83848d6051867290d9c8bfcef0edb,citation,https://doi.org/10.1109/TIFS.2017.2746062,Label-Sensitive Deep Metric Learning for Facial Age Estimation,2018 -80,IMDB,imdb_wiki,42.36782045,-71.12666653,Harvard University,edu,0ba402af3b8682e2aa89f76bd823ddffdf89fa0a,citation,http://pdfs.semanticscholar.org/c0d8/4377168c554cb8e83099bed940091fe49dec.pdf,Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks,2016 -81,IMDB,imdb_wiki,40.9153196,-73.1270626,Stony Brook University,edu,0ba402af3b8682e2aa89f76bd823ddffdf89fa0a,citation,http://pdfs.semanticscholar.org/c0d8/4377168c554cb8e83099bed940091fe49dec.pdf,Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks,2016 -82,IMDB,imdb_wiki,46.0658836,11.1159894,University of Trento,edu,df31e9c882dfb3ea5a3abe3b139ceacb1d90a302,citation,https://arxiv.org/pdf/1808.09211.pdf,DeepGUM: Learning Deep Robust Regression with a Gaussian-Uniform Mixture Model,2018 -83,IMDB,imdb_wiki,51.7534538,-1.25400997,University of Oxford,edu,523854a7d8755e944bd50217c14481fe1329a969,citation,https://arxiv.org/pdf/1808.00380.pdf,A Differentially Private Kernel Two-Sample Test,2018 -84,IMDB,imdb_wiki,51.49887085,-0.17560797,Imperial College London,edu,9b0489f2d5739213ef8c3e2e18739c4353c3a3b7,citation,http://pdfs.semanticscholar.org/9b04/89f2d5739213ef8c3e2e18739c4353c3a3b7.pdf,Visual Data Augmentation through Learning,2018 -85,IMDB,imdb_wiki,51.59029705,-0.22963221,Middlesex University,edu,9b0489f2d5739213ef8c3e2e18739c4353c3a3b7,citation,http://pdfs.semanticscholar.org/9b04/89f2d5739213ef8c3e2e18739c4353c3a3b7.pdf,Visual Data Augmentation through Learning,2018 -86,IMDB,imdb_wiki,40.00229045,116.32098908,Tsinghua University,edu,51f626540860ad75b68206025a45466a6d087aa6,citation,https://doi.org/10.1109/ICIP.2017.8296595,Cluster convolutional neural networks for facial age estimation,2017 -87,IMDB,imdb_wiki,49.2593879,-122.9151893,"AltumView Systems Inc., Burnaby, BC, Canada",company,b44f03b5fa8c6275238c2d13345652e6ff7e6ea9,citation,https://doi.org/10.1109/GlobalSIP.2017.8309138,Lapped convolutional neural networks for embedded systems,2017 -88,IMDB,imdb_wiki,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 -89,IMDB,imdb_wiki,22.15263985,113.56803206,Macau University of Science and Technology,edu,56f231fc40424ed9a7c93cbc9f5a99d022e1d242,citation,http://pdfs.semanticscholar.org/d060/f2f3641c6a89ade021eea749414a5c6b443f.pdf,Age Estimation Based on a Single Network with Soft Softmax of Aging Modeling,2016 -90,IMDB,imdb_wiki,40.0044795,116.370238,Chinese Academy of Sciences,edu,56f231fc40424ed9a7c93cbc9f5a99d022e1d242,citation,http://pdfs.semanticscholar.org/d060/f2f3641c6a89ade021eea749414a5c6b443f.pdf,Age Estimation Based on a Single Network with Soft Softmax of Aging Modeling,2016 -91,IMDB,imdb_wiki,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,56f231fc40424ed9a7c93cbc9f5a99d022e1d242,citation,http://pdfs.semanticscholar.org/d060/f2f3641c6a89ade021eea749414a5c6b443f.pdf,Age Estimation Based on a Single Network with Soft Softmax of Aging Modeling,2016 -92,IMDB,imdb_wiki,43.614386,7.071125,EURECOM,edu,1648cf24c042122af2f429641ba9599a2187d605,citation,https://doi.org/10.1109/BTAS.2017.8272698,Boosting cross-age face verification via generative age normalization,2017 -93,IMDB,imdb_wiki,21.003952,105.84360183,Hanoi University of Science and Technology,edu,ca37933b6297cdca211aa7250cbe6b59f8be40e5,citation,http://doi.acm.org/10.1145/3155133.3155207,"Multi-task learning for smile detection, emotion recognition and gender classification",2017 -94,IMDB,imdb_wiki,51.49887085,-0.17560797,Imperial College London,edu,cf2002fac81ccdccdadb5cc43f7b1cd30882d2c2,citation,https://arxiv.org/pdf/1803.09546.pdf,Calibrated Prediction Intervals for Neural Network Regressors,2018 -95,IMDB,imdb_wiki,51.7534538,-1.25400997,University of Oxford,edu,75f9d3533f175943e33c9155f4038488f32a24bc,citation,https://arxiv.org/pdf/1811.06817.pdf,Evaluating Uncertainty Quantification in End-to-End Autonomous Driving Control,2018 -96,IMDB,imdb_wiki,32.8536333,-117.2035286,Kyung Hee University,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7903648,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017 -97,IMDB,imdb_wiki,24.7246403,46.62335012,King Saud University,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7903648,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017 -98,IMDB,imdb_wiki,23.7289899,90.3982682,Institute of Information Technology,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7903648,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017 -99,IMDB,imdb_wiki,28.5456282,77.2731505,"IIIT Delhi, India",edu,f726738954e7055bb3615fa7e8f59f136d3e0bdc,citation,https://arxiv.org/pdf/1803.07385.pdf,Are you eligible? Predicting adulthood from face images via class specific mean autoencoder,2018 -100,IMDB,imdb_wiki,42.0551164,-87.67581113,Northwestern University,edu,c1586ee25e660f31cba0ca9ba5bf39ffcc020aab,citation,https://arxiv.org/pdf/1807.06708.pdf,A Modulation Module for Multi-task Learning with Applications in Image Retrieval,2018 -101,IMDB,imdb_wiki,37.4102193,-122.05965487,Carnegie Mellon University,edu,c1586ee25e660f31cba0ca9ba5bf39ffcc020aab,citation,https://arxiv.org/pdf/1807.06708.pdf,A Modulation Module for Multi-task Learning with Applications in Image Retrieval,2018 -102,IMDB,imdb_wiki,30.04287695,31.23664139,American University in Cairo,edu,3a2c90e0963bfb07fc7cd1b5061383e9a99c39d2,citation,https://arxiv.org/pdf/1710.03804.pdf,End-to-End Deep Learning for Steering Autonomous Vehicles Considering Temporal Dependencies,2017 -103,IMDB,imdb_wiki,31.83907195,117.26420748,University of Science and Technology of China,edu,47cd161546c59ab1e05f8841b82e985f72e5ddcb,citation,https://doi.org/10.1109/ICIP.2017.8296552,Gender classification in live videos,2017 -104,IMDB,imdb_wiki,39.2899685,-76.62196103,University of Maryland,edu,1491d0938bb4183bd19f2fee3b61997e1918160d,citation,https://arxiv.org/pdf/1807.00453.pdf,Elastic Neural Networks: A Scalable Framework for Embedded Computer Vision,2018 -105,IMDB,imdb_wiki,30.44235995,-84.29747867,Florida State University,edu,b88bace97d214d279e3a2053ccff0b6425295708,citation,https://arxiv.org/pdf/1803.11521.pdf,A Novel Framework for Online Supervised Learning with Feature Selection,2018 -106,IMDB,imdb_wiki,61.44964205,23.85877462,Tampere University of Technology,edu,b20cfbb2348984b4e25b6b9174f3c7b65b6aed9e,citation,http://pdfs.semanticscholar.org/b20c/fbb2348984b4e25b6b9174f3c7b65b6aed9e.pdf,Learning with Ambiguous Label Distribution for Apparent Age Estimation,2016 -107,IMDB,imdb_wiki,39.9082804,116.2458527,University of Chinese Academy of 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Sciences,edu,3d89f9b4da3d6fb1fdb33dea7592b5992069a096,citation,https://doi.org/10.1109/CISP-BMEI.2017.8302003,Face recognition based on convolution siamese networks,2017 -86,LFW,lfw,25.01682835,121.53846924,National Taiwan University,edu,e51927b125640bfc47bbf1aa00c3c026748c75bd,citation,http://doi.acm.org/10.1145/2647868.2655015,Automatic Facial Image Annotation and Retrieval by Integrating Voice Label and Visual Appearance,2014 -87,LFW,lfw,1.2962018,103.77689944,National University of Singapore,edu,4e8c608fc4b8198f13f8a68b9c1a0780f6f50105,citation,http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Yang_How_Related_Exemplars_2013_ICCV_paper.pdf,How Related Exemplars Help Complex Event Detection in Web Videos?,2013 -88,LFW,lfw,-27.49741805,153.01316956,University of Queensland,edu,4e8c608fc4b8198f13f8a68b9c1a0780f6f50105,citation,http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Yang_How_Related_Exemplars_2013_ICCV_paper.pdf,How Related Exemplars Help 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A Case for Facial Attribute Classification and Face Retrieval,2016 -120,LFW,lfw,50.7791703,6.06728733,RWTH Aachen University,edu,48906f609446afcdaacbe1d65770d7a6165a8eee,citation,https://doi.org/10.1007/s12559-017-9482-4,Storages Are Not Forever,2017 -121,LFW,lfw,37.43131385,-122.16936535,Stanford University,edu,48906f609446afcdaacbe1d65770d7a6165a8eee,citation,https://doi.org/10.1007/s12559-017-9482-4,Storages Are Not Forever,2017 -122,LFW,lfw,1.3484104,103.68297965,Nanyang Technological University,edu,48906f609446afcdaacbe1d65770d7a6165a8eee,citation,https://doi.org/10.1007/s12559-017-9482-4,Storages Are Not Forever,2017 -123,LFW,lfw,40.00229045,116.32098908,Tsinghua University,edu,31f905d40a4ac3c16c91d5be8427762fa91277f1,citation,https://doi.org/10.1109/TIP.2017.2704661,Learning Rotation-Invariant Local Binary Descriptor,2017 -124,LFW,lfw,51.0784038,-114.1287077,University of 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Kong,edu,4934d44aa89b6d871eb6709dd1d1eebf16f3aaf1,citation,http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Luo_A_Deep_Sum-Product_2013_ICCV_paper.pdf,A Deep Sum-Product Architecture for Robust Facial Attributes Analysis,2013 -128,LFW,lfw,30.19331415,120.11930822,Zhejiang University,edu,134f1cee8408cca648d8b4ca44b38b0a7023af71,citation,https://pdfs.semanticscholar.org/134f/1cee8408cca648d8b4ca44b38b0a7023af71.pdf,Partially Shared MultiTask Convolutional Neural Network with Local Constraint for Face Attribute Learning,0 -129,LFW,lfw,43.293621,5.358066,"Aix Marseille University, France",edu,d77f18917a58e7d4598d31af4e7be2762d858370,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6289062,Detecting person presence in TV shows with linguistic and structural features,2012 -130,LFW,lfw,48.754168,-3.4584586,"Orange Labs, Lannion, 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Telecommunications,edu,9729930ab0f9cbcd07f1105bc69c540330cda50a,citation,https://doi.org/10.1109/ACCESS.2017.2749331,Compressing Fisher Vector for Robust Face Recognition,2017 -158,LFW,lfw,39.65404635,-79.96475355,West Virginia University,edu,3b9b200e76a35178da940279d566bbb7dfebb787,citation,http://pdfs.semanticscholar.org/3b9b/200e76a35178da940279d566bbb7dfebb787.pdf,Learning Channel Inter-dependencies at Multiple Scales on Dense Networks for Face Recognition,2017 -159,LFW,lfw,42.3383668,-71.08793524,Northeastern University,edu,feea73095b1be0cbae1ad7af8ba2c4fb6f316d35,citation,http://dl.acm.org/citation.cfm?id=3126693,Deep Face Recognition with Center Invariant Loss,2017 -160,LFW,lfw,51.4584837,-2.6097752,University of Bristol,edu,54948ee407b5d32da4b2eee377cc44f20c3a7e0c,citation,https://arxiv.org/pdf/1806.06296.pdf,Right for the Right Reason: Training Agnostic Networks,2018 -161,LFW,lfw,32.0565957,118.77408833,Nanjing 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Sciences,edu,1eb9c859ff7537182a25556635954bcd11830822,citation,https://doi.org/10.1109/ICDSP.2015.7252004,Multi-features fusion based CRFs for face segmentation,2015 -165,LFW,lfw,31.32235655,121.38400941,Shanghai University,edu,1eb9c859ff7537182a25556635954bcd11830822,citation,https://doi.org/10.1109/ICDSP.2015.7252004,Multi-features fusion based CRFs for face segmentation,2015 -166,LFW,lfw,51.4584837,-2.6097752,University of Bristol,edu,3cd9b0a61bdfa1bb8a0a1bf0369515a76ecd06e3,citation,http://pdfs.semanticscholar.org/51f7/3cfcc6d671bd99b5c3c512ff9b7bb959f33b.pdf,Distance Metric Learning with Eigenvalue Optimization,2012 -167,LFW,lfw,50.7369302,-3.53647672,University of Exeter,edu,3cd9b0a61bdfa1bb8a0a1bf0369515a76ecd06e3,citation,http://pdfs.semanticscholar.org/51f7/3cfcc6d671bd99b5c3c512ff9b7bb959f33b.pdf,Distance Metric Learning with Eigenvalue Optimization,2012 -168,LFW,lfw,51.7534538,-1.25400997,University of Oxford,edu,f5aee1529b98136194ef80961ba1a6de646645fe,citation,http://pdfs.semanticscholar.org/f5ae/e1529b98136194ef80961ba1a6de646645fe.pdf,Large-scale learning of discriminative image representations,2013 -169,LFW,lfw,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 -170,LFW,lfw,39.94976005,116.33629046,Beijing Jiaotong University,edu,d40c16285d762f7a1c862b8ac05a0fdb24af1202,citation,https://doi.org/10.1109/BESC.2017.8256378,Coarse-to-fine facial landmarks localization based on convolutional feature,2017 -171,LFW,lfw,38.2167565,-85.75725023,University of Louisville,edu,780c8a795baca1ba4cb4956cded877dd3d1ca313,citation,http://doi.ieeecomputersociety.org/10.1109/ISSPIT.2013.6781879,Simulation of face recognition at a distance by scaling down images,2013 -172,LFW,lfw,44.8055716,-0.6051972,"Bordeaux INP, France",edu,c222f8079c246ead285894c47bdbb2dfc7741044,citation,https://doi.org/10.1109/ICIP.2015.7351631,Face de-identification with expressions preservation,2015 -173,LFW,lfw,44.808375,-0.596705,"University of Bordeaux, France",edu,c222f8079c246ead285894c47bdbb2dfc7741044,citation,https://doi.org/10.1109/ICIP.2015.7351631,Face de-identification with expressions preservation,2015 -174,LFW,lfw,1.2962018,103.77689944,National University of Singapore,edu,2f2aa67c5d6dbfaf218c104184a8c807e8b29286,citation,http://sesame.comp.nus.edu.sg/components/com_flexicontent/uploads/lekhaicon13.pdf,Video analytics for surveillance camera networks,2013 -175,LFW,lfw,45.7413921,126.62552755,Harbin Institute of Technology,edu,982d4f1dee188f662a4b5616a045d69fc5c21b54,citation,https://doi.org/10.1109/IJCNN.2016.7727859,Learning to link human objects in videos and advertisements with clothes retrieval,2016 -176,LFW,lfw,42.3383668,-71.08793524,Northeastern University,edu,982d4f1dee188f662a4b5616a045d69fc5c21b54,citation,https://doi.org/10.1109/IJCNN.2016.7727859,Learning to link human objects in videos and advertisements with clothes retrieval,2016 -177,LFW,lfw,16.46007565,102.81211798,Khon Kaen University,edu,81a80b26979b40d5ebe3f5ba70b03cb9f19dd7a5,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8369725,Histogram equalized deep PCA with ELM classification for expressive face recognition,2018 -178,LFW,lfw,37.2520226,127.0555019,"Samsung SAIT, Korea",company,86fa086d02f424705bbea53943390f009191740a,citation,https://doi.org/10.1109/ICIP.2015.7351651,Precise eye localization with improved SDM,2015 -179,LFW,lfw,39.9041999,116.4073963,"Samsung SAIT, Beijing",company,86fa086d02f424705bbea53943390f009191740a,citation,https://doi.org/10.1109/ICIP.2015.7351651,Precise eye localization with improved SDM,2015 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University,edu,7fc3442c8b4c96300ad3e860ee0310edb086de94,citation,http://pdfs.semanticscholar.org/82f3/b7cacc15e026fd3a7639091d54162f6ae064.pdf,Similarity Scores Based on Background Samples,2009 -184,LFW,lfw,37.43131385,-122.16936535,Stanford University,edu,42f6f5454dda99d8989f9814989efd50fe807ee8,citation,http://pdfs.semanticscholar.org/42f6/f5454dda99d8989f9814989efd50fe807ee8.pdf,Conditional generative adversarial nets for convolutional face generation,2015 -185,LFW,lfw,47.6423318,-122.1369302,Microsoft,company,0aebe97a92f590bdf21cdadfddec8061c682cdb2,citation,http://doi.ieeecomputersociety.org/10.1109/TPAMI.2017.2695183,Probabilistic Elastic Part Model: A Pose-Invariant Representation for Real-World Face Verification,2018 -186,LFW,lfw,22.2081469,114.25964115,University of Hong Kong,edu,7ffef9f26c39377ee937d29b8990580266a7a8a5,citation,https://arxiv.org/pdf/1810.06951.pdf,Deep Metric Learning with Hierarchical Triplet Loss,2018 -187,LFW,lfw,47.6543238,-122.30800894,University of Washington,edu,7862f646d640cbf9f88e5ba94a7d642e2a552ec9,citation,http://pdfs.semanticscholar.org/7862/f646d640cbf9f88e5ba94a7d642e2a552ec9.pdf,Being John Malkovich,2010 -188,LFW,lfw,41.10427915,29.02231159,Istanbul Technical University,edu,754626bd5fb06fee5e10962fdfeddd495513e84b,citation,https://doi.org/10.1109/SIU.2017.7960646,Facial expression pair matching,2017 -189,LFW,lfw,42.3383668,-71.08793524,Northeastern University,edu,e3c8e49ffa7beceffca3f7f276c27ae6d29b35db,citation,https://arxiv.org/pdf/1604.02182.pdf,Families in the Wild (FIW): Large-Scale Kinship Image Database and Benchmarks,2016 -190,LFW,lfw,55.94951105,-3.19534913,University of Edinburgh,edu,b306bd9b485c6a6c1e4550beb1910ed9b6585359,citation,https://pdfs.semanticscholar.org/b306/bd9b485c6a6c1e4550beb1910ed9b6585359.pdf,Learning generative models of mid-level structure in natural images,2012 -191,LFW,lfw,40.00229045,116.32098908,Tsinghua University,edu,aed6af12148b43e4a24ee6e2bc3604ca59bd99a5,citation,https://doi.org/10.1109/TIP.2017.2717505,Discriminative Deep Metric Learning for Face and Kinship Verification,2017 -192,LFW,lfw,1.3484104,103.68297965,Nanyang Technological University,edu,aed6af12148b43e4a24ee6e2bc3604ca59bd99a5,citation,https://doi.org/10.1109/TIP.2017.2717505,Discriminative Deep Metric Learning for Face and Kinship Verification,2017 -193,LFW,lfw,43.7743911,-79.50481085,York University,edu,ffe4bb47ec15f768e1744bdf530d5796ba56cfc1,citation,https://arxiv.org/pdf/1706.04277.pdf,AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces,2017 -194,LFW,lfw,27.18794105,31.17009498,Assiut University,edu,ffe4bb47ec15f768e1744bdf530d5796ba56cfc1,citation,https://arxiv.org/pdf/1706.04277.pdf,AFIF4: Deep Gender Classification based on AdaBoost-based Fusion of Isolated Facial Features and Foggy Faces,2017 -195,LFW,lfw,51.5231607,-0.1282037,University College 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Chinese Academy of Sciences,edu,0034e37a0faf0f71395245b266aacbf5412f190a,citation,https://doi.org/10.1109/TMM.2014.2355134,Face Distortion Recovery Based on Online Learning Database for Conversational Video,2014 -200,LFW,lfw,31.28473925,121.49694909,Tongji University,edu,fe0cf8eaa5a5f59225197ef1bb8613e603cd96d4,citation,https://pdfs.semanticscholar.org/4e20/8cfff33327863b5aeef0bf9b327798a5610c.pdf,Improved Face Verification with Simple Weighted Feature Combination,2017 -201,LFW,lfw,39.65404635,-79.96475355,West Virginia University,edu,e20e2db743e8db1ff61279f4fda32bf8cf381f8e,citation,https://arxiv.org/pdf/1801.01486.pdf,Deep Cross Polarimetric Thermal-to-Visible Face Recognition,2018 -202,LFW,lfw,32.198055,119.46326791,Jiangsu University of Science and Technology,edu,9b4d2cd2e5edbf5c8efddbdcce1db9a02a853534,citation,https://doi.org/10.1016/j.neucom.2016.02.063,Exponential Discriminant Locality Preserving Projection for face recognition,2016 -203,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,7783095a565094ae5b3dccf082d504ddd7255a5c,citation,http://dl.acm.org/citation.cfm?id=2502258,"""Wow! you are so beautiful today!""",2013 -204,LFW,lfw,1.2962018,103.77689944,National University of Singapore,edu,7783095a565094ae5b3dccf082d504ddd7255a5c,citation,http://dl.acm.org/citation.cfm?id=2502258,"""Wow! you are so beautiful today!""",2013 -205,LFW,lfw,22.3386304,114.2620337,Hong Kong University of Science and Technology,edu,fff31548617f208cd5ae5c32917afd48abc4ff6a,citation,http://doi.acm.org/10.1145/3139295.3139309,Mobile situated analytics of ego-centric network data,2017 -206,LFW,lfw,37.21872455,-80.42542519,Virginia Polytechnic Institute and State University,edu,0de1450369cb57e77ef61cd334c3192226e2b4c2,citation,https://doi.org/10.1109/BTAS.2017.8272747,"In defense of low-level structural features and SVMs for facial attribute classification: Application to detection of eye state, Mouth State, and eyeglasses in the wild",2017 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Learning a Symmetry Three-Player GAN for Identity-Preserving Face Synthesis,2018 -211,LFW,lfw,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,58d76380d194248b3bb291b8c7c5137a0a376897,citation,https://pdfs.semanticscholar.org/58d7/6380d194248b3bb291b8c7c5137a0a376897.pdf,FaceID-GAN : Learning a Symmetry Three-Player GAN for Identity-Preserving Face Synthesis,2018 -212,LFW,lfw,39.9808333,116.34101249,Beihang University,edu,5dd57b7e0e82a33420c054da7ea3f435d49e910e,citation,https://doi.org/10.1007/s10851-014-0493-4,Matching and Perturbation Theories for Affine-Invariant Shapes Using QR Factorization with Column Pivoting,2014 -213,LFW,lfw,30.527151,114.400762,China University of Geosciences,edu,110919f803740912e02bb7e1424373d325f558a9,citation,http://doi.acm.org/10.1145/3123266.3123421,Statistical Inference of Gaussian-Laplace Distribution for Person Verification,2017 -214,LFW,lfw,35.6924853,139.7582533,"National Institute of Informatics, Japan",edu,110919f803740912e02bb7e1424373d325f558a9,citation,http://doi.acm.org/10.1145/3123266.3123421,Statistical Inference of Gaussian-Laplace Distribution for Person Verification,2017 -215,LFW,lfw,30.60903415,114.3514284,Wuhan University of Technology,edu,110919f803740912e02bb7e1424373d325f558a9,citation,http://doi.acm.org/10.1145/3123266.3123421,Statistical Inference of Gaussian-Laplace Distribution for Person Verification,2017 -216,LFW,lfw,42.4505507,-76.4783513,Cornell University,edu,537328af75f50d49696972a6c34bca97c14bc762,citation,https://arxiv.org/pdf/1805.04049.pdf,Exploiting Unintended Feature Leakage in Collaborative Learning,2018 -217,LFW,lfw,43.47061295,-80.54724732,University of Waterloo,edu,9825c4dddeb2ed7eaab668b55403aa2c38bc3320,citation,https://arxiv.org/pdf/1807.09532.pdf,Aerial Imagery for Roof Segmentation: A Large-Scale Dataset towards Automatic Mapping of Buildings,2018 -218,LFW,lfw,35.9020448,139.93622009,University of Tokyo,edu,9825c4dddeb2ed7eaab668b55403aa2c38bc3320,citation,https://arxiv.org/pdf/1807.09532.pdf,Aerial Imagery for Roof Segmentation: A Large-Scale Dataset towards Automatic Mapping of Buildings,2018 -219,LFW,lfw,39.65404635,-79.96475355,West Virginia University,edu,7a65fc9e78eff3ab6062707deaadde024d2fad40,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/papers/Zhu_A_Study_on_ICCV_2015_paper.pdf,A Study on Apparent Age Estimation,2015 -220,LFW,lfw,29.5357046,106.60482474,Chongqing University of Posts and Telecommunications,edu,0750c796467b6ef60b0caff5fb199337d54d431e,citation,https://doi.org/10.1109/ICMLC.2016.7873015,Face detection method based on histogram of sparse code in tree deformable model,2016 -221,LFW,lfw,34.2375581,-77.9270129,University of North Carolina Wilmington,edu,0750c796467b6ef60b0caff5fb199337d54d431e,citation,https://doi.org/10.1109/ICMLC.2016.7873015,Face detection method based on histogram of sparse code in tree deformable 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Sciences,edu,d04d5692461d208dd5f079b98082eda887b62323,citation,http://www.cbsr.ia.ac.cn/users/zlei/papers/ICB2015/ZLEI-ICB-15.pdf,Subspace learning with frequency regularizer: Its application to face recognition,2015 -227,LFW,lfw,40.00229045,116.32098908,Tsinghua University,edu,e908ce44fa94bb7ecf2a8b70cb5ec0b1a00b311a,citation,http://doi.ieeecomputersociety.org/10.1109/ICME.2017.8019548,Topology preserving graph matching for partial face recognition,2017 -228,LFW,lfw,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,84574aa43a98ad8a29470977e7b091f5a5ec2366,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7301321,Latent max-margin metric learning for comparing video face tubes,2015 -229,LFW,lfw,48.831533,2.28066283,"Technicolor, France",edu,84574aa43a98ad8a29470977e7b091f5a5ec2366,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7301321,Latent max-margin metric learning for comparing video face tubes,2015 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Singapore",edu,6bfb0f8dd1a2c0b44347f09006dc991b8a08559c,citation,https://www.computer.org/web/csdl/index/-/csdl/proceedings/fg/2013/5545/00/06553724.pdf,Multiview discriminative learning for age-invariant face recognition,2013 -242,LFW,lfw,1.3484104,103.68297965,Nanyang Technological University,edu,6bfb0f8dd1a2c0b44347f09006dc991b8a08559c,citation,https://www.computer.org/web/csdl/index/-/csdl/proceedings/fg/2013/5545/00/06553724.pdf,Multiview discriminative learning for age-invariant face recognition,2013 -243,LFW,lfw,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,6bfb0f8dd1a2c0b44347f09006dc991b8a08559c,citation,https://www.computer.org/web/csdl/index/-/csdl/proceedings/fg/2013/5545/00/06553724.pdf,Multiview discriminative learning for age-invariant face recognition,2013 -244,LFW,lfw,-35.2776999,149.118527,Australian National University,edu,c58b7466f2855ffdcff1bebfad6b6a027b8c5ee1,citation,http://pdfs.semanticscholar.org/d6f1/42f5ddcb027e7b346eb20703abbf5cc4e883.pdf,Ultra-Resolving Face Images by Discriminative Generative Networks,2016 -245,LFW,lfw,52.2380139,6.8566761,University of Twente,edu,d8288322f32ee4501cef5a9b667e5bb79ebd7018,citation,https://doi.org/10.1016/j.patcog.2011.12.018,Facing scalability: Naming faces in an online social network,2012 -246,LFW,lfw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,de7f5e4ccc2f38e0c8f3f72a930ae1c43e0fdcf0,citation,https://arxiv.org/pdf/1707.03986.pdf,Merge or Not? Learning to Group Faces via Imitation Learning,2018 -247,LFW,lfw,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 -248,LFW,lfw,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,edb5813a32ce1167feb263ca2803d0ae934d902c,citation,https://arxiv.org/pdf/1807.08571.pdf,Invisible Steganography via Generative Adversarial Networks,2018 -249,LFW,lfw,38.99203005,-76.9461029,University of Maryland College Park,edu,7ca7255c2e0c86e4adddbbff2ce74f36b1dc522d,citation,https://pdfs.semanticscholar.org/7ca7/255c2e0c86e4adddbbff2ce74f36b1dc522d.pdf,Stereo Matching for Unconstrained Face Recognition Ph . D . Proposal,2009 -250,LFW,lfw,39.2899685,-76.62196103,University of Maryland,edu,7ca7255c2e0c86e4adddbbff2ce74f36b1dc522d,citation,https://pdfs.semanticscholar.org/7ca7/255c2e0c86e4adddbbff2ce74f36b1dc522d.pdf,Stereo Matching for Unconstrained Face Recognition Ph . D . Proposal,2009 -251,LFW,lfw,37.8687126,-122.25586815,"University of California, Berkeley",edu,53bfe2ab770e74d064303f3bd2867e5bf7b86379,citation,https://pdfs.semanticscholar.org/d989/c3064d49bf8e63587ada4ed2bdb0d32b120a.pdf,Learning to Synthesize and Manipulate Natural Images,2017 -252,LFW,lfw,40.4319722,-86.92389368,Purdue University,edu,1a46d3a9bc1e4aff0ccac6403b49a13c8a89fc1d,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2012.6247878,Online robust image alignment via iterative convex optimization,2012 -253,LFW,lfw,32.0565957,118.77408833,Nanjing University,edu,1a46d3a9bc1e4aff0ccac6403b49a13c8a89fc1d,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2012.6247878,Online robust image alignment via iterative convex optimization,2012 -254,LFW,lfw,39.95472495,-75.15346905,Temple University,edu,1a46d3a9bc1e4aff0ccac6403b49a13c8a89fc1d,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2012.6247878,Online robust image alignment via iterative convex optimization,2012 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University,edu,39f525f3a0475e6bbfbe781ae3a74aca5b401125,citation,http://pdfs.semanticscholar.org/39f5/25f3a0475e6bbfbe781ae3a74aca5b401125.pdf,Deep Joint Face Hallucination and Recognition,2016 -259,LFW,lfw,40.00229045,116.32098908,Tsinghua University,edu,88cd4209db62a34d9cba0b9cbe9d45d1e57d21e5,citation,https://pdfs.semanticscholar.org/88cd/4209db62a34d9cba0b9cbe9d45d1e57d21e5.pdf,Runtime Neural Pruning,2017 -260,LFW,lfw,-34.9181706,-56.1665725,"Universidad de la República, Uruguay",edu,3b75681f0162752865d85befd8b15e7d954ebfe6,citation,https://doi.org/10.1109/CLEI.2014.6965097,Evaluation of a face recognition system performance's variation on a citizen passports database,2014 -261,LFW,lfw,45.5039761,-73.5749687,McGill University,edu,ed9d11e995baeec17c5d2847ec1a8d5449254525,citation,https://pdfs.semanticscholar.org/ed9d/11e995baeec17c5d2847ec1a8d5449254525.pdf,Efficient Gender Classification Using a Deep LDA-Pruned Net,2017 -262,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,21d1315761131ea6b3e2afe7a745b606341616fd,citation,https://pdfs.semanticscholar.org/21d1/315761131ea6b3e2afe7a745b606341616fd.pdf,Generative Adversarial Network with Spatial Attention for Face Attribute Editing,2018 -263,LFW,lfw,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,21d1315761131ea6b3e2afe7a745b606341616fd,citation,https://pdfs.semanticscholar.org/21d1/315761131ea6b3e2afe7a745b606341616fd.pdf,Generative Adversarial Network with Spatial Attention for Face Attribute Editing,2018 -264,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,13a994d489c15d440c1238fc1ac37dad06dd928c,citation,http://pdfs.semanticscholar.org/13a9/94d489c15d440c1238fc1ac37dad06dd928c.pdf,Learning Discriminant Face Descriptor for Face Recognition,2012 -265,LFW,lfw,33.776033,-84.39884086,Georgia Institute of Technology,edu,96f0e7416994035c91f4e0dfa40fd45090debfc5,citation,https://arxiv.org/pdf/1803.01260.pdf,Unsupervised Learning of Face Representations,2018 -266,LFW,lfw,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 -267,LFW,lfw,37.52914535,45.04886077,Urmia University,edu,d2f2b10a8f29165d815e652f8d44955a12d057e6,citation,http://doi.org/10.1007/s10044-015-0475-1,Multiscale binarised statistical image features for symmetric face matching using multiple descriptor fusion based on class-specific LDA,2015 -268,LFW,lfw,32.77824165,34.99565673,Open University of Israel,edu,582edc19f2b1ab2ac6883426f147196c8306685a,citation,http://pdfs.semanticscholar.org/be6c/db7b181e73f546d43cf2ab6bc7181d7d619b.pdf,Do We Really Need to Collect Millions of Faces for Effective Face Recognition?,2016 -269,LFW,lfw,30.19331415,120.11930822,Zhejiang University,edu,2d3c17ced03e4b6c4b014490fe3d40c62d02e914,citation,http://pdfs.semanticscholar.org/2d3c/17ced03e4b6c4b014490fe3d40c62d02e914.pdf,Video-driven 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for unconstrained face verification,2012 -274,LFW,lfw,39.8720489,32.75395155,Bilkent University,edu,955e2a39f51c0b6f967199942d77625009e580f9,citation,http://pdfs.semanticscholar.org/955e/2a39f51c0b6f967199942d77625009e580f9.pdf,Naming Faces on the Web,2010 -275,LFW,lfw,45.7413921,126.62552755,Harbin Institute of Technology,edu,fdd19fee07f2404952e629cc7f7ffaac14febe01,citation,https://doi.org/10.1109/CISP-BMEI.2016.7852754,Face recognition based on dictionary learning with the locality constraints of atoms,2016 -276,LFW,lfw,23.131707,113.371643,Guangdong Polytechnic Normal University,edu,fdd19fee07f2404952e629cc7f7ffaac14febe01,citation,https://doi.org/10.1109/CISP-BMEI.2016.7852754,Face recognition based on dictionary learning with the locality constraints of atoms,2016 -277,LFW,lfw,42.3889785,-72.5286987,University of Massachusetts,edu,e39a66a6d1c5e753f8e6c33cd5d335f9bc9c07fa,citation,https://pdfs.semanticscholar.org/e39a/66a6d1c5e753f8e6c33cd5d335f9bc9c07fa.pdf,Weakly Supervised Learning for Unconstrained Face Processing,2014 -278,LFW,lfw,42.718568,-84.47791571,Michigan State University,edu,051f03bc25ec633592aa2ff5db1d416b705eac6c,citation,http://www.cse.msu.edu/biometrics/Publications/Face/LiaoJain_PartialFR_AlignmentFreeApproach_ICJB11.pdf,Partial face recognition: An alignment free approach,2011 -279,LFW,lfw,31.846918,117.29053367,Hefei University of Technology,edu,f6e6b4d0b7c16112dcb71ff502033a2187b1ec9b,citation,https://doi.org/10.1109/TMM.2015.2476657,Understanding Blooming Human Groups in Social Networks,2015 -280,LFW,lfw,29.58333105,-98.61944505,University of Texas at San Antonio,edu,f6e6b4d0b7c16112dcb71ff502033a2187b1ec9b,citation,https://doi.org/10.1109/TMM.2015.2476657,Understanding Blooming Human Groups in Social Networks,2015 -281,LFW,lfw,1.2962018,103.77689944,National University of Singapore,edu,f6e6b4d0b7c16112dcb71ff502033a2187b1ec9b,citation,https://doi.org/10.1109/TMM.2015.2476657,Understanding Blooming Human Groups in Social Networks,2015 -282,LFW,lfw,39.9808333,116.34101249,Beihang University,edu,a961f1234e963a7945fed70197015678149b37d8,citation,http://dl.acm.org/citation.cfm?id=3206068,Facial Expression Synthesis by U-Net Conditional Generative Adversarial Networks,2018 -283,LFW,lfw,34.2152538,117.1398541,China University of Mining and Technology,edu,660c99ac408b535bb0468ab3708d0d1d5db30180,citation,http://doi.org/10.1007/s11042-015-3083-6,An improved redundant dictionary based on sparse representation for face recognition,2015 -284,LFW,lfw,1.2988926,103.7873107,"A*STAR, Singapore",edu,c444c4dab97dd6d6696f56c1cacda051dde60448,citation,http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.37,Multiview Face Detection and Registration Requiring Minimal Manual Intervention,2013 -285,LFW,lfw,1.3484104,103.68297965,Nanyang Technological University,edu,c444c4dab97dd6d6696f56c1cacda051dde60448,citation,http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.37,Multiview Face Detection and Registration Requiring Minimal Manual Intervention,2013 -286,LFW,lfw,28.3656193,75.5834953,"Central Electronics Research Institute, Pilani, India",edu,1aeef2ab062c27e0dbba481047e818d4c471ca57,citation,https://doi.org/10.1109/ICACCI.2015.7275860,Analyzing impact of image scaling algorithms on viola-jones face detection framework,2015 -287,LFW,lfw,34.0224149,-118.28634407,University of Southern California,edu,d6ae7941dcec920d5726d50d1b1cdfe4dde34d35,citation,http://dl.acm.org/citation.cfm?id=31310887,Avatar digitization from a single image for real-time rendering,2017 -288,LFW,lfw,28.54632595,77.27325504,Indian Institute of Technology Delhi,edu,fba95853ca3135cc52a4b2bc67089041c2a9408c,citation,https://pdfs.semanticscholar.org/fba9/5853ca3135cc52a4b2bc67089041c2a9408c.pdf,Disguised Faces in the Wild,2018 -289,LFW,lfw,39.2899685,-76.62196103,University of Maryland,edu,fba95853ca3135cc52a4b2bc67089041c2a9408c,citation,https://pdfs.semanticscholar.org/fba9/5853ca3135cc52a4b2bc67089041c2a9408c.pdf,Disguised Faces in the Wild,2018 -290,LFW,lfw,44.808375,-0.596705,University of Bordeaux,edu,4512b87d68458d9ba0956c0f74b60371b6c69df4,citation,https://doi.org/10.1109/TIP.2017.2708504,SuperPatchMatch: An Algorithm for Robust Correspondences Using Superpixel Patches,2017 -291,LFW,lfw,38.99203005,-76.9461029,University of Maryland College Park,edu,38a9ca2c49a77b540be52377784b9f734e0417e4,citation,http://homepages.dcc.ufmg.br/~william/papers/paper_2011_IJCB_Faces.pdf,Face verification using large feature sets and one shot similarity,2011 -292,LFW,lfw,-27.5953995,-48.6154218,University of Campinas,edu,38a9ca2c49a77b540be52377784b9f734e0417e4,citation,http://homepages.dcc.ufmg.br/~william/papers/paper_2011_IJCB_Faces.pdf,Face verification using large feature sets and one shot similarity,2011 -293,LFW,lfw,39.2899685,-76.62196103,University of Maryland,edu,38a9ca2c49a77b540be52377784b9f734e0417e4,citation,http://homepages.dcc.ufmg.br/~william/papers/paper_2011_IJCB_Faces.pdf,Face verification using large feature sets and one shot similarity,2011 -294,LFW,lfw,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,ac2881bdf7b57dc1672a17b221d68a438d79fce8,citation,https://arxiv.org/pdf/1806.08472.pdf,Learning a High Fidelity Pose Invariant Model for High-resolution Face Frontalization,2018 -295,LFW,lfw,46.0501558,14.46907327,University of Ljubljana,edu,69adbfa7b0b886caac15ebe53b89adce390598a3,citation,https://arxiv.org/pdf/1805.10938.pdf,Face hallucination using cascaded super-resolution and identity priors,2018 -296,LFW,lfw,41.70456775,-86.23822026,University of Notre Dame,edu,69adbfa7b0b886caac15ebe53b89adce390598a3,citation,https://arxiv.org/pdf/1805.10938.pdf,Face hallucination using cascaded super-resolution and identity priors,2018 -297,LFW,lfw,-33.8840504,151.1992254,University of Technology,edu,8686b15802529ff8aea50995ef14079681788110,citation,https://doi.org/10.1109/TNNLS.2014.2376936,Deformed Graph Laplacian for Semisupervised Learning,2015 -298,LFW,lfw,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,8686b15802529ff8aea50995ef14079681788110,citation,https://doi.org/10.1109/TNNLS.2014.2376936,Deformed Graph Laplacian for Semisupervised Learning,2015 -299,LFW,lfw,35.9020448,139.93622009,University of Tokyo,edu,9f131b4e036208f2402182a1af2a59e3c5d7dd44,citation,http://dl.acm.org/citation.cfm?id=3206038,Face Retrieval Framework Relying on User's Visual Memory,2018 -300,LFW,lfw,33.8898728,130.70856205,Waseda University,edu,9f131b4e036208f2402182a1af2a59e3c5d7dd44,citation,http://dl.acm.org/citation.cfm?id=3206038,Face Retrieval Framework Relying on User's Visual Memory,2018 -301,LFW,lfw,47.6543238,-122.30800894,University of Washington,edu,09ce14b84af2dc2f76ae1cf227356fa0ba337d07,citation,http://grail.cs.washington.edu/3dfaces/paper.pdf,Face reconstruction in the wild,2011 -302,LFW,lfw,13.0222347,77.56718325,Indian Institute of Science Bangalore,edu,d79365336115661b0e8dbbcd4b2aa1f504b91af6,citation,https://arxiv.org/pdf/1603.01801.pdf,Variational methods for conditional multimodal deep learning,2017 -303,LFW,lfw,31.30104395,121.50045497,Fudan University,edu,7df4f96138a4e23492ea96cf921794fc5287ba72,citation,https://arxiv.org/pdf/1707.08705.pdf,A Jointly Learned Deep Architecture for Facial Attribute Analysis and Face Detection in the Wild,2017 -304,LFW,lfw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4,citation,http://arxiv.org/pdf/1411.7766v2.pdf,Deep Learning Face Attributes in the Wild,2015 -305,LFW,lfw,23.143197,113.34009651,South China Normal University,edu,dc6ad30c7a4bc79bb06b4725b16e202d3d7d8935,citation,http://doi.org/10.1007/s11042-017-4646-5,Age classification with deep learning face representation,2017 -306,LFW,lfw,23.0502042,113.39880323,South China University of Technology,edu,dc6ad30c7a4bc79bb06b4725b16e202d3d7d8935,citation,http://doi.org/10.1007/s11042-017-4646-5,Age classification with deep learning face representation,2017 -307,LFW,lfw,32.0565957,118.77408833,Nanjing University,edu,bbcb4920b312da201bf4d2359383fb4ee3b17ed9,citation,http://pdfs.semanticscholar.org/bbcb/4920b312da201bf4d2359383fb4ee3b17ed9.pdf,Robust Face Recognition via Multi-Scale Patch-Based Matrix Regression,2016 -308,LFW,lfw,32.0575279,118.78682252,Southeast University,edu,feb6e267923868bff6e2108603d00fdfd65251ca,citation,http://pdfs.semanticscholar.org/feb6/e267923868bff6e2108603d00fdfd65251ca.pdf,Unsupervised Discovery of Visual Face Categories,2013 -309,LFW,lfw,24.7246403,46.62335012,King Saud University,edu,feb6e267923868bff6e2108603d00fdfd65251ca,citation,http://pdfs.semanticscholar.org/feb6/e267923868bff6e2108603d00fdfd65251ca.pdf,Unsupervised Discovery of Visual Face Categories,2013 -310,LFW,lfw,39.5469449,-119.81346566,University of Nevada,edu,feb6e267923868bff6e2108603d00fdfd65251ca,citation,http://pdfs.semanticscholar.org/feb6/e267923868bff6e2108603d00fdfd65251ca.pdf,Unsupervised Discovery of Visual Face Categories,2013 -311,LFW,lfw,42.36782045,-71.12666653,Harvard University,edu,8f6263e4d3775757e804796e104631c7a2bb8679,citation,http://pdfs.semanticscholar.org/8f62/63e4d3775757e804796e104631c7a2bb8679.pdf,Characterizing Visual Representations within Convolutional Neural Networks: Toward a Quantitative Approach,2016 -312,LFW,lfw,53.406179,-2.96670819,University of Liverpool,edu,2ab034e1f54c37bfc8ae93f7320160748310dc73,citation,https://arxiv.org/pdf/1805.07242.pdf,Siamese Capsule Networks,2018 -313,LFW,lfw,44.4962318,11.354157,University of Bologna,edu,1d1a7ef193b958f9074f4f236060a5f5e7642fc1,citation,http://pdfs.semanticscholar.org/db40/804914afbb7f8279ca9a4f52e0ade695f19e.pdf,Ensemble of Patterns of Oriented Edge Magnitudes Descriptors For Face Recognition,2013 -314,LFW,lfw,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,36486944b4feeb88c0499fecd253c5a53034a23f,citation,https://doi.org/10.1109/CISP-BMEI.2017.8301986,Deep feature selection and projection for cross-age face retrieval,2017 -315,LFW,lfw,49.443232,1.099971,"IRSEEM Rouen, France",edu,e7436b8e68bb7139b823a7572af3decd96241e78,citation,https://doi.org/10.1109/ROBIO.2011.6181560,A new approach for face detection with omnidirectional sensors,2011 -316,LFW,lfw,49.3849757,1.0683257,"University of Rouen, France",edu,e7436b8e68bb7139b823a7572af3decd96241e78,citation,https://doi.org/10.1109/ROBIO.2011.6181560,A new approach for face detection with omnidirectional sensors,2011 -317,LFW,lfw,48.8476037,2.2639934,"Université Paris-Saclay, France",edu,96e318f8ff91ba0b10348d4de4cb7c2142eb8ba9,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8364450,State-of-the-art face recognition performance using publicly available software and datasets,2018 -318,LFW,lfw,40.00229045,116.32098908,Tsinghua University,edu,e8523c4ac9d7aa21f3eb4062e09f2a3bc1eedcf7,citation,https://arxiv.org/pdf/1701.07174.pdf,Toward End-to-End Face Recognition Through Alignment Learning,2017 -319,LFW,lfw,47.6543238,-122.30800894,University of Washington,edu,405526dfc79de98f5bf3c97bf4aa9a287700f15d,citation,http://pdfs.semanticscholar.org/8a6c/57fcd99a77982ec754e0b97fd67519ccb60c.pdf,MegaFace: A Million Faces for Recognition at Scale,2015 -320,LFW,lfw,42.4505507,-76.4783513,Cornell University,edu,053b263b4a4ccc6f9097ad28ebf39c2957254dfb,citation,http://pdfs.semanticscholar.org/7a49/4b4489408ec3adea15817978ecd2e733f5fe.pdf,Cost-Effective HITs for Relative Similarity Comparisons,2014 -321,LFW,lfw,32.87935255,-117.23110049,"University of California, San Diego",edu,053b263b4a4ccc6f9097ad28ebf39c2957254dfb,citation,http://pdfs.semanticscholar.org/7a49/4b4489408ec3adea15817978ecd2e733f5fe.pdf,Cost-Effective HITs for Relative Similarity Comparisons,2014 -322,LFW,lfw,37.4102193,-122.05965487,Carnegie Mellon University,edu,82e66c4832386cafcec16b92ac88088ffd1a1bc9,citation,http://pdfs.semanticscholar.org/82e6/6c4832386cafcec16b92ac88088ffd1a1bc9.pdf,OpenFace: A general-purpose face recognition library with mobile applications,2016 -323,LFW,lfw,52.4004837,16.95158083,Poznan University of Technology,edu,82e66c4832386cafcec16b92ac88088ffd1a1bc9,citation,http://pdfs.semanticscholar.org/82e6/6c4832386cafcec16b92ac88088ffd1a1bc9.pdf,OpenFace: A general-purpose face recognition library with mobile applications,2016 -324,LFW,lfw,50.89273635,-1.39464295,University of Southampton,edu,8bbbdff11e88327816cad3c565f4ab1bb3ee20db,citation,http://doi.ieeecomputersociety.org/10.1109/FG.2017.31,Automatic Semantic Face Recognition,2017 -325,LFW,lfw,42.9336278,-78.88394479,SUNY Buffalo,edu,2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d,citation,https://doi.org/10.1109/CVPRW.2011.5981801,Genealogical face recognition based on UB KinFace database,2011 -326,LFW,lfw,25.0410728,121.6147562,Institute of Information 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-330,LFW,lfw,52.2380139,6.8566761,University of Twente,edu,84e6669b47670f9f4f49c0085311dce0e178b685,citation,http://pdfs.semanticscholar.org/84e6/669b47670f9f4f49c0085311dce0e178b685.pdf,Face frontalization for Alignment and Recognition,2015 -331,LFW,lfw,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 -332,LFW,lfw,40.0433204,116.3418109,Beijing Institute of Science and Technology Information,edu,5039834df68600a24e7e8eefb6ba44a5124e67fc,citation,https://doi.org/10.1109/ICIP.2013.6738761,Modular hierarchical feature learning with deep neural networks for face verification,2013 -333,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,5039834df68600a24e7e8eefb6ba44a5124e67fc,citation,https://doi.org/10.1109/ICIP.2013.6738761,Modular hierarchical feature learning with deep neural networks for face verification,2013 -334,LFW,lfw,23.09461185,113.28788994,Sun Yat-Sen University,edu,44f48a4b1ef94a9104d063e53bf88a69ff0f55f3,citation,http://pdfs.semanticscholar.org/44f4/8a4b1ef94a9104d063e53bf88a69ff0f55f3.pdf,Automatically Building Face Datasets of New Domains from Weakly Labeled Data with Pretrained Models,2016 -335,LFW,lfw,41.40657415,2.1945341,Universitat Oberta de Catalunya,edu,6584c3c877400e1689a11ef70133daa86a238602,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8039231,Supervised Committee of Convolutional Neural Networks in Automated Facial Expression Analysis,2018 -336,LFW,lfw,51.24303255,-0.59001382,University of Surrey,edu,5763b09ebca9a756b4adebf74d6d7de27e80e298,citation,https://doi.org/10.1109/BTAS.2013.6712738,Picture-specific cohort score normalization for face pair matching,2013 -337,LFW,lfw,39.7487516,30.47653071,Eskisehir Osmangazi University,edu,13bda03fc8984d5943ed8d02e49a779d27c84114,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2012.6248047,Efficient object detection using cascades of nearest convex model classifiers,2012 -338,LFW,lfw,40.742252,-74.0270949,Stevens Institute of Technology,edu,1e1d7cbbef67e9e042a3a0a9a1bcefcc4a9adacf,citation,http://personal.stevens.edu/~hli18//data/papers/CVPR2016_CameraReady.pdf,A Multi-level Contextual Model for Person Recognition in Photo Albums,2016 -339,LFW,lfw,1.3484104,103.68297965,Nanyang Technological University,edu,47190d213caef85e8b9dd0d271dbadc29ed0a953,citation,https://arxiv.org/pdf/1807.11649.pdf,The Devil of Face Recognition is in the Noise,2018 -340,LFW,lfw,32.87935255,-117.23110049,"University of California, San Diego",edu,47190d213caef85e8b9dd0d271dbadc29ed0a953,citation,https://arxiv.org/pdf/1807.11649.pdf,The Devil of Face Recognition is in the Noise,2018 -341,LFW,lfw,51.0784038,-114.1287077,University of Calgary,edu,e66b4aa85524f493dafde8c75176ac0afad5b79c,citation,https://doi.org/10.1109/SSCI.2017.8285219,Watchlist risk assessment using multiparametric cost and relative entropy,2017 -342,LFW,lfw,37.5953979,127.0630499,Hankuk University of Foreign Studies,edu,8af411697e73f6cfe691fe502d4bfb42510b4835,citation,http://pdfs.semanticscholar.org/8af4/11697e73f6cfe691fe502d4bfb42510b4835.pdf,Dynamic Local Ternary Pattern for Face Recognition and Verification,2013 -343,LFW,lfw,23.7289899,90.3982682,Institute of Information Technology,edu,8af411697e73f6cfe691fe502d4bfb42510b4835,citation,http://pdfs.semanticscholar.org/8af4/11697e73f6cfe691fe502d4bfb42510b4835.pdf,Dynamic Local Ternary Pattern for Face Recognition and Verification,2013 -344,LFW,lfw,23.7316957,90.3965275,University of Dhaka,edu,8af411697e73f6cfe691fe502d4bfb42510b4835,citation,http://pdfs.semanticscholar.org/8af4/11697e73f6cfe691fe502d4bfb42510b4835.pdf,Dynamic Local Ternary Pattern for Face Recognition and Verification,2013 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University,edu,539f55c0e2501c1d86791c8b54b225d9b3187b9c,citation,https://doi.org/10.1109/TIP.2017.2738560,Low-Rank Latent Pattern Approximation With Applications to Robust Image Classification,2017 -349,LFW,lfw,36.3173432,50.0367286,Azad University,edu,82ccd62f70e669ec770daf11d9611cab0a13047e,citation,http://www.csse.uwa.edu.au/~ajmal/papers/Farshid_DICTA2013.pdf,Sparse Variation Pattern for Texture Classification,2013 -350,LFW,lfw,34.68092465,50.05341352,Tafresh University,edu,82ccd62f70e669ec770daf11d9611cab0a13047e,citation,http://www.csse.uwa.edu.au/~ajmal/papers/Farshid_DICTA2013.pdf,Sparse Variation Pattern for Texture Classification,2013 -351,LFW,lfw,-31.95040445,115.79790037,University of Western Australia,edu,82ccd62f70e669ec770daf11d9611cab0a13047e,citation,http://www.csse.uwa.edu.au/~ajmal/papers/Farshid_DICTA2013.pdf,Sparse Variation Pattern for Texture Classification,2013 -352,LFW,lfw,56.3411984,-2.7930938,University of St Andrews,edu,1b5875dbebc76fec87e72cee7a5263d325a77376,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.528,Learnt Quasi-Transitive Similarity for Retrieval from Large Collections of Faces,2016 -353,LFW,lfw,24.4399419,118.09301781,Xiamen University,edu,5632ba72b2652df3b648b2ee698233e76a4eee65,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8346387,Reconstruction of 3D facial image using a single 2D image,2018 -354,LFW,lfw,-33.3578899,151.37834708,University of Newcastle,edu,5632ba72b2652df3b648b2ee698233e76a4eee65,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8346387,Reconstruction of 3D facial image using a single 2D image,2018 -355,LFW,lfw,33.5609504,73.07125966,Foundation University Rawalpindi Campus,edu,7c42371bae54050dbbf7ded1e7a9b4109a23a482,citation,http://pdfs.semanticscholar.org/7c42/371bae54050dbbf7ded1e7a9b4109a23a482.pdf,Optimized features selection using hybrid PSO-GA for multi-view gender classification,2015 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Sciences,edu,bf8a520533f401347e2f55da17383a3e567ef6d8,citation,http://pdfs.semanticscholar.org/bf8a/520533f401347e2f55da17383a3e567ef6d8.pdf,Bounded-Distortion Metric Learning,2015 -364,LFW,lfw,13.01119095,74.79498825,"National Institute of Technology, Karnataka",edu,e9e40e588f8e6510fa5537e0c9e083ceed5d07ad,citation,http://pdfs.semanticscholar.org/e9e4/0e588f8e6510fa5537e0c9e083ceed5d07ad.pdf,Fast Face Detection Using Graphics Processor,2011 -365,LFW,lfw,37.4102193,-122.05965487,Carnegie Mellon University,edu,192235f5a9e4c9d6a28ec0d333e36f294b32f764,citation,http://www.andrew.cmu.edu/user/sjayasur/iccv.pdf,Reconfiguring the Imaging Pipeline for Computer Vision,2017 -366,LFW,lfw,42.4505507,-76.4783513,Cornell University,edu,192235f5a9e4c9d6a28ec0d333e36f294b32f764,citation,http://www.andrew.cmu.edu/user/sjayasur/iccv.pdf,Reconfiguring the Imaging Pipeline for Computer Vision,2017 -367,LFW,lfw,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,cd6aaa37fffd0b5c2320f386be322b8adaa1cc68,citation,https://arxiv.org/pdf/1804.06655.pdf,Deep Face Recognition: A Survey,2018 -368,LFW,lfw,-33.8809651,151.20107299,University of Technology Sydney,edu,e4e3faa47bb567491eaeaebb2213bf0e1db989e1,citation,http://pdfs.semanticscholar.org/e4e3/faa47bb567491eaeaebb2213bf0e1db989e1.pdf,Empirical Risk Minimization for Metric Learning Using Privileged Information,2016 -369,LFW,lfw,31.846918,117.29053367,Hefei University of Technology,edu,e4e3faa47bb567491eaeaebb2213bf0e1db989e1,citation,http://pdfs.semanticscholar.org/e4e3/faa47bb567491eaeaebb2213bf0e1db989e1.pdf,Empirical Risk Minimization for Metric Learning Using Privileged Information,2016 -370,LFW,lfw,23.1353836,113.29470496,Guangdong University of Technology,edu,4b02387c2db968a70b69d98da3c443f139099e91,citation,http://pdfs.semanticscholar.org/4b02/387c2db968a70b69d98da3c443f139099e91.pdf,Detecting facial landmarks in the video based on a hybrid framework,2016 -371,LFW,lfw,1.3484104,103.68297965,Nanyang Technological University,edu,f7c50d2be9fba0e4527fd9fbe3095e9d9a94fdd3,citation,http://pdfs.semanticscholar.org/f7c5/0d2be9fba0e4527fd9fbe3095e9d9a94fdd3.pdf,Large Margin Multi-metric Learning for Face and Kinship Verification in the Wild,2014 -372,LFW,lfw,40.8419836,-73.94368971,Columbia University,edu,217de4ff802d4904d3f90d2e24a29371307942fe,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2013.128,"POOF: Part-Based One-vs.-One Features for Fine-Grained Categorization, Face Verification, and Attribute Estimation",2013 -373,LFW,lfw,40.47913175,-74.43168868,Rutgers University,edu,e3b324101157daede3b4d16bdc9c2388e849c7d4,citation,https://pdfs.semanticscholar.org/e3b3/24101157daede3b4d16bdc9c2388e849c7d4.pdf,"Robust Real-Time 3 D Face Tracking from RGBD Videos under Extreme Pose , Depth , and Expression Variations",2017 -374,LFW,lfw,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 -375,LFW,lfw,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,0c59071ddd33849bd431165bc2d21bbe165a81e0,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Oh_Person_Recognition_in_ICCV_2015_paper.pdf,Person Recognition in Personal Photo Collections,2015 -376,LFW,lfw,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 -377,LFW,lfw,2.92749755,101.64185301,Multimedia University,edu,90ad0daa279c3e30b360f9fe9371293d68f4cebf,citation,http://pdfs.semanticscholar.org/90ad/0daa279c3e30b360f9fe9371293d68f4cebf.pdf,Spatio-temporal Framework and Algorithms for Video-based Face Recognition,2015 -378,LFW,lfw,23.09461185,113.28788994,Sun Yat-Sen University,edu,c675534be881e59a78a5986b8fb4e649ddd2abbe,citation,https://doi.org/10.1109/ICIP.2017.8296548,Face recognition by landmark pooling-based CNN with concentrate loss,2017 -379,LFW,lfw,50.7338124,7.1022465,Rheinische-Friedrich-Wilhelms University,edu,561ae67de137e75e9642ab3512d3749b34484310,citation,http://pdfs.semanticscholar.org/561a/e67de137e75e9642ab3512d3749b34484310.pdf,DeepGestalt - Identifying Rare Genetic Syndromes Using Deep Learning,2018 -380,LFW,lfw,32.1119889,34.80459702,Tel Aviv University,edu,561ae67de137e75e9642ab3512d3749b34484310,citation,http://pdfs.semanticscholar.org/561a/e67de137e75e9642ab3512d3749b34484310.pdf,DeepGestalt - Identifying Rare Genetic Syndromes Using Deep Learning,2018 -381,LFW,lfw,32.87935255,-117.23110049,"University of California, San Diego",edu,561ae67de137e75e9642ab3512d3749b34484310,citation,http://pdfs.semanticscholar.org/561a/e67de137e75e9642ab3512d3749b34484310.pdf,DeepGestalt - Identifying Rare Genetic Syndromes Using Deep Learning,2018 -382,LFW,lfw,1.3484104,103.68297965,Nanyang Technological University,edu,39f03d1dfd94e6f06c1565d7d1bb14ab0eee03bc,citation,http://openaccess.thecvf.com/content_iccv_2015/papers/Lu_Simultaneous_Local_Binary_ICCV_2015_paper.pdf,Simultaneous Local Binary Feature Learning and Encoding for Face Recognition,2015 -383,LFW,lfw,40.00229045,116.32098908,Tsinghua University,edu,39f03d1dfd94e6f06c1565d7d1bb14ab0eee03bc,citation,http://openaccess.thecvf.com/content_iccv_2015/papers/Lu_Simultaneous_Local_Binary_ICCV_2015_paper.pdf,Simultaneous Local Binary Feature Learning and Encoding for Face Recognition,2015 -384,LFW,lfw,37.4102193,-122.05965487,Carnegie Mellon University,edu,2b869d5551b10f13bf6fcdb8d13f0aa4d1f59fc4,citation,https://arxiv.org/pdf/1803.00130.pdf,Ring loss: Convex Feature Normalization for Face Recognition,2018 -385,LFW,lfw,37.4102193,-122.05965487,Carnegie Mellon University,edu,b1fdd4ae17d82612cefd4e78b690847b071379d3,citation,https://pdfs.semanticscholar.org/4fc5/416b6c7173d3462e5be796bda3ad8d5645a1.pdf,Supervised Descent Method,2015 -386,LFW,lfw,51.49887085,-0.17560797,Imperial College London,edu,1921795408345751791b44b379f51b7dd54ebfa2,citation,https://arxiv.org/pdf/1807.07872.pdf,From Face Recognition to Models of Identity: A Bayesian Approach to Learning About Unknown Identities from Unsupervised Data,2018 -387,LFW,lfw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,f05ad40246656a977cf321c8299158435e3f3b61,citation,http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Lu_Face_Recognition_Using_2013_ICCV_paper.pdf,Face Recognition Using Face Patch Networks,2013 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-392,LFW,lfw,42.36782045,-71.12666653,Harvard University,edu,785eeac2e236a85a45b4e0356c0745279c31e089,citation,https://doi.org/10.1109/TIFS.2014.2359543,Learning Person-Specific Representations From Faces in the Wild,2014 -393,LFW,lfw,-22.8137765,-47.0640004,State University of Campinas,edu,785eeac2e236a85a45b4e0356c0745279c31e089,citation,https://doi.org/10.1109/TIFS.2014.2359543,Learning Person-Specific Representations From Faces in the Wild,2014 -394,LFW,lfw,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 -395,LFW,lfw,25.4299114,81.7711827,"IIIT Allahabad, India",edu,e1449be4951ba7519945cd1ad50656c3516113da,citation,https://doi.org/10.1109/TCSVT.2016.2603535,Local Gradient Hexa Pattern: A Descriptor for Face Recognition and Retrieval,2018 -396,LFW,lfw,41.1664858,-73.1920564,University of Bridgeport,edu,f92ade569cbe54344ffd3bb25efd366dcd8ad659,citation,https://arxiv.org/pdf/1704.01464.pdf,Effect of Super Resolution on High Dimensional Features for Unsupervised Face Recognition in the Wild,2017 -397,LFW,lfw,25.2873992,110.3324277,Guilin University of Electronic Technology Guangxi Guilin,edu,9989ad33b64accea8042e386ff3f1216386ba7f1,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8393320,Facial feature extraction method based on shallow and deep fusion CNN,2017 -398,LFW,lfw,53.21967825,6.56251482,University of Groningen,edu,8efda5708bbcf658d4f567e3866e3549fe045bbb,citation,http://pdfs.semanticscholar.org/8efd/a5708bbcf658d4f567e3866e3549fe045bbb.pdf,Pre-trained Deep Convolutional Neural Networks for Face Recognition,2018 -399,LFW,lfw,39.2899685,-76.62196103,University of Maryland,edu,3b092733f428b12f1f920638f868ed1e8663fe57,citation,http://www.math.jhu.edu/~data/RamaPapers/PerformanceBounds.pdf,On the size of Convolutional Neural Networks and generalization performance,2016 -400,LFW,lfw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,55966926e7c28b1eee1c7eb7a0b11b10605a1af0,citation,http://pdfs.semanticscholar.org/baa8/bdeb5aa545af5b5f43efaf9dda08490da0bc.pdf,Surpassing Human-Level Face Verification Performance on LFW with GaussianFace,2015 -401,LFW,lfw,65.0592157,25.46632601,University of Oulu,edu,e6d6203fa911429d76f026e2ec2de260ec520432,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7899663,Siamese network features for image matching,2016 -402,LFW,lfw,60.18558755,24.8242733,Aalto University,edu,e6d6203fa911429d76f026e2ec2de260ec520432,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7899663,Siamese network features for image matching,2016 -403,LFW,lfw,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,4d3c4c3fe8742821242368e87cd72da0bd7d3783,citation,http://www.ee.cuhk.edu.hk/~xgwang/papers/sunWTiccv13.pdf,Hybrid Deep Learning for Face Verification,2013 -404,LFW,lfw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,4d3c4c3fe8742821242368e87cd72da0bd7d3783,citation,http://www.ee.cuhk.edu.hk/~xgwang/papers/sunWTiccv13.pdf,Hybrid Deep Learning for Face Verification,2013 -405,LFW,lfw,51.49887085,-0.17560797,Imperial College London,edu,9af9a88c60d9e4b53e759823c439fc590a4b5bc5,citation,https://arxiv.org/pdf/1708.00277.pdf,Learning Deep Convolutional Embeddings for Face Representation Using Joint Sample- and Set-Based Supervision,2017 -406,LFW,lfw,46.109237,7.08453549,IDIAP Research Institute,edu,939123cf21dc9189a03671484c734091b240183e,citation,http://publications.idiap.ch/downloads/papers/2015/Erdogmus_MMSP_2015.pdf,Within- and cross- database evaluations for face gender classification via befit protocols,2014 -407,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,061e29eae705f318eee703b9e17dc0989547ba0c,citation,http://pdfs.semanticscholar.org/061e/29eae705f318eee703b9e17dc0989547ba0c.pdf,Enhancing Expression Recognition in the Wild with Unlabeled Reference Data,2012 -408,LFW,lfw,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,435642641312364e45f4989fac0901b205c49d53,citation,http://pdfs.semanticscholar.org/4356/42641312364e45f4989fac0901b205c49d53.pdf,Face Model Compression by Distilling Knowledge from Neurons,2016 -409,LFW,lfw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,435642641312364e45f4989fac0901b205c49d53,citation,http://pdfs.semanticscholar.org/4356/42641312364e45f4989fac0901b205c49d53.pdf,Face Model Compression by Distilling Knowledge from Neurons,2016 -410,LFW,lfw,23.09461185,113.28788994,Sun Yat-Sen University,edu,80d42f74ee9bf03f3790c8d0f5a307deffe0b3b7,citation,https://doi.org/10.1109/TNNLS.2016.2522431,Learning Kernel Extended Dictionary for Face Recognition,2017 -411,LFW,lfw,36.3693473,120.673818,Shandong University,edu,edbddf8c176d6e914f0babe64ad56c051597d415,citation,https://doi.org/10.1109/TMM.2016.2644866,Predicting Image Memorability Through Adaptive Transfer Learning From External Sources,2017 -412,LFW,lfw,36.20304395,117.05842113,Tianjin University,edu,edbddf8c176d6e914f0babe64ad56c051597d415,citation,https://doi.org/10.1109/TMM.2016.2644866,Predicting Image Memorability Through Adaptive Transfer Learning From External Sources,2017 -413,LFW,lfw,42.3583961,-71.09567788,MIT,edu,18c6c92c39c8a5a2bb8b5673f339d3c26b8dcaae,citation,http://pdfs.semanticscholar.org/18c6/c92c39c8a5a2bb8b5673f339d3c26b8dcaae.pdf,Learning invariant representations and applications to face verification,2013 -414,LFW,lfw,42.3626295,-71.0914481,McGovern Institute for Brain Research,edu,18c6c92c39c8a5a2bb8b5673f339d3c26b8dcaae,citation,http://pdfs.semanticscholar.org/18c6/c92c39c8a5a2bb8b5673f339d3c26b8dcaae.pdf,Learning invariant representations and applications to face verification,2013 -415,LFW,lfw,50.0764296,14.41802312,Czech Technical University,edu,37c8514df89337f34421dc27b86d0eb45b660a5e,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w25/papers/Uricar_Facial_Landmark_Tracking_ICCV_2015_paper.pdf,Facial Landmark Tracking by Tree-Based Deformable Part Model Based Detector,2015 -416,LFW,lfw,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 -417,LFW,lfw,17.4454957,78.34854698,International Institute of Information Technology,edu,96e1ccfe96566e3c96d7b86e134fa698c01f2289,citation,https://arxiv.org/pdf/1712.00321.pdf,Semi-adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images,2018 -418,LFW,lfw,42.718568,-84.47791571,Michigan State University,edu,96e1ccfe96566e3c96d7b86e134fa698c01f2289,citation,https://arxiv.org/pdf/1712.00321.pdf,Semi-adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images,2018 -419,LFW,lfw,5.4409448,10.07120561,University of Dschang,edu,d78fbd11f12cbc194e8ede761d292dc2c02d38a2,citation,http://pdfs.semanticscholar.org/d78f/bd11f12cbc194e8ede761d292dc2c02d38a2.pdf,Enhancing Gray Scale Images for Face Detection under Unstable Lighting Condition,2017 -420,LFW,lfw,26.88111275,112.62850666,Hunan University,edu,86d0127e1fd04c3d8ea78401c838af621647dc95,citation,https://arxiv.org/pdf/1804.02810.pdf,A Novel Multi-Task Tensor Correlation Neural Network for Facial Attribute Prediction,2018 -421,LFW,lfw,28.2290209,112.99483204,"National University of Defense Technology, China",edu,86d0127e1fd04c3d8ea78401c838af621647dc95,citation,https://arxiv.org/pdf/1804.02810.pdf,A Novel Multi-Task Tensor Correlation Neural Network for Facial Attribute Prediction,2018 -422,LFW,lfw,29.58333105,-98.61944505,University of Texas at San Antonio,edu,86d0127e1fd04c3d8ea78401c838af621647dc95,citation,https://arxiv.org/pdf/1804.02810.pdf,A Novel Multi-Task Tensor Correlation Neural Network for Facial Attribute Prediction,2018 -423,LFW,lfw,43.47061295,-80.54724732,University of Waterloo,edu,103a7c3eba36792886ae8005f6492332e6b05bad,citation,https://arxiv.org/pdf/1809.06218.pdf,Facial Recognition with Encoded Local Projections,2018 -424,LFW,lfw,47.6423318,-122.1369302,Microsoft,company,291265db88023e92bb8c8e6390438e5da148e8f5,citation,http://pdfs.semanticscholar.org/4603/cb8e05258bb0572ae912ad20903b8f99f4b1.pdf,MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition,2016 -425,LFW,lfw,39.1254938,-77.22293475,National Institute of Standards and Technology,edu,35d42f4e7a1d898bc8e2d052c38e1106f3e80188,citation,https://doi.org/10.1109/BTAS.2015.7358765,Human and algorithm performance on the PaSC face Recognition Challenge,2015 -426,LFW,lfw,32.9820799,-96.7566278,University of Texas at Dallas,edu,35d42f4e7a1d898bc8e2d052c38e1106f3e80188,citation,https://doi.org/10.1109/BTAS.2015.7358765,Human and algorithm performance on the PaSC face Recognition Challenge,2015 -427,LFW,lfw,51.49887085,-0.17560797,Imperial College London,edu,06d7ef72fae1be206070b9119fb6b61ce4699587,citation,http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Zafeiriou_On_One-Shot_Similarity_2013_ICCV_paper.pdf,On One-Shot Similarity Kernels: Explicit Feature Maps and Properties,2013 -428,LFW,lfw,51.59029705,-0.22963221,Middlesex University,edu,06d7ef72fae1be206070b9119fb6b61ce4699587,citation,http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Zafeiriou_On_One-Shot_Similarity_2013_ICCV_paper.pdf,On One-Shot Similarity Kernels: Explicit Feature Maps and Properties,2013 -429,LFW,lfw,38.2899482,21.7886469,University of Patras,edu,06d7ef72fae1be206070b9119fb6b61ce4699587,citation,http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Zafeiriou_On_One-Shot_Similarity_2013_ICCV_paper.pdf,On One-Shot Similarity Kernels: Explicit Feature Maps and Properties,2013 -430,LFW,lfw,1.2962018,103.77689944,National University of Singapore,edu,c17c7b201cfd0bcd75441afeaa734544c6ca3416,citation,https://doi.org/10.1109/TCSVT.2016.2587389,Layerwise Class-Aware Convolutional Neural Network,2017 -431,LFW,lfw,32.0575279,118.78682252,Southeast University,edu,c17c7b201cfd0bcd75441afeaa734544c6ca3416,citation,https://doi.org/10.1109/TCSVT.2016.2587389,Layerwise Class-Aware Convolutional Neural Network,2017 -432,LFW,lfw,29.7207902,-95.34406271,University of Houston,edu,e6da1fcd2a8cda0c69b3d94812caa7d844903007,citation,http://dl.acm.org/citation.cfm?id=3137154,"Sonicdoor: scaling person identification with ultrasonic sensors by novel modeling of shape, behavior and walking patterns",2017 -433,LFW,lfw,23.09461185,113.28788994,Sun Yat-Sen University,edu,cd74d606e76ecddee75279679d9770cdc0b49861,citation,https://doi.org/10.1109/TIP.2014.2365725,Transfer Learning of Structured Representation for Face Recognition,2014 -434,LFW,lfw,23.7289899,90.3982682,Institute of Information Technology,edu,2e58ec57d71b2b2a3e71086234dd7037559cc17e,citation,https://pdfs.semanticscholar.org/2e58/ec57d71b2b2a3e71086234dd7037559cc17e.pdf,A Gender Recognition System from Facial Image,2018 -435,LFW,lfw,23.7316957,90.3965275,University of Dhaka,edu,2e58ec57d71b2b2a3e71086234dd7037559cc17e,citation,https://pdfs.semanticscholar.org/2e58/ec57d71b2b2a3e71086234dd7037559cc17e.pdf,A Gender Recognition System from Facial Image,2018 -436,LFW,lfw,36.3697191,127.362537,Korea Advanced Institute of Science and Technology,edu,8f99f7ccb85af6d4b9e015a9b215c529126e7844,citation,https://doi.org/10.1109/ROMAN.2017.8172359,Face image-based age and gender estimation with consideration of ethnic difference,2017 -437,LFW,lfw,42.718568,-84.47791571,Michigan State University,edu,4b605e6a9362485bfe69950432fa1f896e7d19bf,citation,http://biometrics.cse.msu.edu/Publications/Face/BlantonAllenMillerKalkaJain_CVPRWB2016_HID.pdf,A Comparison of Human and Automated Face Verification Accuracy on Unconstrained Image Sets,2016 -438,LFW,lfw,22.34000115,114.16970291,City University of Hong Kong,edu,2af2b74c3462ccff3a6881ff7cf4f321b3242fa9,citation,http://yugangjiang.info/publication/JCST-nameface.pdf,"Name-Face Association in Web Videos: A Large-Scale Dataset, Baselines, and Open Issues",2014 -439,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,2af2b74c3462ccff3a6881ff7cf4f321b3242fa9,citation,http://yugangjiang.info/publication/JCST-nameface.pdf,"Name-Face Association in Web Videos: A Large-Scale Dataset, Baselines, and Open Issues",2014 -440,LFW,lfw,31.30104395,121.50045497,Fudan University,edu,2af2b74c3462ccff3a6881ff7cf4f321b3242fa9,citation,http://yugangjiang.info/publication/JCST-nameface.pdf,"Name-Face Association in Web Videos: A Large-Scale Dataset, Baselines, and Open Issues",2014 -441,LFW,lfw,29.7207902,-95.34406271,University of Houston,edu,9ff931ca721d50e470e1a38e583c7b18b6cdc2cc,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7407637,An Overview and Empirical Comparison of Distance Metric Learning Methods,2017 -442,LFW,lfw,40.48256135,-3.6906079,Universidad Autonoma de Madrid,edu,578117ff493d691166fefc52fd61bad70d8752a9,citation,https://doi.org/10.1109/CCST.2016.7815707,Dealing with occlusions in face recognition by region-based fusion,2016 -443,LFW,lfw,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,3cea3aba77649d718991d0cb30135887267c11e8,citation,https://arxiv.org/pdf/1809.00594.pdf,Adversarial Attack Type I: Generating False Positives,2018 -444,LFW,lfw,38.99203005,-76.9461029,University of Maryland College Park,edu,053931267af79a89791479b18d1b9cde3edcb415,citation,https://pdfs.semanticscholar.org/0539/31267af79a89791479b18d1b9cde3edcb415.pdf,Attributes for Improved Attributes: A Multi-Task Network Utilizing Implicit and Explicit Relationships for Facial Attribute Classification,2017 -445,LFW,lfw,39.2899685,-76.62196103,University of Maryland,edu,053931267af79a89791479b18d1b9cde3edcb415,citation,https://pdfs.semanticscholar.org/0539/31267af79a89791479b18d1b9cde3edcb415.pdf,Attributes for Improved Attributes: A Multi-Task Network Utilizing Implicit and Explicit Relationships for Facial Attribute Classification,2017 -446,LFW,lfw,42.718568,-84.47791571,Michigan State University,edu,02467703b6e087799e04e321bea3a4c354c5487d,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2016.27,Grouper: Optimizing Crowdsourced Face Annotations,2016 -447,LFW,lfw,46.0501558,14.46907327,University of Ljubljana,edu,afe9cfba90d4b1dbd7db1cf60faf91f24d12b286,citation,http://pdfs.semanticscholar.org/afe9/cfba90d4b1dbd7db1cf60faf91f24d12b286.pdf,Principal Directions of Synthetic Exact Filters for Robust Real-Time Eye Localization,2011 -448,LFW,lfw,53.21967825,6.56251482,University of Groningen,edu,4ff4c27e47b0aa80d6383427642bb8ee9d01c0ac,citation,https://doi.org/10.1109/SSCI.2015.37,Deep Convolutional Neural Networks and Support Vector Machines for Gender Recognition,2015 -449,LFW,lfw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,0ce3a786aed896d128f5efdf78733cc675970854,citation,http://pdfs.semanticscholar.org/3689/2b6bb4848a9c21158b8eded7f14a6654dd7e.pdf,Learning the Face Prior for Bayesian Face Recognition,2014 -450,LFW,lfw,37.3936717,-122.0807262,Facebook,company,628a3f027b7646f398c68a680add48c7969ab1d9,citation,https://pdfs.semanticscholar.org/628a/3f027b7646f398c68a680add48c7969ab1d9.pdf,Plan for Final Year Project : HKU-Face : A Large Scale Dataset for Deep Face Recognition,2017 -451,LFW,lfw,40.00229045,116.32098908,Tsinghua University,edu,9fc993aeb0a007ccfaca369a9a8c0ccf7697261d,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7936534,Context-Aware Local Binary Feature Learning for Face Recognition,2018 -452,LFW,lfw,43.7776426,11.259765,University of Florence,edu,746c0205fdf191a737df7af000eaec9409ede73f,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8423119,Investigating Nuisances in DCNN-Based Face Recognition,2018 -453,LFW,lfw,53.8338371,10.7035939,Institute of Systems and Robotics,edu,3802c97f925cb03bac91d9db13d8b777dfd29dcc,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2014.232,Non-parametric Bayesian Constrained Local Models,2014 -454,LFW,lfw,43.7776426,11.259765,University of Florence,edu,71ca8b6e84c17b3e68f980bfb8cddc837100f8bf,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7899774,Effective 3D based frontalization for unconstrained face recognition,2016 -455,LFW,lfw,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 -456,LFW,lfw,25.37461295,51.48980354,Qatar University,edu,d0d75a7116a76ccd98a3aeb6f6fff10ba91de1c1,citation,https://doi.org/10.1109/TIP.2015.2502144,Constrained Metric Learning by Permutation Inducing Isometries,2016 -457,LFW,lfw,-31.95040445,115.79790037,University of Western Australia,edu,d0d75a7116a76ccd98a3aeb6f6fff10ba91de1c1,citation,https://doi.org/10.1109/TIP.2015.2502144,Constrained Metric Learning by Permutation Inducing Isometries,2016 -458,LFW,lfw,-35.2776999,149.118527,Australian National University,edu,79db191ca1268dc88271abef3179c4fe4ee92aed,citation,https://pdfs.semanticscholar.org/79db/191ca1268dc88271abef3179c4fe4ee92aed.pdf,Facial Expression Based Automatic Album Creation,2010 -459,LFW,lfw,-35.23656905,149.08446994,University of Canberra,edu,79db191ca1268dc88271abef3179c4fe4ee92aed,citation,https://pdfs.semanticscholar.org/79db/191ca1268dc88271abef3179c4fe4ee92aed.pdf,Facial Expression Based Automatic Album Creation,2010 -460,LFW,lfw,-33.8809651,151.20107299,University of Technology Sydney,edu,3983370efe7a7521bde255017171724d845b3383,citation,https://arxiv.org/pdf/1810.01152.pdf,Learning Discriminators as Energy Networks in Adversarial Learning,2018 -461,LFW,lfw,41.6659,-91.57310307,University of Iowa,edu,3983370efe7a7521bde255017171724d845b3383,citation,https://arxiv.org/pdf/1810.01152.pdf,Learning Discriminators as Energy Networks in Adversarial Learning,2018 -462,LFW,lfw,40.7423025,-74.17928172,New Jersey Institute of Technology,edu,faf19885431cb39360158982c3a1127f6090a1f6,citation,https://doi.org/10.1109/BTAS.2015.7358768,Inheritable Fisher vector feature for kinship verification,2015 -463,LFW,lfw,51.4584837,-2.6097752,University of Bristol,edu,c4f3185f010027a0a97fcb9753d74eb27a9cfd3e,citation,http://doi.org/10.1016/j.patrec.2015.02.006,Learning to classify gender from four million images,2015 -464,LFW,lfw,12.81608485,74.92449278,Mangalore University,edu,b68452e28951bf8db5f1193eca3a8fd9e2d0d7ef,citation,https://doi.org/10.1109/ICACCI.2015.7275752,Approximate radial gradient transform based face recognition,2015 -465,LFW,lfw,42.3583961,-71.09567788,MIT,edu,5e0e516226413ea1e973f1a24e2fdedde98e7ec0,citation,http://pdfs.semanticscholar.org/74ce/97da57ec848db660ee69dec709f226c74f43.pdf,The Invariance Hypothesis and the Ventral Stream,2013 -466,LFW,lfw,53.21967825,6.56251482,University of Groningen,edu,d8896861126b7fd5d2ceb6fed8505a6dff83414f,citation,http://pdfs.semanticscholar.org/d889/6861126b7fd5d2ceb6fed8505a6dff83414f.pdf,In-plane Rotational Alignment of Faces by Eye and Eye-pair Detection,2015 -467,LFW,lfw,39.9808333,116.34101249,Beihang University,edu,70d2ab1af0edd5c0a30d576a5d4aa397c4f92d3e,citation,http://doi.org/10.1007/s11042-018-5608-2,Elastic preserving projections based on L1-norm maximization,2018 -468,LFW,lfw,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 -469,LFW,lfw,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 -470,LFW,lfw,35.9042272,-78.85565763,"IBM Research, North Carolina",company,148eb413bede35487198ce7851997bf8721ea2d6,citation,http://pdfs.semanticscholar.org/148e/b413bede35487198ce7851997bf8721ea2d6.pdf,People Search in Surveillance Videos,2009 -471,LFW,lfw,38.8964679,-104.8050594,University of Colorado at Colorado Springs,edu,e3e2c106ccbd668fb9fca851498c662add257036,citation,http://www.vast.uccs.edu/~tboult/PAPERS/BTAS13-Sapkota-et-al-Ensembles.pdf,"Appearance, context and co-occurrence ensembles for identity recognition in personal photo collections",2013 -472,LFW,lfw,42.3383668,-71.08793524,Northeastern University,edu,d22b378fb4ef241d8d210202893518d08e0bb213,citation,http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Zhang_Random_Faces_Guided_2013_ICCV_paper.pdf,Random Faces Guided Sparse Many-to-One Encoder for Pose-Invariant Face Recognition,2013 -473,LFW,lfw,1.2962018,103.77689944,National University of Singapore,edu,dbb9601a1d2febcce4c07dd2b819243d81abb2c2,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8361884,Landmark Free Face Attribute Prediction,2018 -474,LFW,lfw,1.27486,103.797787,"SAP Innovation Center Network, Singapore",company,dbb9601a1d2febcce4c07dd2b819243d81abb2c2,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8361884,Landmark Free Face Attribute Prediction,2018 -475,LFW,lfw,37.4092265,-122.0236615,"Baidu Research, USA",company,8633732d9f787f8497c2696309c7d70176995c15,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7298967,Multi-objective convolutional learning for face labeling,2015 -476,LFW,lfw,37.36566745,-120.42158888,"University of California, Merced",edu,8633732d9f787f8497c2696309c7d70176995c15,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7298967,Multi-objective convolutional learning for face labeling,2015 -477,LFW,lfw,42.718568,-84.47791571,Michigan State University,edu,7fb5006b6522436ece5bedf509e79bdb7b79c9a7,citation,https://pdfs.semanticscholar.org/7fb5/006b6522436ece5bedf509e79bdb7b79c9a7.pdf,Multi-Task Convolutional Neural Network for Face Recognition,2017 -478,LFW,lfw,42.3889785,-72.5286987,University of Massachusetts,edu,368e99f669ea5fd395b3193cd75b301a76150f9d,citation,https://arxiv.org/pdf/1506.01342.pdf,One-to-many face recognition with bilinear CNNs,2016 -479,LFW,lfw,33.5866784,-101.87539204,Electrical and Computer Engineering,edu,c18a03568d4b512a0d8380cbb1fbf6bd56d11f05,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8430403,A Wearable IoT with Complex Artificial Perception Embedding for Alzheimer Patients,2018 -480,LFW,lfw,32.1119889,34.80459702,Tel Aviv University,edu,a6b5ffb5b406abfda2509cae66cdcf56b4bb3837,citation,http://pdfs.semanticscholar.org/bce2/02717ce134b317b39f0a18151659d643875b.pdf,One Shot Similarity Metric Learning for Action Recognition,2011 -481,LFW,lfw,52.02453775,-0.70927481,Open University,edu,a6b5ffb5b406abfda2509cae66cdcf56b4bb3837,citation,http://pdfs.semanticscholar.org/bce2/02717ce134b317b39f0a18151659d643875b.pdf,One Shot Similarity Metric Learning for Action Recognition,2011 -482,LFW,lfw,31.9078499,34.81334092,Weizmann Institute of Science,edu,a6b5ffb5b406abfda2509cae66cdcf56b4bb3837,citation,http://pdfs.semanticscholar.org/bce2/02717ce134b317b39f0a18151659d643875b.pdf,One Shot Similarity Metric Learning for Action Recognition,2011 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Florida,edu,c07ab025d9e3c885ad5386e6f000543efe091c4b,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8302601,Preserving Model Privacy for Machine Learning in Distributed Systems,2018 -487,LFW,lfw,39.9922379,116.30393816,Peking University,edu,5798055e11e25c404b1b0027bc9331bcc6e00555,citation,http://doi.acm.org/10.1145/2393347.2396357,PDSS: patch-descriptor-similarity space for effective face verification,2012 -488,LFW,lfw,51.49887085,-0.17560797,Imperial College London,edu,c43ed9b34cad1a3976bac7979808eb038d88af84,citation,https://arxiv.org/pdf/1804.03675.pdf,Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model,2018 -489,LFW,lfw,51.24303255,-0.59001382,University of Surrey,edu,c43ed9b34cad1a3976bac7979808eb038d88af84,citation,https://arxiv.org/pdf/1804.03675.pdf,Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model,2018 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Performance-driven 3D Face Tracking in Long Range Depth Scenes,2015 -494,LFW,lfw,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 -495,LFW,lfw,39.94976005,116.33629046,Beijing Jiaotong University,edu,21959bc56a160ebd450606867dce1462a913afab,citation,http://doi.org/10.1007/s11042-018-6071-9,Face recognition based on manifold constrained joint sparse sensing with K-SVD,2018 -496,LFW,lfw,-32.00686365,115.89691775,Curtin University,edu,21959bc56a160ebd450606867dce1462a913afab,citation,http://doi.org/10.1007/s11042-018-6071-9,Face recognition based on manifold constrained joint sparse sensing with K-SVD,2018 -497,LFW,lfw,31.32235655,121.38400941,Shanghai University,edu,21959bc56a160ebd450606867dce1462a913afab,citation,http://doi.org/10.1007/s11042-018-6071-9,Face recognition based on manifold constrained joint sparse sensing with K-SVD,2018 -498,LFW,lfw,31.2284923,121.40211389,East China Normal University,edu,06518858bd99cddf9bc9200fac5311fc29ac33b4,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8392777,Sparse Low-Rank Component-Based Representation for Face Recognition With Low-Quality Images,2019 -499,LFW,lfw,31.28473925,121.49694909,Tongji University,edu,06518858bd99cddf9bc9200fac5311fc29ac33b4,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8392777,Sparse Low-Rank Component-Based Representation for Face Recognition With Low-Quality Images,2019 -500,LFW,lfw,39.87391435,116.47722285,Beijing University of Technology,edu,f1d6da83dcf71eda45a56a86c5ae13e7f45a8536,citation,https://doi.org/10.1109/ACCESS.2017.2737544,A Secure Face-Verification Scheme Based on Homomorphic Encryption and Deep Neural Networks,2017 -501,LFW,lfw,25.0410728,121.6147562,Institute of Information 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University,edu,1de23d7fe718d9fab0159f58f422099e44ad3f0a,citation,http://doi.org/10.1007/s11063-016-9558-2,Locality Preserving Collaborative Representation for Face Recognition,2016 -518,LFW,lfw,39.2899685,-76.62196103,University of Maryland,edu,4f7b92bd678772552b3c3edfc9a7c5c4a8c60a8e,citation,https://pdfs.semanticscholar.org/4f7b/92bd678772552b3c3edfc9a7c5c4a8c60a8e.pdf,Deep Density Clustering of Unconstrained Faces,0 -519,LFW,lfw,35.97320905,-78.89755054,North Carolina Central University,edu,5bb53fb36a47b355e9a6962257dd465cd7ad6827,citation,http://pdfs.semanticscholar.org/5bb5/3fb36a47b355e9a6962257dd465cd7ad6827.pdf,Mask-off: Synthesizing Face Images in the Presence of Head-mounted Displays,2016 -520,LFW,lfw,38.0333742,-84.5017758,University of Kentucky,edu,5bb53fb36a47b355e9a6962257dd465cd7ad6827,citation,http://pdfs.semanticscholar.org/5bb5/3fb36a47b355e9a6962257dd465cd7ad6827.pdf,Mask-off: Synthesizing Face Images in the Presence of Head-mounted Displays,2016 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Ljubljana,edu,12003a7d65c4f98fb57587fd0e764b44d0d10125,citation,http://doi.ieeecomputersociety.org/10.1109/FG.2015.7284835,Face recognition in the wild with the Probabilistic Gabor-Fisher Classifier,2015 -538,LFW,lfw,36.3697191,127.362537,Korea Advanced Institute of Science and Technology,edu,7a09e8f65bd85d4c79f0ae90d4e2685869a9894f,citation,https://doi.org/10.1109/TMM.2016.2551698,Face and Hair Region Labeling Using Semi-Supervised Spectral Clustering-Based Multiple Segmentations,2016 -539,LFW,lfw,36.399184,127.394656,Korea Institute of Oriental Medicine,edu,7a09e8f65bd85d4c79f0ae90d4e2685869a9894f,citation,https://doi.org/10.1109/TMM.2016.2551698,Face and Hair Region Labeling Using Semi-Supervised Spectral Clustering-Based Multiple Segmentations,2016 -540,LFW,lfw,41.21002475,-73.80407056,IBM Thomas J. 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Maryland,edu,872dfdeccf99bbbed7c8f1ea08afb2d713ebe085,citation,https://arxiv.org/pdf/1703.09507.pdf,L2-constrained Softmax Loss for Discriminative Face Verification,2017 -545,LFW,lfw,23.09461185,113.28788994,Sun Yat-Sen University,edu,57ca530e9acb63487e8591cb6efb89473aa1e5b4,citation,https://doi.org/10.1109/TIP.2014.2356292,Multilayer Surface Albedo for Face Recognition With Reference Images in Bad Lighting Conditions,2014 -546,LFW,lfw,39.2899685,-76.62196103,University of Maryland,edu,a8748a79e8d37e395354ba7a8b3038468cb37e1f,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2016.47,Seeing the Forest from the Trees: A Holistic Approach to Near-Infrared Heterogeneous Face Recognition,2016 -547,LFW,lfw,39.65404635,-79.96475355,West Virginia University,edu,a8748a79e8d37e395354ba7a8b3038468cb37e1f,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2016.47,Seeing the Forest from the Trees: A Holistic Approach to Near-Infrared Heterogeneous Face Recognition,2016 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University,edu,2a92bda6dbd5cce5894f7d370d798c07fa8783f4,citation,https://doi.org/10.1109/TIFS.2014.2359587,Class-Specific Kernel Fusion of Multiple Descriptors for Face Verification Using Multiscale Binarised Statistical Image Features,2014 -553,LFW,lfw,51.24303255,-0.59001382,University of Surrey,edu,2a92bda6dbd5cce5894f7d370d798c07fa8783f4,citation,https://doi.org/10.1109/TIFS.2014.2359587,Class-Specific Kernel Fusion of Multiple Descriptors for Face Verification Using Multiscale Binarised Statistical Image Features,2014 -554,LFW,lfw,22.3386304,114.2620337,Hong Kong University of Science and Technology,edu,52bf00df3b970e017e4e2f8079202460f1c0e1bd,citation,http://pdfs.semanticscholar.org/52bf/00df3b970e017e4e2f8079202460f1c0e1bd.pdf,Learning High-level Prior with Convolutional Neural Networks for Semantic Segmentation,2015 -555,LFW,lfw,40.00229045,116.32098908,Tsinghua University,edu,52bf00df3b970e017e4e2f8079202460f1c0e1bd,citation,http://pdfs.semanticscholar.org/52bf/00df3b970e017e4e2f8079202460f1c0e1bd.pdf,Learning High-level Prior with Convolutional Neural Networks for Semantic Segmentation,2015 -556,LFW,lfw,31.83907195,117.26420748,University of Science and Technology of China,edu,52bf00df3b970e017e4e2f8079202460f1c0e1bd,citation,http://pdfs.semanticscholar.org/52bf/00df3b970e017e4e2f8079202460f1c0e1bd.pdf,Learning High-level Prior with Convolutional Neural Networks for Semantic Segmentation,2015 -557,LFW,lfw,47.5612651,7.5752961,University of Basel,edu,0081e2188c8f34fcea3e23c49fb3e17883b33551,citation,http://pdfs.semanticscholar.org/0081/e2188c8f34fcea3e23c49fb3e17883b33551.pdf,Training Deep Face Recognition Systems with Synthetic Data,2018 -558,LFW,lfw,30.642769,104.06751175,"Sichuan University, Chengdu",edu,2201f187a7483982c2e8e2585ad9907c5e66671d,citation,https://pdfs.semanticscholar.org/1cad/9aa5095733b56e998ad0cd396e89c2bc9928.pdf,Joint 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recognition,2018 -636,LFW,lfw,30.2931534,120.1620458,Zhejiang University of Technology,edu,ac48ecbc7c3c1a7eab08820845d47d6ce197707c,citation,https://doi.org/10.1109/TIP.2017.2681841,Iterative Re-Constrained Group Sparse Face Recognition With Adaptive Weights Learning,2017 -637,LFW,lfw,42.4505507,-76.4783513,Cornell University,edu,09f58353e48780c707cf24a0074e4d353da18934,citation,http://www.cse.msu.edu/rgroups/biometrics/Publications/Face/BestrowdenBishtKlontzJain_CrowdsourcingHumanPeformance_IJCB2014.pdf,Unconstrained face recognition: Establishing baseline human performance via crowdsourcing,2014 -638,LFW,lfw,42.718568,-84.47791571,Michigan State University,edu,09f58353e48780c707cf24a0074e4d353da18934,citation,http://www.cse.msu.edu/rgroups/biometrics/Publications/Face/BestrowdenBishtKlontzJain_CrowdsourcingHumanPeformance_IJCB2014.pdf,Unconstrained face recognition: Establishing baseline human performance via crowdsourcing,2014 -639,LFW,lfw,25.0410728,121.6147562,Institute of Information Science,edu,0106a2f6251dc9ffc90709c6f0d9b54c1e82326b,citation,http://www.iis.sinica.edu.tw/papers/song/14922-A.pdf,Applying scattering operators for face recognition: A comparative study,2012 -640,LFW,lfw,25.01682835,121.53846924,National Taiwan University,edu,0106a2f6251dc9ffc90709c6f0d9b54c1e82326b,citation,http://www.iis.sinica.edu.tw/papers/song/14922-A.pdf,Applying scattering operators for face recognition: A comparative study,2012 -641,LFW,lfw,35.0274996,135.78154513,University of Caen,edu,0ad8149318912b5449085187eb3521786a37bc78,citation,http://arxiv.org/abs/1604.02975,CP-mtML: Coupled Projection Multi-Task Metric Learning for Large Scale Face Retrieval,2016 -642,LFW,lfw,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,bc27434e376db89fe0e6ef2d2fabc100d2575ec6,citation,https://arxiv.org/pdf/1607.08438.pdf,Faceless Person Recognition; Privacy Implications in Social Media,2016 -643,LFW,lfw,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 -644,LFW,lfw,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 -645,LFW,lfw,45.7413921,126.62552755,Harbin Institute of Technology,edu,5c4f9260762a450892856b189df240f25b5ed333,citation,https://doi.org/10.1109/TIP.2017.2651396,Discriminative Elastic-Net Regularized Linear Regression,2017 -646,LFW,lfw,22.53521465,113.9315911,Shenzhen University,edu,5c4f9260762a450892856b189df240f25b5ed333,citation,https://doi.org/10.1109/TIP.2017.2651396,Discriminative Elastic-Net Regularized Linear Regression,2017 -647,LFW,lfw,52.6221571,1.2409136,University of East Anglia,edu,5c4f9260762a450892856b189df240f25b5ed333,citation,https://doi.org/10.1109/TIP.2017.2651396,Discriminative Elastic-Net Regularized Linear Regression,2017 -648,LFW,lfw,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,57ebeff9273dea933e2a75c306849baf43081a8c,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Sun_Deep_Convolutional_Network_2013_CVPR_paper.pdf,Deep Convolutional Network Cascade for Facial Point Detection,2013 -649,LFW,lfw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,57ebeff9273dea933e2a75c306849baf43081a8c,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Sun_Deep_Convolutional_Network_2013_CVPR_paper.pdf,Deep Convolutional Network Cascade for Facial Point Detection,2013 -650,LFW,lfw,32.77824165,34.99565673,Open University of Israel,edu,9ce97efc1d520dadaa0d114192ca789f23442727,citation,http://doi.acm.org/10.1145/2597627,Teaching Computer Vision: Bringing Research Benchmarks to the Classroom,2014 -651,LFW,lfw,42.9336278,-78.88394479,SUNY Buffalo,edu,4793f11fbca4a7dba898b9fff68f70d868e2497c,citation,http://pdfs.semanticscholar.org/4793/f11fbca4a7dba898b9fff68f70d868e2497c.pdf,Kinship Verification through Transfer Learning,2011 -652,LFW,lfw,-33.88890695,151.18943366,University of Sydney,edu,08d55271589f989d90a7edce3345f78f2468a7e0,citation,https://arxiv.org/pdf/1704.03373v1.pdf,Quality Aware Network for Set to Set Recognition,2017 -653,LFW,lfw,59.34986645,18.07063213,"KTH Royal Institute of Technology, Stockholm",edu,3c18fb8ff0f5003fefa8e9dc9bebaf88908d255c,citation,https://doi.org/10.1109/ICIP.2014.7025145,Is block matching an alternative tool to LBP for face recognition?,2014 -654,LFW,lfw,1.340216,103.965089,Singapore University of Technology and Design,edu,651cafb2620ab60a0e4f550c080231f20ae6d26e,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6360717,4D unconstrained real-time face recognition using a commodity depth camera,2012 -655,LFW,lfw,53.21967825,6.56251482,University of Groningen,edu,651cafb2620ab60a0e4f550c080231f20ae6d26e,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6360717,4D unconstrained real-time face recognition using a commodity depth camera,2012 -656,LFW,lfw,50.0764296,14.41802312,Czech Technical University,edu,56e25358ebfaf8a8b3c7c33ed007e24f026065d0,citation,https://doi.org/10.1007/s10994-015-5541-9,V-shaped interval insensitive loss for ordinal classification,2015 -657,LFW,lfw,32.0565957,118.77408833,Nanjing University,edu,ce37e11f4046a4b766b0e3228870ae4f26dddd67,citation,http://pdfs.semanticscholar.org/ce37/e11f4046a4b766b0e3228870ae4f26dddd67.pdf,Learning One-Shot Exemplar SVM from the Web for Face Verification,2014 -658,LFW,lfw,49.10184375,8.4331256,Karlsruhe Institute of Technology,edu,ab0d227b63b702ba80f70fd053175cd1b2fd28cc,citation,https://pdfs.semanticscholar.org/0eed/cda8981740ae2c34ad5809dbdfcd817f2518.pdf,Boosting Pseudo Census Transform Features for Face Alignment,2011 -659,LFW,lfw,40.8419836,-73.94368971,Columbia University,edu,4c170a0dcc8de75587dae21ca508dab2f9343974,citation,http://pdfs.semanticscholar.org/73a8/1d311eedac8dea3ca24dc15b6990fa4a725e.pdf,FaceTracer: A Search Engine for Large Collections of Images with Faces,2008 -660,LFW,lfw,40.4319722,-86.92389368,Purdue University,edu,65f0b05052c3145a58c2653821e5429ca62555ce,citation,https://arxiv.org/pdf/1810.11580.pdf,Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples,2018 -661,LFW,lfw,37.3936717,-122.0807262,Facebook,company,edfce091688bc88389dd4877950bd58e00ff1253,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6553700,A talking profile to distinguish identical twins,2013 -662,LFW,lfw,1.2962018,103.77689944,National University of Singapore,edu,edfce091688bc88389dd4877950bd58e00ff1253,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6553700,A talking profile to distinguish identical twins,2013 -663,LFW,lfw,51.5247272,-0.03931035,Queen Mary University of London,edu,1e3068886b138304ec5a7296702879cc8788143d,citation,http://doi.org/10.1007/s11263-013-0630-3,Active Rare Class Discovery and Classification Using Dirichlet Processes,2013 -664,LFW,lfw,32.77824165,34.99565673,Open University of Israel,edu,566a39d753c494f57b4464d6bde61bf3593f7ceb,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2013.43,A Critical Review of Action Recognition Benchmarks,2013 -665,LFW,lfw,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,16fadde3e68bba301f9829b3f99157191106bd0f,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4562953,Utility data annotation with Amazon Mechanical Turk,2008 -666,LFW,lfw,42.4505507,-76.4783513,Cornell University,edu,bd379f8e08f88729a9214260e05967f4ca66cd65,citation,https://arxiv.org/pdf/1711.06148.pdf,Learning Compositional Visual Concepts with Mutual Consistency,2017 -667,LFW,lfw,37.8687126,-122.25586815,"University of California, Berkeley",edu,2ff9ffedfc59422a8c7dac418a02d1415eec92f1,citation,http://pdfs.semanticscholar.org/6e3b/778ad384101f792284b42844518f620143aa.pdf,Face Verification Using Boosted Cross-Image Features,2013 -668,LFW,lfw,28.59899755,-81.19712501,University of Central Florida,edu,2ff9ffedfc59422a8c7dac418a02d1415eec92f1,citation,http://pdfs.semanticscholar.org/6e3b/778ad384101f792284b42844518f620143aa.pdf,Face Verification Using Boosted Cross-Image Features,2013 -669,LFW,lfw,42.718568,-84.47791571,Michigan State University,edu,e22adcd2a6a7544f017ec875ce8f89d5c59e09c8,citation,https://arxiv.org/pdf/1807.11936.pdf,Gender Privacy: An Ensemble of Semi Adversarial Networks for Confounding Arbitrary Gender Classifiers,2018 -670,LFW,lfw,38.6480445,-90.3099667,Washington University,edu,22e678d3e915218a7c09af0d1602e73080658bb7,citation,http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/IEEE_CVPR_2009_WS/data/papers/04/13.pdf,Adventures in archiving and using three years of webcam images,2009 -671,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,c3c463a9ee464bb610423b7203300a83a166b500,citation,https://doi.org/10.1109/ICIP.2014.7025069,Transform-invariant dictionary learning for face recognition,2014 -672,LFW,lfw,33.776033,-84.39884086,Georgia Institute of Technology,edu,b75eecc879da38138bf3ace9195ae1613fb6e3cc,citation,https://doi.org/10.1007/s10278-015-9808-2,Improvement in Detection of Wrong-Patient Errors When Radiologists Include Patient Photographs in Their Interpretation of Portable Chest Radiographs,2015 -673,LFW,lfw,-27.49741805,153.01316956,University of Queensland,edu,2af19b5ff2ca428fa42ef4b85ddbb576b5d9a5cc,citation,http://pdfs.semanticscholar.org/2af1/9b5ff2ca428fa42ef4b85ddbb576b5d9a5cc.pdf,Multi-Region Probabilistic Histograms for Robust and Scalable Identity Inference,2009 -674,LFW,lfw,49.10184375,8.4331256,Karlsruhe Institute of Technology,edu,919bdc161485615d5ee571b1585c1eb0539822c8,citation,http://ieeexplore.ieee.org/document/6460332/,A ranking model for face alignment with Pseudo Census Transform,2012 -675,LFW,lfw,40.00229045,116.32098908,Tsinghua University,edu,a3a2f3803bf403262b56ce88d130af15e984fff0,citation,http://pdfs.semanticscholar.org/e538/e1f6557d2920b449249606f909b665fbb924.pdf,Building a Compact Relevant Sample Coverage for Relevance Feedback in Content-Based Image Retrieval,2008 -676,LFW,lfw,24.96841805,121.19139696,National Central University,edu,a192845a7695bdb372cccf008e6590a14ed82761,citation,https://doi.org/10.1109/TIP.2014.2321495,A Novel Local Pattern Descriptor—Local Vector Pattern in High-Order Derivative Space for Face Recognition,2014 -677,LFW,lfw,22.1240187,113.54510901,University of Macau,edu,8db9188e5137e167bffb3ee974732c1fe5f7a7dc,citation,https://doi.org/10.1109/TIP.2016.2612885,Tree-Structured Nuclear Norm Approximation With Applications to Robust Face Recognition,2016 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face and pose recovery,2010 -682,LFW,lfw,1.3484104,103.68297965,Nanyang Technological University,edu,28be652db01273289499bc6e56379ca0237506c0,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/3B_018_ext.pdf,FaLRR: A fast low rank representation solver,2015 -683,LFW,lfw,50.8142701,8.771435,Philipps-Universität Marburg,edu,5981c309bd0ffd849c51b1d8a2ccc481a8ec2f5c,citation,https://doi.org/10.1109/ICT.2017.7998256,SmartFace: Efficient face detection on smartphones for wireless on-demand emergency networks,2017 -684,LFW,lfw,40.0044795,116.370238,Chinese Academy of Sciences,edu,1a40c2a2d17c52c8b9d20648647d0886e30a60fa,citation,https://doi.org/10.1109/ICPR.2016.7900283,Hybrid hypergraph construction for facial expression recognition,2016 -685,LFW,lfw,24.8186587,67.0316585,Shaheed Zulfikar Ali Bhutto Institute of Science and Technology,edu,70580ed8bc482cad66e059e838e4a779081d1648,citation,http://pdfs.semanticscholar.org/7058/0ed8bc482cad66e059e838e4a779081d1648.pdf,Gender Classification using Multi-Level Wavelets on Real World Face Images,2013 -686,LFW,lfw,37.4102193,-122.05965487,Carnegie Mellon University,edu,9fc04a13eef99851136eadff52e98eb9caac919d,citation,http://pdfs.semanticscholar.org/9fc0/4a13eef99851136eadff52e98eb9caac919d.pdf,Rethinking the Camera Pipeline for Computer Vision,2017 -687,LFW,lfw,42.4505507,-76.4783513,Cornell University,edu,9fc04a13eef99851136eadff52e98eb9caac919d,citation,http://pdfs.semanticscholar.org/9fc0/4a13eef99851136eadff52e98eb9caac919d.pdf,Rethinking the Camera Pipeline for Computer Vision,2017 -688,LFW,lfw,40.51865195,-74.44099801,State University of New Jersey,edu,0ca66283f4fb7dbc682f789fcf6d6732006befd5,citation,http://pdfs.semanticscholar.org/0ca6/6283f4fb7dbc682f789fcf6d6732006befd5.pdf,Active Dictionary Learning for Image Representation,2015 -689,LFW,lfw,65.0592157,25.46632601,University of Oulu,edu,761304bbd259a9e419a2518193e1ff1face9fd2d,citation,https://doi.org/10.1007/978-3-642-33885-4_57,Robust and Computationally Efficient Face Detection Using Gaussian Derivative Features of Higher Orders,2012 -690,LFW,lfw,13.0222347,77.56718325,Indian Institute of Science Bangalore,edu,f5af3c28b290dc797c499283e2d0662570f9ed02,citation,https://pdfs.semanticscholar.org/f5af/3c28b290dc797c499283e2d0662570f9ed02.pdf,GenLR-Net : Deep framework for very low resolution face and object recognition with generalization to unseen categories,2018 -691,LFW,lfw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,a0d6390dd28d802152f207940c7716fe5fae8760,citation,http://pdfs.semanticscholar.org/a0d6/390dd28d802152f207940c7716fe5fae8760.pdf,Bayesian Face Revisited: A Joint Formulation,2012 -692,LFW,lfw,31.83907195,117.26420748,University of Science and Technology of China,edu,a0d6390dd28d802152f207940c7716fe5fae8760,citation,http://pdfs.semanticscholar.org/a0d6/390dd28d802152f207940c7716fe5fae8760.pdf,Bayesian Face Revisited: A Joint Formulation,2012 -693,LFW,lfw,45.7833244,4.8781984,University of Lyon,edu,54ba18952fe36c9be9f2ab11faecd43d123b389b,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7163085,Triangular similarity metric learning for face verification,2015 -694,LFW,lfw,-27.5953995,-48.6154218,University of Campinas,edu,a2bd81be79edfa8dcfde79173b0a895682d62329,citation,http://pdfs.semanticscholar.org/a2bd/81be79edfa8dcfde79173b0a895682d62329.pdf,Multi-Objective Vehicle Routing Problem Applied to Large Scale Post Office Deliveries,2017 -695,LFW,lfw,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,4cfd770ccecae1c0b4248bc800d7fd35c817bbbd,citation,https://pdfs.semanticscholar.org/8774/e206564df3bf9050f8c2be6b434cc2469c5b.pdf,A Discriminative Feature Learning Approach for Deep Face Recognition,2016 -696,LFW,lfw,22.42031295,114.20788644,Chinese University of Hong Kong,edu,4cfd770ccecae1c0b4248bc800d7fd35c817bbbd,citation,https://pdfs.semanticscholar.org/8774/e206564df3bf9050f8c2be6b434cc2469c5b.pdf,A Discriminative Feature Learning Approach for Deep Face Recognition,2016 -697,LFW,lfw,43.08250655,-77.67121663,Rochester Institute of Technology,edu,69b2a7533e38c2c8c9a0891a728abb423ad2c7e7,citation,https://doi.org/10.1016/j.imavis.2013.03.003,Manifold based sparse representation for facial understanding in natural images,2013 -698,LFW,lfw,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,0df0d1adea39a5bef318b74faa37de7f3e00b452,citation,https://scalable.mpi-inf.mpg.de/files/2015/09/zhang_CVPR15.pdf,Appearance-based gaze estimation in the wild,2015 -699,LFW,lfw,59.34986645,18.07063213,"KTH Royal Institute of Technology, Stockholm",edu,633101e794d7b80f55f466fd2941ea24595e10e6,citation,https://pdfs.semanticscholar.org/6331/01e794d7b80f55f466fd2941ea24595e10e6.pdf,Face Attribute Prediction with classification CNN,2016 -700,LFW,lfw,39.1254938,-77.22293475,National Institute of Standards and Technology,edu,089b5e8eb549723020b908e8eb19479ba39812f5,citation,http://www.face-recognition-challenge.com/RobustnessOfDCNN-preprint.pdf,A Cross Benchmark Assessment of a Deep Convolutional Neural Network for Face Recognition,2017 -701,LFW,lfw,-27.49741805,153.01316956,University of Queensland,edu,f27fd2a1bc229c773238f1912db94991b8bf389a,citation,https://doi.org/10.1109/IVCNZ.2016.7804414,How do you develop a face detector for the unconstrained environment?,2016 -702,LFW,lfw,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 -703,LFW,lfw,40.00229045,116.32098908,Tsinghua University,edu,a52a69bf304d49fba6eac6a73c5169834c77042d,citation,https://doi.org/10.1109/LSP.2017.2789251,Margin Loss: Making Faces More Separable,2018 -704,LFW,lfw,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 -705,LFW,lfw,40.00229045,116.32098908,Tsinghua University,edu,93eb3963bc20e28af26c53ef3bce1e76b15e3209,citation,https://doi.org/10.1109/ICIP.2017.8296992,Occlusion robust face recognition based on mask learning,2017 -706,LFW,lfw,51.49887085,-0.17560797,Imperial College London,edu,a06b6d30e2b31dc600f622ab15afe5e2929581a7,citation,https://ibug.doc.ic.ac.uk/media/uploads/documents/2209.pdf,Robust Joint and Individual Variance Explained,2017 -707,LFW,lfw,51.59029705,-0.22963221,Middlesex University,edu,a06b6d30e2b31dc600f622ab15afe5e2929581a7,citation,https://ibug.doc.ic.ac.uk/media/uploads/documents/2209.pdf,Robust Joint and Individual Variance Explained,2017 -708,LFW,lfw,39.9601488,116.35193921,Beijing 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University,edu,c907104680ad53bdc673f2648d713e4d26335825,citation,http://doi.acm.org/10.1145/3077286.3077304,Dataset and Metrics for Adult Age-Progression Evaluation,2017 -71,MORPH Commercial,morph,34.2375581,-77.9270129,University of North Carolina Wilmington,edu,c907104680ad53bdc673f2648d713e4d26335825,citation,http://doi.acm.org/10.1145/3077286.3077304,Dataset and Metrics for Adult Age-Progression Evaluation,2017 -72,MORPH Commercial,morph,37.5600406,126.9369248,Yonsei University,edu,fde41dc4ec6ac6474194b99e05b43dd6a6c4f06f,citation,https://arxiv.org/pdf/1809.01990.pdf,Multi-Expert Gender Classification on Age Group by Integrating Deep Neural Networks,2018 -73,MORPH Commercial,morph,34.2375581,-77.9270129,University of North Carolina Wilmington,edu,31a36014354ee7c89aa6d94e656db77922b180a5,citation,http://doi.acm.org/10.1145/2304496.2304509,An interactive tool for extremely dense landmarking of faces,2012 -74,MORPH Commercial,morph,37.5901411,127.0362318,Korea 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estimation,2010 -92,MORPH Commercial,morph,34.2249827,-77.86907744,University of North Carolina at Wilmington,edu,97c59db934ff85c60c460a4591106682b5ab9caa,citation,https://doi.org/10.1109/BTAS.2012.6374568,Extremely dense face registration: Comparing automatic landmarking algorithms for general and ethno-gender models,2012 -93,MORPH Commercial,morph,43.2213516,-75.4085577,"Air Force Research Lab, Rome, NY",mil,834736698f2cc5c221c22369abe95515243a9fc3,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6996249,GARP-face: Balancing privacy protection and utility preservation in face de-identification,2014 -94,MORPH Commercial,morph,39.95472495,-75.15346905,Temple University,edu,834736698f2cc5c221c22369abe95515243a9fc3,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6996249,GARP-face: Balancing privacy protection and utility preservation in face de-identification,2014 -95,MORPH Commercial,morph,32.0575279,118.78682252,Southeast University,edu,3cb488a3b71f221a8616716a1fc2b951dd0de549,citation,http://doi.ieeecomputersociety.org/10.1109/ICPR.2014.764,Facial Age Estimation by Adaptive Label Distribution Learning,2014 -96,MORPH Commercial,morph,22.3386304,114.2620337,Hong Kong University of Science and Technology,edu,8000c4f278e9af4d087c0d0895fff7012c5e3d78,citation,https://www.cse.ust.hk/~yuzhangcse/papers/Zhang_Yeung_CVPR10.pdf,Multi-task warped Gaussian process for personalized age estimation,2010 -97,MORPH Commercial,morph,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,59fe66eeb06d1a7e1496a85f7ffc7b37512cd7e5,citation,http://doi.ieeecomputersociety.org/10.1109/ICME.2016.7552862,Robust feature encoding for age-invariant face recognition,2016 -98,MORPH Commercial,morph,23.0502042,113.39880323,South China University of Technology,edu,4bd3de97b256b96556d19a5db71dda519934fd53,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.529,Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition,2016 -99,MORPH Commercial,morph,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,4bd3de97b256b96556d19a5db71dda519934fd53,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.529,Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition,2016 -100,MORPH Commercial,morph,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,1d3dd9aba79a53390317ec1e0b7cd742cba43132,citation,http://www.cise.ufl.edu/~dihong/assets/Gong_A_Maximum_Entropy_2015_CVPR_paper.pdf,A maximum entropy feature descriptor for age invariant face recognition,2015 -101,MORPH Commercial,morph,40.0044795,116.370238,Chinese Academy of Sciences,edu,1d3dd9aba79a53390317ec1e0b7cd742cba43132,citation,http://www.cise.ufl.edu/~dihong/assets/Gong_A_Maximum_Entropy_2015_CVPR_paper.pdf,A maximum entropy feature descriptor for age invariant face recognition,2015 -102,MORPH Commercial,morph,22.42031295,114.20788644,Chinese University of Hong Kong,edu,1d3dd9aba79a53390317ec1e0b7cd742cba43132,citation,http://www.cise.ufl.edu/~dihong/assets/Gong_A_Maximum_Entropy_2015_CVPR_paper.pdf,A maximum entropy feature descriptor for age invariant face recognition,2015 -103,MORPH Commercial,morph,32.0575279,118.78682252,Southeast University,edu,1c530de1a94ac70bf9086e39af1712ea8d2d2781,citation,http://pdfs.semanticscholar.org/1c53/0de1a94ac70bf9086e39af1712ea8d2d2781.pdf,Sparsity Conditional Energy Label Distribution Learning for Age Estimation,2016 -104,MORPH Commercial,morph,37.4102193,-122.05965487,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 -105,MORPH Commercial,morph,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 -106,MORPH Commercial,morph,57.6252103,39.8845656,Yaroslavl State University,edu,cfaf61bacf61901b7e1ac25b779a1f87c1e8cf7f,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6737950,Application for video analysis based on machine learning and computer vision algorithms,2013 -107,MORPH Commercial,morph,51.49887085,-0.17560797,Imperial College London,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017 -108,MORPH Commercial,morph,51.59029705,-0.22963221,Middlesex University,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017 -109,MORPH 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University,edu,2050847bc7a1a0453891f03aeeb4643e360fde7d,citation,https://cvhci.anthropomatik.kit.edu/~mtapaswi/papers/ICMR2015.pdf,Accio: A Data Set for Face Track Retrieval in Movies Across Age,2015 -129,MORPH Commercial,morph,49.10184375,8.4331256,Karlsruhe Institute of Technology,edu,2050847bc7a1a0453891f03aeeb4643e360fde7d,citation,https://cvhci.anthropomatik.kit.edu/~mtapaswi/papers/ICMR2015.pdf,Accio: A Data Set for Face Track Retrieval in Movies Across Age,2015 -130,MORPH Commercial,morph,40.62984145,22.9588935,Aristotle University of Thessaloniki,edu,3cc46bf79fb9225cf308815c7d41c8dd5625cc29,citation,http://poseidon.csd.auth.gr/papers/PUBLISHED/CONFERENCE/pdf/2016/Pantraki2016.pdf,Age interval and gender prediction using PARAFAC2 applied to speech utterances,2016 -131,MORPH Commercial,morph,34.67567405,33.04577648,Cyprus University of Technology,edu,3cc46bf79fb9225cf308815c7d41c8dd5625cc29,citation,http://poseidon.csd.auth.gr/papers/PUBLISHED/CONFERENCE/pdf/2016/Pantraki2016.pdf,Age interval and gender prediction using PARAFAC2 applied to speech utterances,2016 -132,MORPH Commercial,morph,23.09461185,113.28788994,Sun Yat-Sen University,edu,189e5a2fa51ed471c0e7227d82dffb52736070d8,citation,https://doi.org/10.1109/ICIP.2017.8296995,Cross-age face recognition using reference coding with kernel direct discriminant analysis,2017 -133,MORPH Commercial,morph,42.357757,-83.06286711,Wayne State University,edu,4f1249369127cc2e2894f6b2f1052d399794919a,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8239663,Deep Age Estimation: From Classification to Ranking,2018 -134,MORPH Commercial,morph,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,cd6aaa37fffd0b5c2320f386be322b8adaa1cc68,citation,https://arxiv.org/pdf/1804.06655.pdf,Deep Face Recognition: A Survey,2018 -135,MORPH 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Rome,edu,62750d78e819d745b9200b0c5c35fcae6fb9f404,citation,http://doi.org/10.1007/s11042-016-4085-8,Leveraging implicit demographic information for face recognition using a multi-expert system,2016 -146,MORPH Commercial,morph,40.845492,14.2578058,University of Naples Federico II,edu,62750d78e819d745b9200b0c5c35fcae6fb9f404,citation,http://doi.org/10.1007/s11042-016-4085-8,Leveraging implicit demographic information for face recognition using a multi-expert system,2016 -147,MORPH Commercial,morph,25.01353105,121.54173736,National Taiwan University of Science and Technology,edu,e4c3587392d477b7594086c6f28a00a826abf004,citation,https://doi.org/10.1109/ICIP.2017.8296998,Face recognition by facial attribute assisted network,2017 -148,MORPH Commercial,morph,39.9922379,116.30393816,Peking University,edu,c4ca092972abb74ee1c20b7cae6e69c654479e2c,citation,https://doi.org/10.1109/ICIP.2016.7532960,Linear canonical correlation analysis based ranking approach for facial age estimation,2016 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Thessaloniki,edu,1fd3dbb6e910708fa85c8a86e17ba0b6fef5617c,citation,http://pdfs.semanticscholar.org/1fd3/dbb6e910708fa85c8a86e17ba0b6fef5617c.pdf,Age interval and gender prediction using PARAFAC2 on speech recordings and face images,2016 -164,MORPH Commercial,morph,40.00229045,116.32098908,Tsinghua University,edu,6c6f0e806e4e286f3b18b934f42c72b67030ce17,citation,https://doi.org/10.1109/FG.2011.5771345,Combination of age and head pose for adult face verification,2011 -165,MORPH Commercial,morph,46.5190557,6.5667576,"Swiss Federal, Institute of Technology, Lausanne",edu,6c6f0e806e4e286f3b18b934f42c72b67030ce17,citation,https://doi.org/10.1109/FG.2011.5771345,Combination of age and head pose for adult face verification,2011 -166,MORPH Commercial,morph,52.6221571,1.2409136,University of East Anglia,edu,05a0d04693b2a51a8131d195c68ad9f5818b2ce1,citation,http://pdfs.semanticscholar.org/05a0/d04693b2a51a8131d195c68ad9f5818b2ce1.pdf,Dual-reference Face Retrieval: What Does He/She Look Like at 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is the influence across race and gender?,2010 -171,MORPH Commercial,morph,1.3484104,103.68297965,Nanyang Technological University,edu,b6a23f72007cb40223d7e1e1cc47e466716de945,citation,https://doi.org/10.1109/CVPRW.2010.5544598,Ordinary preserving manifold analysis for human age estimation,2010 -172,MORPH Commercial,morph,60.7897318,10.6821927,"Norwegian Biometrics Lab, NTNU, Norway",edu,0647c9d56cf11215894d57d677997826b22f6a13,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8401557,Transgender face recognition with off-the-shelf pre-trained CNNs: A comprehensive study,2018 -173,MORPH Commercial,morph,52.3553655,4.9501644,University of Amsterdam,edu,935a7793cbb8f102924fa34fce1049727de865c2,citation,https://doi.org/10.1109/ICIP.2015.7351554,Age estimation under changes in image quality: An experimental study,2015 -174,MORPH Commercial,morph,40.01407945,-105.26695944,"University of Colorado, 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University,edu,55bc7abcef8266d76667896bbc652d081d00f797,citation,http://www.cse.msu.edu/~rossarun/pubs/ChenCosmeticsGenderAge_VISAPP2014.pdf,Impact of facial cosmetics on automatic gender and age estimation algorithms,2014 -244,MORPH Commercial,morph,42.718568,-84.47791571,Michigan State University,edu,55bc7abcef8266d76667896bbc652d081d00f797,citation,http://www.cse.msu.edu/~rossarun/pubs/ChenCosmeticsGenderAge_VISAPP2014.pdf,Impact of facial cosmetics on automatic gender and age estimation algorithms,2014 -245,MORPH Commercial,morph,39.65404635,-79.96475355,West Virginia University,edu,7a65fc9e78eff3ab6062707deaadde024d2fad40,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/papers/Zhu_A_Study_on_ICCV_2015_paper.pdf,A Study on Apparent Age Estimation,2015 -246,MORPH Commercial,morph,42.357757,-83.06286711,Wayne State University,edu,28d99dc2d673d62118658f8375b414e5192eac6f,citation,http://www.cs.wayne.edu/~mdong/cvpr17.pdf,Using Ranking-CNN for Age 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a/site/datasets/final/morph.json +++ b/site/datasets/final/morph.json @@ -1 +1 @@ -{"id": "9055b155cbabdce3b98e16e5ac9c0edf00f9552f", "dataset": {"key": "morph", "name_short": "MORPH Commercial", "using": "N", "ft_share": "1", "subset_of": "", "superset_of": "", "name_full": "Craniofacial Longitudinal Morphological Face Dataset", "url": "https://ebill.uncw.edu/C20231_ustores/web/classic/store_main.jsp?STOREID=4", "added_on": "", "faces": "", "pdf_paper": "Y", "comments": "mugshots", "": "", "relevance": ""}, "statistics": {"key": "morph", "name": "MORPH Commercial", "berit": "", "charlie": "Y", "adam": "", "priority": "", "wild": "", "indoor": "", "outdoor": "", "cyberspace": "", "names": "", "downloaded": "", "year_start": "1962", "year_end": "1998", "year_published": "2006", "ongoing": "", "images": "", "videos": "", "faces_unique": "515 ", "total_faces": "", "img_per_person": "", "num_cameras": "", "faces_persons": "1724", "female": "294 ", "male": "1,430 ", "landmarks": "", 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Non-Commercial,morph_nc,29.6328784,-82.3490133,University of Florida,edu,df7312cbabb7d75d915ba0d91dea77100ded5c56,citation,https://arxiv.org/pdf/1811.06446.pdf,Preliminary Studies on a Large Face Database,2018 -31,MORPH Non-Commercial,morph_nc,31.83907195,117.26420748,University of Science and Technology of China,edu,56c700693b63e3da3b985777da6d9256e2e0dc21,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/1A_079.pdf,Global refinement of random forest,2015 -32,MORPH Non-Commercial,morph_nc,40.00229045,116.32098908,Tsinghua University,edu,1e344b99583b782e3eaf152cdfa15f217b781181,citation,http://doi.acm.org/10.1145/2499788.2499789,A new biologically inspired active appearance model for face age estimation by using local ordinal ranking,2013 -33,MORPH Non-Commercial,morph_nc,39.94976005,116.33629046,Beijing Jiaotong University,edu,4b9ec224949c79a980a5a66664d0ac6233c3d575,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7565501,Human Facial Age 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partially dense aging databases,2009 -37,MORPH Non-Commercial,morph_nc,39.9922379,116.30393816,Peking University,edu,bd8b7599acf53e3053aa27cfd522764e28474e57,citation,http://www.jdl.ac.cn/doc/2009/iccv09_Learning%20Long%20Term%20Face%20Aging%20Patterns%20from%20Partially%20Dense%20Aging%20Databases.pdf,Learning long term face aging patterns from partially dense aging databases,2009 -38,MORPH Non-Commercial,morph_nc,43.614386,7.071125,EURECOM,edu,70569810e46f476515fce80a602a210f8d9a2b95,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2016.105,Apparent Age Estimation from Face Images Combining General and Children-Specialized Deep Learning Models,2016 -39,MORPH Non-Commercial,morph_nc,39.9213097,32.7988233,"TOBB Economy and Technology University, Ankara, Turkey",edu,cc1ed45b02d7fffb42a0fd8cffe5f11792b6ea74,citation,https://doi.org/10.1109/SIU.2016.7495874,Analysis of the effect of image resolution on automatic face gender and age classification,2016 -40,MORPH Non-Commercial,morph_nc,-33.91758275,151.23124025,University of New South Wales,edu,29631ca6cff21c9199c70bcdbbcd5f812d331a96,citation,http://pdfs.semanticscholar.org/2963/1ca6cff21c9199c70bcdbbcd5f812d331a96.pdf,Error Rates in Users of Automatic Face Recognition Software,2015 -41,MORPH Non-Commercial,morph_nc,-33.88890695,151.18943366,University of Sydney,edu,29631ca6cff21c9199c70bcdbbcd5f812d331a96,citation,http://pdfs.semanticscholar.org/2963/1ca6cff21c9199c70bcdbbcd5f812d331a96.pdf,Error Rates in Users of Automatic Face Recognition Software,2015 -42,MORPH Non-Commercial,morph_nc,42.718568,-84.47791571,Michigan State University,edu,1a53ca294bbe5923c46a339955e8207907e9c8c6,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7273870,What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics,2016 -43,MORPH Non-Commercial,morph_nc,43.614386,7.071125,EURECOM,edu,1a53ca294bbe5923c46a339955e8207907e9c8c6,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7273870,What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics,2016 -44,MORPH Non-Commercial,morph_nc,40.4319722,-86.92389368,Purdue University,edu,c7c53d75f6e963b403057d8ba5952e4974a779ad,citation,https://pdfs.semanticscholar.org/c7c5/3d75f6e963b403057d8ba5952e4974a779ad.pdf,Aging effects in automated face recognition,2018 -45,MORPH Non-Commercial,morph_nc,41.02451875,28.97697953,Bahçeşehir University,edu,0c2370e156a4eb8d84a5fdb049c5a894c3431f1c,citation,https://doi.org/10.1109/CIBIM.2014.7015437,Biometric template update under facial aging,2014 -46,MORPH Non-Commercial,morph_nc,53.22853665,-0.54873472,University of Lincoln,edu,0c2370e156a4eb8d84a5fdb049c5a894c3431f1c,citation,https://doi.org/10.1109/CIBIM.2014.7015437,Biometric template update under facial aging,2014 -47,MORPH Non-Commercial,morph_nc,46.0810723,13.2119474,University of Udine,edu,0c2370e156a4eb8d84a5fdb049c5a894c3431f1c,citation,https://doi.org/10.1109/CIBIM.2014.7015437,Biometric template update under facial aging,2014 -48,MORPH Non-Commercial,morph_nc,25.0410728,121.6147562,Institute of Information Science,edu,1c17450c4d616e1e1eece248c42eba4f87de9e0d,citation,http://pdfs.semanticscholar.org/d269/39a00a8d3964de612cd3faa86764343d5622.pdf,Automatic Age Estimation from Face Images via Deep Ranking,2015 -49,MORPH Non-Commercial,morph_nc,43.47061295,-80.54724732,University of Waterloo,edu,f2902f5956d7e2dca536d9131d4334f85f52f783,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6460191,Facial age estimation using Clustered Multi-task Support Vector Regression Machine,2012 -50,MORPH Non-Commercial,morph_nc,39.65404635,-79.96475355,West Virginia University,edu,ba2bbef34f05551291410103e3de9e82fdf9dddd,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Guo_A_Study_on_2014_CVPR_paper.pdf,A Study on Cross-Population Age Estimation,2014 -51,MORPH Non-Commercial,morph_nc,31.32235655,121.38400941,Shanghai University,edu,d454ad60b061c1a1450810a0f335fafbfeceeccc,citation,https://arxiv.org/pdf/1712.07195.pdf,Deep Regression Forests for Age Estimation,2017 -52,MORPH Non-Commercial,morph_nc,42.718568,-84.47791571,Michigan State University,edu,ad2cb5c255e555d9767d526721a4c7053fa2ac58,citation,https://arxiv.org/pdf/1711.03990.pdf,Longitudinal Study of Child Face Recognition,2018 -53,MORPH Non-Commercial,morph_nc,39.95472495,-75.15346905,Temple University,edu,0cf2eecf20cfbcb7f153713479e3206670ea0e9c,citation,https://arxiv.org/pdf/1806.08906.pdf,Privacy-Protective-GAN for Face De-identification,2018 -54,MORPH Non-Commercial,morph_nc,31.32235655,121.38400941,Shanghai University,edu,c0b02be66a5a1907e8cfb8117de50f80b90a65a8,citation,http://doi.acm.org/10.1145/2808492.2808523,Manifold learning in sparse selected feature subspaces,2015 -55,MORPH Non-Commercial,morph_nc,47.6423318,-122.1369302,Microsoft,company,ff012c56b9b1de969328dacd13e26b7138ff298b,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7762921,Facial Age Estimation With Age Difference,2017 -56,MORPH Non-Commercial,morph_nc,1.2962018,103.77689944,National University of Singapore,edu,ff012c56b9b1de969328dacd13e26b7138ff298b,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7762921,Facial Age Estimation With Age Difference,2017 -57,MORPH Non-Commercial,morph_nc,31.846918,117.29053367,Hefei University of Technology,edu,ff012c56b9b1de969328dacd13e26b7138ff298b,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7762921,Facial Age Estimation With Age Difference,2017 -58,MORPH Non-Commercial,morph_nc,1.3484104,103.68297965,Nanyang Technological University,edu,ff012c56b9b1de969328dacd13e26b7138ff298b,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7762921,Facial Age Estimation With Age Difference,2017 -59,MORPH Non-Commercial,morph_nc,40.00229045,116.32098908,Tsinghua University,edu,2149d49c84a83848d6051867290d9c8bfcef0edb,citation,https://doi.org/10.1109/TIFS.2017.2746062,Label-Sensitive Deep Metric Learning for Facial Age Estimation,2018 -60,MORPH Non-Commercial,morph_nc,25.0410728,121.6147562,Institute of Information Science,edu,c44c84540db1c38ace232ef34b03bda1c81ba039,citation,http://pdfs.semanticscholar.org/c44c/84540db1c38ace232ef34b03bda1c81ba039.pdf,Cross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval,2014 -61,MORPH Non-Commercial,morph_nc,25.01682835,121.53846924,National Taiwan University,edu,c44c84540db1c38ace232ef34b03bda1c81ba039,citation,http://pdfs.semanticscholar.org/c44c/84540db1c38ace232ef34b03bda1c81ba039.pdf,Cross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval,2014 -62,MORPH Non-Commercial,morph_nc,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 -63,MORPH Non-Commercial,morph_nc,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 -64,MORPH Non-Commercial,morph_nc,42.718568,-84.47791571,Michigan State University,edu,2f2406551c693d616a840719ae1e6ea448e2f5d3,citation,http://biometrics.cse.msu.edu/Presentations/CharlesOtto_ICB13_AgeEstimationFaceImages_HumanVsMachinePerformance.pdf,Age estimation from face images: Human vs. machine performance,2013 -65,MORPH Non-Commercial,morph_nc,1.3037257,103.7737763,"Advanced Digital Sciences Center, Singapore",edu,15fbb5fc3bdd692a6b2dd737cce7f39f7c89a25c,citation,https://doi.org/10.1109/TMM.2011.2167317,Web Image and Video Mining Towards Universal and Robust Age Estimator,2011 -66,MORPH Non-Commercial,morph_nc,1.2962018,103.77689944,National University of Singapore,edu,15fbb5fc3bdd692a6b2dd737cce7f39f7c89a25c,citation,https://doi.org/10.1109/TMM.2011.2167317,Web Image and Video Mining Towards Universal and Robust Age Estimator,2011 -67,MORPH Non-Commercial,morph_nc,42.718568,-84.47791571,Michigan State University,edu,b446bcd7fb78adfe346cf7a01a38e4f43760f363,citation,http://pdfs.semanticscholar.org/b446/bcd7fb78adfe346cf7a01a38e4f43760f363.pdf,To appear in ICB 2018 Longitudinal Study of Child Face Recognition,2017 -68,MORPH Non-Commercial,morph_nc,42.718568,-84.47791571,Michigan State University,edu,c035c193eed5d72c7f187f0bc880a17d217dada0,citation,http://pdfs.semanticscholar.org/c035/c193eed5d72c7f187f0bc880a17d217dada0.pdf,"Local Gradient Gabor Pattern (LGGP) with Applications in Face Recognition, Cross-spectral Matching and Soft Biometrics",2013 -69,MORPH Non-Commercial,morph_nc,39.65404635,-79.96475355,West Virginia University,edu,c035c193eed5d72c7f187f0bc880a17d217dada0,citation,http://pdfs.semanticscholar.org/c035/c193eed5d72c7f187f0bc880a17d217dada0.pdf,"Local Gradient Gabor Pattern (LGGP) with Applications in Face Recognition, Cross-spectral Matching and Soft Biometrics",2013 -70,MORPH Non-Commercial,morph_nc,34.66869155,-82.83743476,Clemson University,edu,c907104680ad53bdc673f2648d713e4d26335825,citation,http://doi.acm.org/10.1145/3077286.3077304,Dataset and Metrics for Adult Age-Progression Evaluation,2017 -71,MORPH Non-Commercial,morph_nc,34.2375581,-77.9270129,University of North Carolina Wilmington,edu,c907104680ad53bdc673f2648d713e4d26335825,citation,http://doi.acm.org/10.1145/3077286.3077304,Dataset and Metrics for Adult Age-Progression Evaluation,2017 -72,MORPH Non-Commercial,morph_nc,37.5600406,126.9369248,Yonsei University,edu,fde41dc4ec6ac6474194b99e05b43dd6a6c4f06f,citation,https://arxiv.org/pdf/1809.01990.pdf,Multi-Expert Gender Classification on Age Group by Integrating Deep Neural Networks,2018 -73,MORPH Non-Commercial,morph_nc,34.2375581,-77.9270129,University of North Carolina Wilmington,edu,31a36014354ee7c89aa6d94e656db77922b180a5,citation,http://doi.acm.org/10.1145/2304496.2304509,An interactive tool for extremely dense landmarking of faces,2012 -74,MORPH Non-Commercial,morph_nc,37.5901411,127.0362318,Korea University,edu,4b519e2e88ccd45718b0fc65bfd82ebe103902f7,citation,http://biometrics.cse.msu.edu/Publications/Face/LiParkJain_DiscriminativeModelAgeInvariantFR_TIFS11.pdf,A Discriminative Model for Age Invariant Face Recognition,2011 -75,MORPH Non-Commercial,morph_nc,42.718568,-84.47791571,Michigan State University,edu,4b519e2e88ccd45718b0fc65bfd82ebe103902f7,citation,http://biometrics.cse.msu.edu/Publications/Face/LiParkJain_DiscriminativeModelAgeInvariantFR_TIFS11.pdf,A Discriminative Model for Age Invariant Face Recognition,2011 -76,MORPH Non-Commercial,morph_nc,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,4b519e2e88ccd45718b0fc65bfd82ebe103902f7,citation,http://biometrics.cse.msu.edu/Publications/Face/LiParkJain_DiscriminativeModelAgeInvariantFR_TIFS11.pdf,A Discriminative Model for Age Invariant Face Recognition,2011 -77,MORPH Non-Commercial,morph_nc,23.09461185,113.28788994,Sun Yat-Sen University,edu,23edcd0d2011d9c0d421193af061f2eb3e155da3,citation,http://doi.org/10.1007/s00371-015-1137-4,Facial age estimation by using stacked feature composition and selection,2015 -78,MORPH Non-Commercial,morph_nc,23.04436505,113.36668458,Guangzhou University,edu,23edcd0d2011d9c0d421193af061f2eb3e155da3,citation,http://doi.org/10.1007/s00371-015-1137-4,Facial age estimation by using stacked feature composition and selection,2015 -79,MORPH Non-Commercial,morph_nc,38.9530519,-77.3354508,"Cernium Corporation, Reston, VA, USA",company,604a281100784b4d5bc1a6db993d423abc5dc8f0,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5353681,Face Verification Across Age Progression Using Discriminative Methods,2010 -80,MORPH Non-Commercial,morph_nc,39.2899685,-76.62196103,University of Maryland,edu,604a281100784b4d5bc1a6db993d423abc5dc8f0,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5353681,Face Verification Across Age Progression Using Discriminative Methods,2010 -81,MORPH Non-Commercial,morph_nc,39.95472495,-75.15346905,Temple University,edu,604a281100784b4d5bc1a6db993d423abc5dc8f0,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5353681,Face Verification Across Age Progression Using Discriminative Methods,2010 -82,MORPH Non-Commercial,morph_nc,51.2975344,1.07296165,University of Kent,edu,6486b36c6f7fd7675257d26e896223a02a1881d9,citation,https://doi.org/10.1109/THMS.2014.2376874,Selective Review and Analysis of Aging Effects in Biometric System Implementation,2015 -83,MORPH Non-Commercial,morph_nc,22.42031295,114.20788644,Chinese University of Hong Kong,edu,16bce9f940bb01aa5ec961892cc021d4664eb9e4,citation,http://www.cise.ufl.edu/~dihong/assets/TIST-2014-10-0214.R2.pdf,Mutual Component Analysis for Heterogeneous Face Recognition,2016 -84,MORPH Non-Commercial,morph_nc,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,16bce9f940bb01aa5ec961892cc021d4664eb9e4,citation,http://www.cise.ufl.edu/~dihong/assets/TIST-2014-10-0214.R2.pdf,Mutual Component Analysis for Heterogeneous Face Recognition,2016 -85,MORPH Non-Commercial,morph_nc,34.67567405,33.04577648,Cyprus University of Technology,edu,9d3aa3b7d392fad596b067b13b9e42443bbc377c,citation,http://pdfs.semanticscholar.org/9d3a/a3b7d392fad596b067b13b9e42443bbc377c.pdf,Facial Biometric Templates and Aging: Problems and Challenges for Artificial Intelligence,2009 -86,MORPH Non-Commercial,morph_nc,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,217a21d60bb777d15cd9328970cab563d70b5d23,citation,http://www.cise.ufl.edu/~dihong/assets/iccv2013.pdf,Hidden Factor Analysis for Age Invariant Face Recognition,2013 -87,MORPH Non-Commercial,morph_nc,22.42031295,114.20788644,Chinese University of Hong Kong,edu,217a21d60bb777d15cd9328970cab563d70b5d23,citation,http://www.cise.ufl.edu/~dihong/assets/iccv2013.pdf,Hidden Factor Analysis for Age Invariant Face Recognition,2013 -88,MORPH Non-Commercial,morph_nc,32.0565957,118.77408833,Nanjing University,edu,b1bb517bd87a1212174033fc786b2237844b04e6,citation,https://doi.org/10.1016/j.neucom.2015.03.078,Cumulative attribute relation regularization learning for human age estimation,2015 -89,MORPH Non-Commercial,morph_nc,40.8419836,-73.94368971,Columbia University,edu,a0dc68c546e0fc72eb0d9ca822cf0c9ccb4b4c4f,citation,http://www.cs.columbia.edu/~neeraj/base/papers/nk_ijcb2011_fusion.pdf,Fusing with context: A Bayesian approach to combining descriptive attributes,2011 -90,MORPH Non-Commercial,morph_nc,34.2375581,-77.9270129,University of North Carolina Wilmington,edu,a0dc68c546e0fc72eb0d9ca822cf0c9ccb4b4c4f,citation,http://www.cs.columbia.edu/~neeraj/base/papers/nk_ijcb2011_fusion.pdf,Fusing with context: A Bayesian approach to combining descriptive attributes,2011 -91,MORPH Non-Commercial,morph_nc,1.3484104,103.68297965,Nanyang Technological University,edu,d119443de1d75cad384d897c2ed5a7b9c1661d98,citation,https://doi.org/10.1109/ICIP.2010.5650873,Cost-sensitive subspace learning for human age estimation,2010 -92,MORPH Non-Commercial,morph_nc,34.2249827,-77.86907744,University of North Carolina at Wilmington,edu,97c59db934ff85c60c460a4591106682b5ab9caa,citation,https://doi.org/10.1109/BTAS.2012.6374568,Extremely dense face registration: Comparing automatic landmarking algorithms for general and ethno-gender models,2012 -93,MORPH Non-Commercial,morph_nc,43.2213516,-75.4085577,"Air Force Research Lab, Rome, NY",mil,834736698f2cc5c221c22369abe95515243a9fc3,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6996249,GARP-face: Balancing privacy protection and utility preservation in face de-identification,2014 -94,MORPH Non-Commercial,morph_nc,39.95472495,-75.15346905,Temple University,edu,834736698f2cc5c221c22369abe95515243a9fc3,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6996249,GARP-face: Balancing privacy protection and utility preservation in face de-identification,2014 -95,MORPH Non-Commercial,morph_nc,32.0575279,118.78682252,Southeast University,edu,3cb488a3b71f221a8616716a1fc2b951dd0de549,citation,http://doi.ieeecomputersociety.org/10.1109/ICPR.2014.764,Facial Age Estimation by Adaptive Label Distribution Learning,2014 -96,MORPH Non-Commercial,morph_nc,22.3386304,114.2620337,Hong Kong University of Science and Technology,edu,8000c4f278e9af4d087c0d0895fff7012c5e3d78,citation,https://www.cse.ust.hk/~yuzhangcse/papers/Zhang_Yeung_CVPR10.pdf,Multi-task warped Gaussian process for personalized age estimation,2010 -97,MORPH Non-Commercial,morph_nc,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,59fe66eeb06d1a7e1496a85f7ffc7b37512cd7e5,citation,http://doi.ieeecomputersociety.org/10.1109/ICME.2016.7552862,Robust feature encoding for age-invariant face recognition,2016 -98,MORPH Non-Commercial,morph_nc,23.0502042,113.39880323,South China University of Technology,edu,4bd3de97b256b96556d19a5db71dda519934fd53,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.529,Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition,2016 -99,MORPH Non-Commercial,morph_nc,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,4bd3de97b256b96556d19a5db71dda519934fd53,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.529,Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition,2016 -100,MORPH Non-Commercial,morph_nc,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,1d3dd9aba79a53390317ec1e0b7cd742cba43132,citation,http://www.cise.ufl.edu/~dihong/assets/Gong_A_Maximum_Entropy_2015_CVPR_paper.pdf,A maximum entropy feature descriptor for age invariant face recognition,2015 -101,MORPH Non-Commercial,morph_nc,40.0044795,116.370238,Chinese Academy of Sciences,edu,1d3dd9aba79a53390317ec1e0b7cd742cba43132,citation,http://www.cise.ufl.edu/~dihong/assets/Gong_A_Maximum_Entropy_2015_CVPR_paper.pdf,A maximum entropy feature descriptor for age invariant face recognition,2015 -102,MORPH Non-Commercial,morph_nc,22.42031295,114.20788644,Chinese University of Hong Kong,edu,1d3dd9aba79a53390317ec1e0b7cd742cba43132,citation,http://www.cise.ufl.edu/~dihong/assets/Gong_A_Maximum_Entropy_2015_CVPR_paper.pdf,A maximum entropy feature descriptor for age invariant face recognition,2015 -103,MORPH Non-Commercial,morph_nc,32.0575279,118.78682252,Southeast University,edu,1c530de1a94ac70bf9086e39af1712ea8d2d2781,citation,http://pdfs.semanticscholar.org/1c53/0de1a94ac70bf9086e39af1712ea8d2d2781.pdf,Sparsity Conditional Energy Label Distribution Learning for Age Estimation,2016 -104,MORPH Non-Commercial,morph_nc,37.4102193,-122.05965487,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 -105,MORPH Non-Commercial,morph_nc,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 -106,MORPH Non-Commercial,morph_nc,57.6252103,39.8845656,Yaroslavl State University,edu,cfaf61bacf61901b7e1ac25b779a1f87c1e8cf7f,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6737950,Application for video analysis based on machine learning and computer vision algorithms,2013 -107,MORPH Non-Commercial,morph_nc,51.49887085,-0.17560797,Imperial College London,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017 -108,MORPH Non-Commercial,morph_nc,51.59029705,-0.22963221,Middlesex University,edu,54bb25a213944b08298e4e2de54f2ddea890954a,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017 -109,MORPH Non-Commercial,morph_nc,37.4102193,-122.05965487,Carnegie Mellon University,edu,17670b60dcfb5cbf8fdae0b266e18cf995f6014c,citation,http://arxiv.org/abs/1606.02254,Longitudinal Face Modeling via Temporal Deep Restricted Boltzmann Machines,2016 -110,MORPH Non-Commercial,morph_nc,45.57022705,-122.63709346,Concordia University,edu,17670b60dcfb5cbf8fdae0b266e18cf995f6014c,citation,http://arxiv.org/abs/1606.02254,Longitudinal Face Modeling via Temporal Deep Restricted Boltzmann Machines,2016 -111,MORPH Non-Commercial,morph_nc,46.0658836,11.1159894,University of Trento,edu,2fd96238a7e372146cdf6c2338edc932031dd1f0,citation,https://arxiv.org/pdf/1802.00237.pdf,Face Aging with Contextual Generative Adversarial Nets,2017 -112,MORPH Non-Commercial,morph_nc,1.2962018,103.77689944,National University of Singapore,edu,2fd96238a7e372146cdf6c2338edc932031dd1f0,citation,https://arxiv.org/pdf/1802.00237.pdf,Face Aging with Contextual Generative Adversarial Nets,2017 -113,MORPH Non-Commercial,morph_nc,51.44415765,7.26096541,Ruhr-University Bochum,edu,b249f10a30907a80f2a73582f696bc35ba4db9e2,citation,http://pdfs.semanticscholar.org/f06d/6161eef9325285b32356e1c4b5527479eb9b.pdf,Improved graph-based SFA: Information preservation complements the slowness principle,2016 -114,MORPH Non-Commercial,morph_nc,39.9808333,116.34101249,Beihang University,edu,8b266e68cc71f98ee42b04dc8f3e336c47f199cb,citation,https://arxiv.org/pdf/1711.10352.pdf,Learning Face Age Progression: A Pyramid Architecture of GANs,2017 -115,MORPH Non-Commercial,morph_nc,42.718568,-84.47791571,Michigan State University,edu,8b266e68cc71f98ee42b04dc8f3e336c47f199cb,citation,https://arxiv.org/pdf/1711.10352.pdf,Learning Face Age Progression: A Pyramid Architecture of GANs,2017 -116,MORPH Non-Commercial,morph_nc,32.0565957,118.77408833,Nanjing University,edu,0e2d956790d3b8ab18cee8df6c949504ee78ad42,citation,https://doi.org/10.1109/IVCNZ.2013.6727024,Scalable face image retrieval integrating multi-feature quantization and constrained reference re-ranking,2013 -117,MORPH Non-Commercial,morph_nc,40.0044795,116.370238,Chinese Academy of Sciences,edu,2a7e6a1b2638550370a47f2f6f6e38e76fe9ac13,citation,http://doi.acm.org/10.1145/3090311,Multifeature Anisotropic Orthogonal Gaussian Process for Automatic Age Estimation,2017 -118,MORPH Non-Commercial,morph_nc,-33.88890695,151.18943366,University of Sydney,edu,2a7e6a1b2638550370a47f2f6f6e38e76fe9ac13,citation,http://doi.acm.org/10.1145/3090311,Multifeature Anisotropic Orthogonal Gaussian Process for Automatic Age Estimation,2017 -119,MORPH Non-Commercial,morph_nc,51.2975344,1.07296165,University of Kent,edu,2336de3a81dada63eb00ea82f7570c4069342fb5,citation,http://doi.acm.org/10.1145/2361407.2361428,A methodological framework for investigating age factors on the performance of biometric systems,2012 -120,MORPH Non-Commercial,morph_nc,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 -121,MORPH Non-Commercial,morph_nc,39.95472495,-75.15346905,Temple University,edu,019e471667c72b5b3728b4a9ba9fe301a7426fb2,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/2A_012.pdf,Cross-age face verification by coordinating with cross-face age verification,2015 -122,MORPH Non-Commercial,morph_nc,45.57022705,-122.63709346,Concordia University,edu,c418a3441f992fea523926f837f4bfb742548c16,citation,http://pdfs.semanticscholar.org/c418/a3441f992fea523926f837f4bfb742548c16.pdf,A Computer Approach for Face Aging Problems,2010 -123,MORPH Non-Commercial,morph_nc,22.42031295,114.20788644,Chinese University of Hong Kong,edu,d80a3d1f3a438e02a6685e66ee908446766fefa9,citation,https://arxiv.org/pdf/1708.09687.pdf,Quantifying Facial Age by Posterior of Age Comparisons,2017 -124,MORPH Non-Commercial,morph_nc,34.67567405,33.04577648,Cyprus University of Technology,edu,ebbceab4e15bf641f74e335b70c6c4490a043961,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4813349,Evaluating the performance of face-aging algorithms,2008 -125,MORPH Non-Commercial,morph_nc,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,d84a48f7d242d73b32a9286f9b148f5575acf227,citation,http://pdfs.semanticscholar.org/d84a/48f7d242d73b32a9286f9b148f5575acf227.pdf,Global and Local Consistent Age Generative Adversarial Networks,2018 -126,MORPH Non-Commercial,morph_nc,12.9551259,77.5741985,Bangalore Institute of Technology,edu,8f5facdc0a2a79283864aad03edc702e2a400346,citation,http://pdfs.semanticscholar.org/8f5f/acdc0a2a79283864aad03edc702e2a400346.pdf,Estimation Framework using Bio - Inspired Features for Facial Image,0 -127,MORPH Non-Commercial,morph_nc,42.718568,-84.47791571,Michigan State University,edu,08f6ad0a3e75b715852f825d12b6f28883f5ca05,citation,http://www.cse.msu.edu/biometrics/Publications/Face/JainKlarePark_FaceRecognition_ChallengesinForensics_FG11.pdf,Face recognition: Some challenges in forensics,2011 -128,MORPH Non-Commercial,morph_nc,41.10427915,29.02231159,Istanbul Technical University,edu,2050847bc7a1a0453891f03aeeb4643e360fde7d,citation,https://cvhci.anthropomatik.kit.edu/~mtapaswi/papers/ICMR2015.pdf,Accio: A Data Set for Face Track Retrieval in Movies Across Age,2015 -129,MORPH Non-Commercial,morph_nc,49.10184375,8.4331256,Karlsruhe Institute of Technology,edu,2050847bc7a1a0453891f03aeeb4643e360fde7d,citation,https://cvhci.anthropomatik.kit.edu/~mtapaswi/papers/ICMR2015.pdf,Accio: A Data Set for Face Track Retrieval in Movies Across Age,2015 -130,MORPH Non-Commercial,morph_nc,40.62984145,22.9588935,Aristotle University of Thessaloniki,edu,3cc46bf79fb9225cf308815c7d41c8dd5625cc29,citation,http://poseidon.csd.auth.gr/papers/PUBLISHED/CONFERENCE/pdf/2016/Pantraki2016.pdf,Age interval and gender prediction using PARAFAC2 applied to speech utterances,2016 -131,MORPH Non-Commercial,morph_nc,34.67567405,33.04577648,Cyprus University of Technology,edu,3cc46bf79fb9225cf308815c7d41c8dd5625cc29,citation,http://poseidon.csd.auth.gr/papers/PUBLISHED/CONFERENCE/pdf/2016/Pantraki2016.pdf,Age interval and gender prediction using PARAFAC2 applied to speech utterances,2016 -132,MORPH Non-Commercial,morph_nc,23.09461185,113.28788994,Sun Yat-Sen University,edu,189e5a2fa51ed471c0e7227d82dffb52736070d8,citation,https://doi.org/10.1109/ICIP.2017.8296995,Cross-age face recognition using reference coding with kernel direct discriminant analysis,2017 -133,MORPH Non-Commercial,morph_nc,42.357757,-83.06286711,Wayne State University,edu,4f1249369127cc2e2894f6b2f1052d399794919a,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8239663,Deep Age Estimation: From Classification to Ranking,2018 -134,MORPH Non-Commercial,morph_nc,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,cd6aaa37fffd0b5c2320f386be322b8adaa1cc68,citation,https://arxiv.org/pdf/1804.06655.pdf,Deep Face Recognition: A Survey,2018 -135,MORPH Non-Commercial,morph_nc,52.3553655,4.9501644,University of Amsterdam,edu,14014a1bdeb5d63563b68b52593e3ac1e3ce7312,citation,http://pdfs.semanticscholar.org/1401/4a1bdeb5d63563b68b52593e3ac1e3ce7312.pdf,Expression-Invariant Age Estimation,2014 -136,MORPH Non-Commercial,morph_nc,31.83907195,117.26420748,University of Science and Technology of China,edu,659dc6aa517645a118b79f0f0273e46ab7b53cd9,citation,https://doi.org/10.1109/ACPR.2015.7486608,Age-invariant face recognition using a feature progressing model,2015 -137,MORPH Non-Commercial,morph_nc,30.0818727,31.24454841,Benha University,edu,a9fc23d612e848250d5b675e064dba98f05ad0d9,citation,http://pdfs.semanticscholar.org/a9fc/23d612e848250d5b675e064dba98f05ad0d9.pdf,Face Age Estimation Approach based on Deep Learning and Principle Component Analysis,2018 -138,MORPH Non-Commercial,morph_nc,31.51368535,34.44019341,"Islamic University of Gaza, Palestine",edu,d5fa9d98c8da54a57abf353767a927d662b7f026,citation,http://pdfs.semanticscholar.org/f15e/9712b8731e1f5fd9566aca513edda910b5b8.pdf,Age Estimation based on Neural Networks using Face Features,2010 -139,MORPH Non-Commercial,morph_nc,32.0575279,118.78682252,Southeast University,edu,8ff8c64288a2f7e4e8bf8fda865820b04ab3dbe8,citation,https://pdfs.semanticscholar.org/0056/92b9fa6728df3a7f14578c43410867bba425.pdf,Age Estimation Using Expectation of Label Distribution Learning,2018 -140,MORPH Non-Commercial,morph_nc,32.0565957,118.77408833,Nanjing University,edu,8ff8c64288a2f7e4e8bf8fda865820b04ab3dbe8,citation,https://pdfs.semanticscholar.org/0056/92b9fa6728df3a7f14578c43410867bba425.pdf,Age Estimation Using Expectation of Label Distribution Learning,2018 -141,MORPH Non-Commercial,morph_nc,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 -142,MORPH Non-Commercial,morph_nc,42.718568,-84.47791571,Michigan State University,edu,e506cdb250eba5e70c5147eb477fbd069714765b,citation,https://pdfs.semanticscholar.org/e506/cdb250eba5e70c5147eb477fbd069714765b.pdf,Heterogeneous Face Recognition,2012 -143,MORPH Non-Commercial,morph_nc,35.90503535,-79.04775327,University of North Carolina,edu,f374ac9307be5f25145b44931f5a53b388a77e49,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5339060,Improvements in Active Appearance Model based synthetic age progression for adult aging,2009 -144,MORPH Non-Commercial,morph_nc,38.83133325,-77.30798839,George Mason University,edu,62750d78e819d745b9200b0c5c35fcae6fb9f404,citation,http://doi.org/10.1007/s11042-016-4085-8,Leveraging implicit demographic information for face recognition using a multi-expert system,2016 -145,MORPH Non-Commercial,morph_nc,41.9037626,12.5144384,Sapienza University of Rome,edu,62750d78e819d745b9200b0c5c35fcae6fb9f404,citation,http://doi.org/10.1007/s11042-016-4085-8,Leveraging implicit demographic information for face recognition using a multi-expert system,2016 -146,MORPH Non-Commercial,morph_nc,40.845492,14.2578058,University of Naples Federico II,edu,62750d78e819d745b9200b0c5c35fcae6fb9f404,citation,http://doi.org/10.1007/s11042-016-4085-8,Leveraging implicit demographic information for face recognition using a multi-expert system,2016 -147,MORPH Non-Commercial,morph_nc,25.01353105,121.54173736,National Taiwan University of Science and Technology,edu,e4c3587392d477b7594086c6f28a00a826abf004,citation,https://doi.org/10.1109/ICIP.2017.8296998,Face recognition by facial attribute assisted network,2017 -148,MORPH Non-Commercial,morph_nc,39.9922379,116.30393816,Peking University,edu,c4ca092972abb74ee1c20b7cae6e69c654479e2c,citation,https://doi.org/10.1109/ICIP.2016.7532960,Linear canonical correlation analysis based ranking approach for facial age estimation,2016 -149,MORPH Non-Commercial,morph_nc,40.0044795,116.370238,Chinese Academy of Sciences,edu,575141e42740564f64d9be8ab88d495192f5b3bc,citation,http://pdfs.semanticscholar.org/5751/41e42740564f64d9be8ab88d495192f5b3bc.pdf,Age Estimation Based on Multi-Region Convolutional Neural Network,2016 -150,MORPH Non-Commercial,morph_nc,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,575141e42740564f64d9be8ab88d495192f5b3bc,citation,http://pdfs.semanticscholar.org/5751/41e42740564f64d9be8ab88d495192f5b3bc.pdf,Age Estimation Based on Multi-Region Convolutional Neural Network,2016 -151,MORPH Non-Commercial,morph_nc,56.66340325,12.87929727,Halmstad University,edu,555f75077a02f33a05841f9b63a1388ec5fbcba5,citation,https://arxiv.org/pdf/1810.03360.pdf,A Survey on Periocular Biometrics Research,2016 -152,MORPH Non-Commercial,morph_nc,39.94976005,116.33629046,Beijing Jiaotong University,edu,0821028073981f9bd2dba2ad2557b25403fe7d7d,citation,http://doi.acm.org/10.1145/2733373.2806318,Facial Age Estimation Based on Structured Low-rank Representation,2015 -153,MORPH Non-Commercial,morph_nc,46.109237,7.08453549,IDIAP Research Institute,edu,939123cf21dc9189a03671484c734091b240183e,citation,http://publications.idiap.ch/downloads/papers/2015/Erdogmus_MMSP_2015.pdf,Within- and cross- database evaluations for face gender classification via befit protocols,2014 -154,MORPH Non-Commercial,morph_nc,36.689487,2.981877,"Center for Development of Advanced Technologies, Algeria",edu,4551194408383b12db19a22cca5db0f185cced5c,citation,https://doi.org/10.1109/TNNLS.2014.2341634,Nonlinear Topological Component Analysis: Application to Age-Invariant Face Recognition,2015 -155,MORPH Non-Commercial,morph_nc,56.45796755,-2.98214831,University of Dundee,edu,8b10383ef569ea0029a2c4a60cc2d8c87391b4db,citation,http://pdfs.semanticscholar.org/fe2d/20dca6dcedc7944cc2d9fea76de6cbb9d90c.pdf,Age classification using Radon transform and entropy based scaling SVM,2011 -156,MORPH Non-Commercial,morph_nc,40.0044795,116.370238,Chinese Academy of Sciences,edu,d37ca68742b2999667faf464f78d2fbf81e0cb07,citation,https://doi.org/10.1007/978-3-319-25417-3_76,DFDnet: Discriminant Face Descriptor Network for Facial Age Estimation,2015 -157,MORPH Non-Commercial,morph_nc,-35.2776999,149.118527,Australian National University,edu,a7191958e806fce2505a057196ccb01ea763b6ea,citation,http://pdfs.semanticscholar.org/a719/1958e806fce2505a057196ccb01ea763b6ea.pdf,Convolutional Neural Network based Age Estimation from Facial Image and Depth Prediction from Single Image,2016 -158,MORPH Non-Commercial,morph_nc,35.907757,127.766922,"Electronics and Telecommunications Research Institute, Korea",edu,abbc6dcbd032ff80e0535850f1bc27c4610b0d45,citation,https://doi.org/10.1109/ICIP.2015.7350983,Facial age estimation via extended curvature Gabor filter,2015 -159,MORPH Non-Commercial,morph_nc,36.3697191,127.362537,Korea Advanced Institute of Science and Technology,edu,abbc6dcbd032ff80e0535850f1bc27c4610b0d45,citation,https://doi.org/10.1109/ICIP.2015.7350983,Facial age estimation via extended curvature Gabor filter,2015 -160,MORPH Non-Commercial,morph_nc,1.2962018,103.77689944,National University of Singapore,edu,989332c5f1b22604d6bb1f78e606cb6b1f694e1a,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Wang_Recurrent_Face_Aging_CVPR_2016_paper.pdf,Recurrent Face Aging,2016 -161,MORPH Non-Commercial,morph_nc,32.0575279,118.78682252,Southeast University,edu,989332c5f1b22604d6bb1f78e606cb6b1f694e1a,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Wang_Recurrent_Face_Aging_CVPR_2016_paper.pdf,Recurrent Face Aging,2016 -162,MORPH Non-Commercial,morph_nc,46.0658836,11.1159894,University of Trento,edu,989332c5f1b22604d6bb1f78e606cb6b1f694e1a,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Wang_Recurrent_Face_Aging_CVPR_2016_paper.pdf,Recurrent Face Aging,2016 -163,MORPH Non-Commercial,morph_nc,40.62984145,22.9588935,Aristotle University of Thessaloniki,edu,1fd3dbb6e910708fa85c8a86e17ba0b6fef5617c,citation,http://pdfs.semanticscholar.org/1fd3/dbb6e910708fa85c8a86e17ba0b6fef5617c.pdf,Age interval and gender prediction using PARAFAC2 on speech recordings and face images,2016 -164,MORPH Non-Commercial,morph_nc,40.00229045,116.32098908,Tsinghua University,edu,6c6f0e806e4e286f3b18b934f42c72b67030ce17,citation,https://doi.org/10.1109/FG.2011.5771345,Combination of age and head pose for adult face verification,2011 -165,MORPH Non-Commercial,morph_nc,46.5190557,6.5667576,"Swiss Federal, Institute of Technology, Lausanne",edu,6c6f0e806e4e286f3b18b934f42c72b67030ce17,citation,https://doi.org/10.1109/FG.2011.5771345,Combination of age and head pose for adult face verification,2011 -166,MORPH Non-Commercial,morph_nc,52.6221571,1.2409136,University of East Anglia,edu,05a0d04693b2a51a8131d195c68ad9f5818b2ce1,citation,http://pdfs.semanticscholar.org/05a0/d04693b2a51a8131d195c68ad9f5818b2ce1.pdf,Dual-reference Face Retrieval: What Does He/She Look Like at Age 'X'?,2017 -167,MORPH Non-Commercial,morph_nc,40.44415295,-79.96243993,University of Pittsburgh,edu,05a0d04693b2a51a8131d195c68ad9f5818b2ce1,citation,http://pdfs.semanticscholar.org/05a0/d04693b2a51a8131d195c68ad9f5818b2ce1.pdf,Dual-reference Face Retrieval: What Does He/She Look Like at Age 'X'?,2017 -168,MORPH Non-Commercial,morph_nc,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,387b54cf6c186c12d83f95df6bd458c5eb1254ee,citation,https://doi.org/10.1109/VCIP.2017.8305123,Deep probabilities for age estimation,2017 -169,MORPH Non-Commercial,morph_nc,35.97320905,-78.89755054,North Carolina Central University,edu,1ca1b4f787712ede215030d22a0eea41534a601e,citation,https://doi.org/10.1109/CVPRW.2010.5543609,Human age estimation: What is the influence across race and gender?,2010 -170,MORPH Non-Commercial,morph_nc,39.65404635,-79.96475355,West Virginia University,edu,1ca1b4f787712ede215030d22a0eea41534a601e,citation,https://doi.org/10.1109/CVPRW.2010.5543609,Human age estimation: What is the influence across race and gender?,2010 -171,MORPH Non-Commercial,morph_nc,1.3484104,103.68297965,Nanyang Technological University,edu,b6a23f72007cb40223d7e1e1cc47e466716de945,citation,https://doi.org/10.1109/CVPRW.2010.5544598,Ordinary preserving manifold analysis for human age estimation,2010 -172,MORPH Non-Commercial,morph_nc,60.7897318,10.6821927,"Norwegian Biometrics Lab, NTNU, 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Singapore,edu,8cffe360a05085d4bcba111a3a3cd113d96c0369,citation,http://doi.ieeecomputersociety.org/10.1109/ICCV.2011.6126248,Learning universal multi-view age estimator using video context,2011 -180,MORPH Non-Commercial,morph_nc,23.143197,113.34009651,South China Normal University,edu,dc6ad30c7a4bc79bb06b4725b16e202d3d7d8935,citation,http://doi.org/10.1007/s11042-017-4646-5,Age classification with deep learning face representation,2017 -181,MORPH Non-Commercial,morph_nc,23.0502042,113.39880323,South China University of Technology,edu,dc6ad30c7a4bc79bb06b4725b16e202d3d7d8935,citation,http://doi.org/10.1007/s11042-017-4646-5,Age classification with deep learning face representation,2017 -182,MORPH Non-Commercial,morph_nc,50.0764296,14.41802312,Czech Technical University,edu,023ed32ac3ea6029f09b8c582efbe3866de7d00a,citation,http://pdfs.semanticscholar.org/023e/d32ac3ea6029f09b8c582efbe3866de7d00a.pdf,Discriminative learning from partially annotated examples,2016 -183,MORPH Non-Commercial,morph_nc,35.5167538,139.48342251,Tokyo Institute of Technology,edu,435dc062d565ce87c6c20a5f49430eb9a4b573c4,citation,http://pdfs.semanticscholar.org/435d/c062d565ce87c6c20a5f49430eb9a4b573c4.pdf,Lighting Condition Adaptation for Perceived Age Estimation,2011 -184,MORPH Non-Commercial,morph_nc,42.718568,-84.47791571,Michigan State University,edu,6a5d7d20a8c4993d56bcf702c772aa3f95f99450,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4813408,Face recognition with temporal invariance: A 3D aging model,2008 -185,MORPH Non-Commercial,morph_nc,35.97320905,-78.89755054,North Carolina Central University,edu,2a6783ae51d7ee781d584ef9a3eb8ab1997d0489,citation,https://doi.org/10.1109/CVPRW.2010.5543608,A study of large-scale ethnicity estimation with gender and age variations,2010 -186,MORPH Non-Commercial,morph_nc,39.65404635,-79.96475355,West Virginia University,edu,2a6783ae51d7ee781d584ef9a3eb8ab1997d0489,citation,https://doi.org/10.1109/CVPRW.2010.5543608,A 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University,edu,e22adcd2a6a7544f017ec875ce8f89d5c59e09c8,citation,https://arxiv.org/pdf/1807.11936.pdf,Gender Privacy: An Ensemble of Semi Adversarial Networks for Confounding Arbitrary Gender Classifiers,2018 -191,MORPH Non-Commercial,morph_nc,25.01682835,121.53846924,National Taiwan University,edu,6ab33fa51467595f18a7a22f1d356323876f8262,citation,http://www.iis.sinica.edu.tw/~kuangyu/OHRank_files/0523.pdf,Ordinal hyperplanes ranker with cost sensitivities for age estimation,2011 -192,MORPH Non-Commercial,morph_nc,25.0410728,121.6147562,Institute of Information Science,edu,6ab33fa51467595f18a7a22f1d356323876f8262,citation,http://www.iis.sinica.edu.tw/~kuangyu/OHRank_files/0523.pdf,Ordinal hyperplanes ranker with cost sensitivities for age estimation,2011 -193,MORPH Non-Commercial,morph_nc,25.0411727,121.6146518,"Academia Sinica, Taiwan",edu,6ab33fa51467595f18a7a22f1d356323876f8262,citation,http://www.iis.sinica.edu.tw/~kuangyu/OHRank_files/0523.pdf,Ordinal hyperplanes ranker with cost 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University,edu,fcb97ede372c5bddde7a61924ac2fd29788c82ce,citation,https://doi.org/10.1109/TSMCC.2012.2192727,Ordinary Preserving Manifold Analysis for Human Age and Head Pose Estimation,2013 -198,MORPH Non-Commercial,morph_nc,36.3697191,127.362537,Korea Advanced Institute of Science and Technology,edu,cb27b45329d61f5f95ed213798d4b2a615e76be2,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8329236,Deep Facial Age Estimation Using Conditional Multitask Learning With Weak Label Expansion,2018 -199,MORPH Non-Commercial,morph_nc,37.2520226,127.0555019,"Samsung SAIT, Korea",company,cb27b45329d61f5f95ed213798d4b2a615e76be2,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8329236,Deep Facial Age Estimation Using Conditional Multitask Learning With Weak Label Expansion,2018 -200,MORPH Non-Commercial,morph_nc,35.14479945,33.90492318,Eastern Mediterranean University,edu,c5421a18583f629b49ca20577022f201692c4f5d,citation,http://pdfs.semanticscholar.org/c542/1a18583f629b49ca20577022f201692c4f5d.pdf,Facial Age Classification using Subpattern-based Approaches,2011 -201,MORPH Non-Commercial,morph_nc,40.0044795,116.370238,Chinese Academy of Sciences,edu,68c4a1d438ea1c6dfba92e3aee08d48f8e7f7090,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/papers/Liu_AgeNet_Deeply_Learned_ICCV_2015_paper.pdf,AgeNet: Deeply Learned Regressor and Classifier for Robust Apparent Age Estimation,2015 -202,MORPH Non-Commercial,morph_nc,31.32235655,121.38400941,Shanghai University,edu,5f0d4a0b5f72d8700cdf8cb179263a8fa866b59b,citation,https://pdfs.semanticscholar.org/5f0d/4a0b5f72d8700cdf8cb179263a8fa866b59b.pdf,Memo No . 85 06 / 2018 Deep Regression Forests for Age Estimation,2018 -203,MORPH Non-Commercial,morph_nc,24.96841805,121.19139696,National Central University,edu,c58ece1a3fa23608f022e424ec5a93cddda31308,citation,https://doi.org/10.1109/JSYST.2014.2325957,Extraction of Visual Facial Features for Health Management,2016 -204,MORPH Non-Commercial,morph_nc,50.0764296,14.41802312,Czech Technical University,edu,56e25358ebfaf8a8b3c7c33ed007e24f026065d0,citation,https://doi.org/10.1007/s10994-015-5541-9,V-shaped interval insensitive loss for ordinal classification,2015 -205,MORPH Non-Commercial,morph_nc,5.7648848,102.6281702,"University Sultan Zainal Abidin, Malaysia",edu,3337cfc3de2c16dee6f7cbeda5f263409a9ad81e,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8398675,Age prediction on face features via multiple classifiers,2018 -206,MORPH Non-Commercial,morph_nc,1.2962018,103.77689944,National University of Singapore,edu,2836d68c86f29bb87537ea6066d508fde838ad71,citation,http://arxiv.org/pdf/1510.06503v1.pdf,Personalized Age Progression with Aging Dictionary,2015 -207,MORPH Non-Commercial,morph_nc,32.0565957,118.77408833,Nanjing University,edu,2836d68c86f29bb87537ea6066d508fde838ad71,citation,http://arxiv.org/pdf/1510.06503v1.pdf,Personalized Age Progression with Aging Dictionary,2015 -208,MORPH Non-Commercial,morph_nc,22.42031295,114.20788644,Chinese University of Hong Kong,edu,55966926e7c28b1eee1c7eb7a0b11b10605a1af0,citation,http://pdfs.semanticscholar.org/baa8/bdeb5aa545af5b5f43efaf9dda08490da0bc.pdf,Surpassing Human-Level Face Verification Performance on LFW with GaussianFace,2015 -209,MORPH Non-Commercial,morph_nc,40.0044795,116.370238,Chinese Academy of Sciences,edu,d492dbfaa42b4f8b8a74786d7343b3be6a3e9a1d,citation,https://pdfs.semanticscholar.org/d492/dbfaa42b4f8b8a74786d7343b3be6a3e9a1d.pdf,Deep Cost-Sensitive and Order-Preserving Feature Learning for Cross-Population Age Estimation,0 -210,MORPH Non-Commercial,morph_nc,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,d492dbfaa42b4f8b8a74786d7343b3be6a3e9a1d,citation,https://pdfs.semanticscholar.org/d492/dbfaa42b4f8b8a74786d7343b3be6a3e9a1d.pdf,Deep Cost-Sensitive and Order-Preserving Feature Learning for Cross-Population Age Estimation,0 -211,MORPH Non-Commercial,morph_nc,34.67567405,33.04577648,Cyprus University of Technology,edu,fa518a033b1f6299d1826389bd1520cf52291b56,citation,https://pdfs.semanticscholar.org/fa51/8a033b1f6299d1826389bd1520cf52291b56.pdf,Facial Age Simulation using Age-specific 3D Models and Recursive PCA,2013 -212,MORPH Non-Commercial,morph_nc,38.83133325,-77.30798839,George Mason University,edu,1c147261f5ab1b8ee0a54021a3168fa191096df8,citation,http://pdfs.semanticscholar.org/1c14/7261f5ab1b8ee0a54021a3168fa191096df8.pdf,Face Recognition across Time Lapse Using Convolutional Neural Networks,2016 -213,MORPH Non-Commercial,morph_nc,32.05765485,118.7550004,HoHai University,edu,b84b7b035c574727e4c30889e973423fe15560d7,citation,http://pdfs.semanticscholar.org/b84b/7b035c574727e4c30889e973423fe15560d7.pdf,Human Age Estimation Using Ranking SVM,2012 -214,MORPH Non-Commercial,morph_nc,40.0044795,116.370238,Chinese Academy of Sciences,edu,b84b7b035c574727e4c30889e973423fe15560d7,citation,http://pdfs.semanticscholar.org/b84b/7b035c574727e4c30889e973423fe15560d7.pdf,Human Age Estimation Using Ranking SVM,2012 -215,MORPH Non-Commercial,morph_nc,39.6810328,-75.7540184,University of Delaware,edu,19da9f3532c2e525bf92668198b8afec14f9efea,citation,http://pdfs.semanticscholar.org/19da/9f3532c2e525bf92668198b8afec14f9efea.pdf,Challenge: Face verification across age progression using real-world data,2011 -216,MORPH Non-Commercial,morph_nc,39.95472495,-75.15346905,Temple University,edu,f24e379e942e134d41c4acec444ecf02b9d0d3a9,citation,http://pdfs.semanticscholar.org/f24e/379e942e134d41c4acec444ecf02b9d0d3a9.pdf,Analysis of Facial Images across Age Progression 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London",edu,0e5557a0cc58194ad53fab5dd6f4d4195d19ce4e,citation,https://doi.org/10.1109/TMM.2015.2500730,Deep Aging Face Verification With Large Gaps,2016 -225,MORPH Non-Commercial,morph_nc,31.846918,117.29053367,Hefei University of Technology,edu,0e5557a0cc58194ad53fab5dd6f4d4195d19ce4e,citation,https://doi.org/10.1109/TMM.2015.2500730,Deep Aging Face Verification With Large Gaps,2016 -226,MORPH Non-Commercial,morph_nc,29.58333105,-98.61944505,University of Texas at San Antonio,edu,f2896dd2701fbb3564492a12c64f11a5ad456a67,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5495414,Cross-database age estimation based on transfer learning,2010 -227,MORPH Non-Commercial,morph_nc,34.1235825,108.83546,Xidian University,edu,f2896dd2701fbb3564492a12c64f11a5ad456a67,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5495414,Cross-database age estimation based on transfer learning,2010 -228,MORPH Non-Commercial,morph_nc,56.66340325,12.87929727,Halmstad University,edu,9cda3e56cec21bd8f91f7acfcefc04ac10973966,citation,https://doi.org/10.1109/IWBF.2016.7449688,"Periocular biometrics: databases, algorithms and directions",2016 -229,MORPH Non-Commercial,morph_nc,34.2375581,-77.9270129,University of North Carolina Wilmington,edu,13aef395f426ca8bd93640c9c3f848398b189874,citation,https://pdfs.semanticscholar.org/13ae/f395f426ca8bd93640c9c3f848398b189874.pdf,1 Image Preprocessing and Complete 2 DPCA with Feature Extraction for Gender Recognition NSF REU 2017 : Statistical Learning and Data Mining,2017 -230,MORPH Non-Commercial,morph_nc,24.7925484,120.9951183,National Tsing Hua University,edu,cfa40560fa74b2fb5c26bdd6ea7c610ba5130e2f,citation,https://doi.org/10.1109/TIFS.2013.2286265,Subspace Learning for Facial Age Estimation Via Pairwise Age Ranking,2013 -231,MORPH Non-Commercial,morph_nc,58.38131405,26.72078081,University of Tartu,edu,1b248ed8e7c9514648cd598960fadf9ab17e7fe8,citation,https://pdfs.semanticscholar.org/1b24/8ed8e7c9514648cd598960fadf9ab17e7fe8.pdf,"From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation",0 -232,MORPH Non-Commercial,morph_nc,41.3868913,2.16352385,University of Barcelona,edu,1b248ed8e7c9514648cd598960fadf9ab17e7fe8,citation,https://pdfs.semanticscholar.org/1b24/8ed8e7c9514648cd598960fadf9ab17e7fe8.pdf,"From apparent to real age: gender, age, ethnic, makeup, and expression bias analysis in real age estimation",0 -233,MORPH Non-Commercial,morph_nc,39.65404635,-79.96475355,West Virginia University,edu,86a8b3d0f753cb49ac3250fa14d277983e30a4b7,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2013.75,Exploiting Unlabeled Ages for Aging Pattern Analysis on a Large Database,2013 -234,MORPH Non-Commercial,morph_nc,34.2239869,-77.8701325,"UNCW, USA",edu,2b5cb5466eecb131f06a8100dcaf0c7a0e30d391,citation,http://doi.acm.org/10.1145/1924559.1924608,A comparative study of active appearance model annotation schemes for the face,2010 -235,MORPH Non-Commercial,morph_nc,42.718568,-84.47791571,Michigan State University,edu,fc798314994bf94d1cde8d615ba4d5e61b6268b6,citation,http://pdfs.semanticscholar.org/fc79/8314994bf94d1cde8d615ba4d5e61b6268b6.pdf,"Face Recognition : face in video , age invariance , and facial marks",2009 -236,MORPH Non-Commercial,morph_nc,24.12084345,120.67571165,National Chung Hsing University,edu,635d2696aa597a278dd6563f079be06aa76a33c0,citation,https://doi.org/10.1109/ICIP.2016.7532429,Age estimation via fusion of multiple binary age grouping systems,2016 -237,MORPH Non-Commercial,morph_nc,25.01682835,121.53846924,National Taiwan University,edu,635d2696aa597a278dd6563f079be06aa76a33c0,citation,https://doi.org/10.1109/ICIP.2016.7532429,Age estimation via fusion of multiple binary age grouping systems,2016 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University,edu,7a65fc9e78eff3ab6062707deaadde024d2fad40,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/papers/Zhu_A_Study_on_ICCV_2015_paper.pdf,A Study on Apparent Age Estimation,2015 -246,MORPH Non-Commercial,morph_nc,42.357757,-83.06286711,Wayne State University,edu,28d99dc2d673d62118658f8375b414e5192eac6f,citation,http://www.cs.wayne.edu/~mdong/cvpr17.pdf,Using Ranking-CNN for Age Estimation,2017 -247,MORPH Non-Commercial,morph_nc,37.4102193,-122.05965487,Carnegie Mellon University,edu,ec05078be14a11157ac0e1c6b430ac886124589b,citation,http://pdfs.semanticscholar.org/ec05/078be14a11157ac0e1c6b430ac886124589b.pdf,Longitudinal Face Aging in the Wild - Recent Deep Learning Approaches,2018 -248,MORPH Non-Commercial,morph_nc,45.57022705,-122.63709346,Concordia University,edu,ec05078be14a11157ac0e1c6b430ac886124589b,citation,http://pdfs.semanticscholar.org/ec05/078be14a11157ac0e1c6b430ac886124589b.pdf,Longitudinal Face Aging in the Wild - Recent Deep Learning Approaches,2018 -249,MORPH Non-Commercial,morph_nc,46.5190557,6.5667576,"Swiss Federal Institute of Technology Lausanne, Switzerland",edu,d7a84db2a1bf7b97657b0250f354f249394dd700,citation,https://doi.org/10.1109/ICIP.2010.5653518,Global and local feature based multi-classifier A-stack model for aging face identification,2010 -250,MORPH Non-Commercial,morph_nc,39.65404635,-79.96475355,West Virginia University,edu,d3c004125c71942846a9b32ae565c5216c068d1e,citation,http://pdfs.semanticscholar.org/d3c0/04125c71942846a9b32ae565c5216c068d1e.pdf,Recognizing Age-Separated Face Images: Humans and Machines,2014 -251,MORPH Non-Commercial,morph_nc,52.3553655,4.9501644,University of Amsterdam,edu,999289b0ef76c4c6daa16a4f42df056bf3d68377,citation,http://pdfs.semanticscholar.org/9992/89b0ef76c4c6daa16a4f42df056bf3d68377.pdf,The Role of Color and Contrast in Facial Age Estimation,2014 -252,MORPH Non-Commercial,morph_nc,51.99882735,4.37396037,Delft University of Technology,edu,999289b0ef76c4c6daa16a4f42df056bf3d68377,citation,http://pdfs.semanticscholar.org/9992/89b0ef76c4c6daa16a4f42df056bf3d68377.pdf,The Role of Color and Contrast in Facial Age Estimation,2014 -253,MORPH Non-Commercial,morph_nc,28.5456282,77.2731505,"IIIT Delhi, India",edu,f726738954e7055bb3615fa7e8f59f136d3e0bdc,citation,https://arxiv.org/pdf/1803.07385.pdf,Are you eligible? Predicting adulthood from face images via class specific mean autoencoder,2018 -254,MORPH Non-Commercial,morph_nc,1.2962018,103.77689944,National University of Singapore,edu,b9d68dbeb8e5fdc5984b49a317ea6798b378e5ae,citation,http://doi.acm.org/10.1145/2733373.2807962,What Shall I Look Like after N Years?,2015 -255,MORPH Non-Commercial,morph_nc,32.0565957,118.77408833,Nanjing University,edu,b9d68dbeb8e5fdc5984b49a317ea6798b378e5ae,citation,http://doi.acm.org/10.1145/2733373.2807962,What Shall I Look Like after N Years?,2015 -256,MORPH Non-Commercial,morph_nc,45.42580475,-75.68740118,University of Ottawa,edu,16820ccfb626dcdc893cc7735784aed9f63cbb70,citation,http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W12/papers/Azarmehr_Real-Time_Embedded_Age_2015_CVPR_paper.pdf,Real-time embedded age and gender classification in unconstrained video,2015 -257,MORPH Non-Commercial,morph_nc,35.0274996,135.78154513,University of Caen,edu,0ad8149318912b5449085187eb3521786a37bc78,citation,http://arxiv.org/abs/1604.02975,CP-mtML: Coupled Projection Multi-Task Metric Learning for Large Scale Face Retrieval,2016 -258,MORPH Non-Commercial,morph_nc,51.44415765,7.26096541,Ruhr-University Bochum,edu,7e1ea2679a110241ed0dd38ff45cd4dfeb7a8e83,citation,http://pdfs.semanticscholar.org/7e1e/a2679a110241ed0dd38ff45cd4dfeb7a8e83.pdf,Extensions of Hierarchical Slow Feature Analysis for Efficient Classification and Regression on High-Dimensional Data,2017 -259,MORPH Non-Commercial,morph_nc,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,2e27667421a7eeab278e0b761db4d2c725683c3f,citation,https://doi.org/10.1007/s11042-013-1815-z,Effective human age estimation using a two-stage approach based on Lie Algebrized Gaussians feature,2013 -260,MORPH Non-Commercial,morph_nc,32.0565957,118.77408833,Nanjing University,edu,0c741fa0966ba3ee4fc326e919bf2f9456d0cd74,citation,http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.51,Facial Age Estimation by Learning from Label Distributions,2010 -261,MORPH Non-Commercial,morph_nc,32.0575279,118.78682252,Southeast University,edu,0c741fa0966ba3ee4fc326e919bf2f9456d0cd74,citation,http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.51,Facial Age Estimation by Learning from Label Distributions,2010 -262,MORPH Non-Commercial,morph_nc,-37.78397455,144.95867433,Monash University,edu,0c741fa0966ba3ee4fc326e919bf2f9456d0cd74,citation,http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.51,Facial Age Estimation by Learning from Label Distributions,2010 -263,MORPH Non-Commercial,morph_nc,1.2962018,103.77689944,National University of Singapore,edu,fca9ebaa30d69ccec8bb577c31693c936c869e72,citation,https://arxiv.org/pdf/1809.00338.pdf,Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face 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Technology,edu,70db3a0d2ca8a797153cc68506b8650908cb0ada,citation,http://pdfs.semanticscholar.org/70db/3a0d2ca8a797153cc68506b8650908cb0ada.pdf,An Overview of Research Activities in Facial Age Estimation Using the FG-NET Aging Database,2014 -268,MORPH Non-Commercial,morph_nc,22.5447154,113.9357164,Tencent,company,a2d1818eb461564a5153c74028e53856cf0b40fd,citation,https://arxiv.org/pdf/1810.07599.pdf,Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition,2018 -269,MORPH Non-Commercial,morph_nc,57.6252103,39.8845656,Yaroslavl State University,edu,05318a267226f6d855d83e9338eaa9e718b2a8dd,citation,https://fruct.org/publications/fruct16/files/Khr.pdf,Age estimation from face images: challenging problem for audience measurement systems,2014 -270,MORPH Non-Commercial,morph_nc,41.5381124,2.4447406,"EUP Mataró, Spain",edu,1f5725a4a2eb6cdaefccbc20dccadf893936df12,citation,https://doi.org/10.1109/CCST.2012.6393544,On the relevance of age in handwritten biometric recognition,2012 -271,MORPH Non-Commercial,morph_nc,34.67567405,33.04577648,Cyprus University of Technology,edu,876583a059154def7a4bc503b21542f80859affd,citation,https://doi.org/10.1109/IWBF.2016.7449697,On the analysis of factors influencing the performance of facial age progression,2016 -272,MORPH Non-Commercial,morph_nc,-35.0636071,147.3552234,Charles Sturt University,edu,2e231f1e7e641dd3619bec59e14d02e91360ac01,citation,https://arxiv.org/pdf/1807.10421.pdf,Fusion Network for Face-Based Age Estimation,2018 -273,MORPH Non-Commercial,morph_nc,51.3791442,-2.3252332,University of Bath,edu,2e231f1e7e641dd3619bec59e14d02e91360ac01,citation,https://arxiv.org/pdf/1807.10421.pdf,Fusion Network for Face-Based Age Estimation,2018 -274,MORPH Non-Commercial,morph_nc,40.0044795,116.370238,Chinese Academy of Sciences,edu,56359d2b4508cc267d185c1d6d310a1c4c2cc8c2,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2015.7298618,Shape driven kernel adaptation in Convolutional Neural Network for robust facial trait recognition,2015 -275,MORPH Non-Commercial,morph_nc,39.9041999,116.4073963,Chinese Academy of Science,edu,56359d2b4508cc267d185c1d6d310a1c4c2cc8c2,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2015.7298618,Shape driven kernel adaptation in Convolutional Neural Network for robust facial trait recognition,2015 -276,MORPH Non-Commercial,morph_nc,1.2962018,103.77689944,National University of Singapore,edu,56359d2b4508cc267d185c1d6d310a1c4c2cc8c2,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2015.7298618,Shape driven kernel adaptation in Convolutional Neural Network for robust facial trait recognition,2015 -277,MORPH Non-Commercial,morph_nc,32.0565957,118.77408833,Nanjing University,edu,a6e43b73f9f87588783988333997a81b4487e2d5,citation,http://pdfs.semanticscholar.org/a6e4/3b73f9f87588783988333997a81b4487e2d5.pdf,Facial Age Estimation by Total Ordering Preserving Projection,2016 -278,MORPH Non-Commercial,morph_nc,1.2988926,103.7873107,"Institution for Infocomm Research, Singapore",edu,8229f2735a0db0ad41f4d7252129311f06959907,citation,https://doi.org/10.1109/TIP.2011.2106794,Active Learning for Solving the Incomplete Data Problem in Facial Age Classification by the Furthest Nearest-Neighbor Criterion,2011 -279,MORPH Non-Commercial,morph_nc,1.3484104,103.68297965,Nanyang Technological University,edu,8229f2735a0db0ad41f4d7252129311f06959907,citation,https://doi.org/10.1109/TIP.2011.2106794,Active Learning for Solving the Incomplete Data Problem in Facial Age Classification by the Furthest Nearest-Neighbor Criterion,2011 -280,MORPH Non-Commercial,morph_nc,39.2899685,-76.62196103,University of Maryland,edu,963a004e208ce4bd26fa79a570af61d31651b3c3,citation,https://doi.org/10.1016/j.jvlc.2009.01.011,Computational methods for modeling facial aging: A survey,2009 -281,MORPH Non-Commercial,morph_nc,40.48256135,-3.6906079,Universidad Autonoma de Madrid,edu,4b5ff8c67f3496a414f94e35cb35a601ec98e5cf,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6547306,Understanding the discrimination power of facial regions in forensic casework,2013 -282,MORPH Non-Commercial,morph_nc,40.4445565,-3.7122785,"Dirección General de la Guardia Civil, Madrid, Spain",edu,4b5ff8c67f3496a414f94e35cb35a601ec98e5cf,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6547306,Understanding the discrimination power of facial regions in forensic casework,2013 -283,MORPH Non-Commercial,morph_nc,-37.8087465,144.9638875,RMIT University,edu,c49075ead6eb07ede5ada4fe372899bd0cfb83ac,citation,https://doi.org/10.1109/ICSPCS.2015.7391782,Multi-stage classification network for automatic age estimation from facial images,2015 -284,MORPH Non-Commercial,morph_nc,34.2375581,-77.9270129,University of North Carolina Wilmington,edu,00301c250d667700276b1e573640ff2fd7be574d,citation,https://doi.org/10.1109/BTAS.2014.6996242,Establishing a test set and initial comparisons for quantitatively evaluating synthetic age progression for adult aging,2014 diff --git a/site/datasets/final/morph_nc.json b/site/datasets/final/morph_nc.json index 8306b300..d072e8c7 100644 --- a/site/datasets/final/morph_nc.json +++ b/site/datasets/final/morph_nc.json @@ -1 +1 @@ -{"id": "9055b155cbabdce3b98e16e5ac9c0edf00f9552f", "dataset": {"key": "morph_nc", "name_short": "MORPH Non-Commercial", "using": "N", "ft_share": "1", "subset_of": "", "superset_of": "", "name_full": "Craniofacial Longitudinal Morphological Face Dataset", "url": "https://ebill.uncw.edu/C20231_ustores/web/classic/store_main.jsp?STOREID=4", "added_on": "", "faces": "", "pdf_paper": "Y", "comments": "the 2 MORPH datasets are interesting as they might include mugshots and pose legal questions. Maybe even some mugshots from as far back as 1967", "": "", "relevance": ""}, "statistics": {"key": "morph_nc", "name": "MORPH Non-Commercial", "berit": "Y", "charlie": "", "adam": "", "priority": "N", "wild": "M", "indoor": "", "outdoor": "", "cyberspace": "", "names": "", "downloaded": "N", "year_start": "2003", "year_end": "2007", "year_published": "2007", "ongoing": "", "images": "55,134 ", "videos": "", "faces_unique": "13,618 ", "total_faces": "", "img_per_person": "", "num_cameras": "", "faces_persons": "", "female": "", "male": "", "landmarks": "", "width": "", "height": "", "color": "", "gray": "", "derivative_of": "", "tags": "fr, age", "source": "mugshot", "purpose_short": "mugshots taken from same subject over a span of 5 years", "size_gb": "", "agreement": "", "agree_requied": "", "agreement_signed": "", "comment": "", "comment 2": "", "comment 3": "", "": ""}, "paper": {"paper_id": "9055b155cbabdce3b98e16e5ac9c0edf00f9552f", "key": "morph_nc", "title": "MORPH: a longitudinal image database of normal adult age-progression", "year": "2006", "pdf": [], "address": "", "name": "MORPH Non-Commercial", "doi": ["http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1613043", "http://doi.ieeecomputersociety.org/10.1109/FGR.2006.78", "http://doi.org/10.1109/FGR.2006.78"]}, "address": null, "additional_papers": [], "citations": [{"id": "dad6b36fd515bda801f3d22a462cc62348f6aad8", "title": "Gait-based age estimation using a whole-generation gait database", "addresses": [{"address": "Osaka University", "lat": "34.80809035", "lng": "135.45785218", "type": "edu"}], "year": "2011", "pdf": ["http://www.am.sanken.osaka-u.ac.jp/~makihara/pdf/ijcb2011_gait_age_estimation.pdf"]}, {"id": "ddd0f1c53f76d7fc20e11b7e33bbdc0437516d2b", "title": "Deep learning-based learning to rank with ties for image re-ranking", "addresses": [{"address": "Chinese Academy of Sciences", "lat": "40.00447950", "lng": "116.37023800", "type": "edu"}, {"address": "Civil Aviation University 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-31,VOC,voc,49.25839375,-123.24658161,University of British Columbia,edu,9fae24003bbedecdb617f9779215d79d06b90dd8,citation,https://arxiv.org/pdf/1807.09856.pdf,Where Are the Blobs: Counting by Localization with Point Supervision,2018 -32,VOC,voc,40.72925325,-73.99625394,New York University,edu,c45681fa9d9c36a6a196017ef283ac38904f91bb,citation,https://arxiv.org/pdf/1711.07377.pdf,Pixel-wise object tracking,2017 -33,VOC,voc,37.4102193,-122.05965487,Carnegie Mellon University,edu,45f858f9e8d7713f60f52618e54089ba68dfcd6d,citation,http://openaccess.thecvf.com/content_ICCV_2017/papers/Sigurdsson_What_Actions_Are_ICCV_2017_paper.pdf,What Actions are Needed for Understanding Human Actions in Videos?,2017 -34,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,57bd01c042a5f64659b3a9f91c048b8594f762f6,citation,http://pdfs.semanticscholar.org/57bd/01c042a5f64659b3a9f91c048b8594f762f6.pdf,Advances in fine-grained visual categorization,2015 -35,VOC,voc,31.30104395,121.50045497,Fudan University,edu,9716416a15e79a36e3481bcdad79cdc905603e6d,citation,https://arxiv.org/pdf/1808.07016.pdf,Gaussian Word Embedding with a Wasserstein Distance Loss,2017 -36,VOC,voc,32.0565957,118.77408833,Nanjing University,edu,97265d64859e06900c11ae5bb5f03f3bd265f858,citation,https://arxiv.org/pdf/1612.01082.pdf,Multilabel Image Classification With Regional Latent Semantic Dependencies,2018 -37,VOC,voc,-34.9189226,138.60423668,University of Adelaide,edu,97265d64859e06900c11ae5bb5f03f3bd265f858,citation,https://arxiv.org/pdf/1612.01082.pdf,Multilabel Image Classification With Regional Latent Semantic Dependencies,2018 -38,VOC,voc,-33.8809651,151.20107299,University of Technology Sydney,edu,97265d64859e06900c11ae5bb5f03f3bd265f858,citation,https://arxiv.org/pdf/1612.01082.pdf,Multilabel Image Classification With Regional Latent Semantic Dependencies,2018 -39,VOC,voc,42.3583961,-71.09567788,MIT,edu,a19904e76b5ded44e6aeb9af85997d160de6bb22,citation,http://pdfs.semanticscholar.org/a199/04e76b5ded44e6aeb9af85997d160de6bb22.pdf,TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation,2018 -40,VOC,voc,47.05821,15.46019568,Graz University of Technology,edu,96a9ca7a8366ae0efe6b58a515d15b44776faf6e,citation,https://arxiv.org/pdf/1609.00129.pdf,Grid Loss: Detecting Occluded Faces,2016 -41,VOC,voc,47.05821,15.46019568,Graz University of Technology,edu,513b8dc73a9fbc467e1ac130fe8c842b5839ca51,citation,http://pdfs.semanticscholar.org/513b/8dc73a9fbc467e1ac130fe8c842b5839ca51.pdf,Dissertation Scalable Visual Navigation for Micro Aerial Vehicles using Geometric Prior Knowledge,2013 -42,VOC,voc,37.8687126,-122.25586815,"University of California, Berkeley",edu,0ee3aa2a78f9680bb65a823bd9195c879572ec1c,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Dubey_What_Makes_an_ICCV_2015_paper.pdf,What Makes an Object Memorable?,2015 -43,VOC,voc,42.3583961,-71.09567788,MIT,edu,0ee3aa2a78f9680bb65a823bd9195c879572ec1c,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Dubey_What_Makes_an_ICCV_2015_paper.pdf,What Makes an Object Memorable?,2015 -44,VOC,voc,37.36566745,-120.42158888,"University of California, Merced",edu,0ee3aa2a78f9680bb65a823bd9195c879572ec1c,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Dubey_What_Makes_an_ICCV_2015_paper.pdf,What Makes an Object Memorable?,2015 -45,VOC,voc,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,a776acc53591c3eb0b53501d9758d984e2e52a97,citation,https://arxiv.org/pdf/1804.00880.pdf,Weakly Supervised Instance Segmentation using Class Peak Response,2018 -46,VOC,voc,35.9990522,-78.9290629,Duke University,edu,a776acc53591c3eb0b53501d9758d984e2e52a97,citation,https://arxiv.org/pdf/1804.00880.pdf,Weakly Supervised Instance Segmentation using Class Peak Response,2018 -47,VOC,voc,1.2962018,103.77689944,National University of Singapore,edu,423b941641728a21e37f41359a691815cdd84ceb,citation,http://arxiv.org/abs/1511.04517,Reversible Recursive Instance-Level Object Segmentation,2016 -48,VOC,voc,47.6423318,-122.1369302,Microsoft,company,666939690c564641b864eed0d60a410b31e49f80,citation,http://pdfs.semanticscholar.org/6669/39690c564641b864eed0d60a410b31e49f80.pdf,What Visual Attributes Characterize an Object Class?,2014 -49,VOC,voc,43.7776426,11.259765,University of Florence,edu,51e8e8c4cac8260ef21c25f9f2a0a68aedbc6d58,citation,https://arxiv.org/pdf/1704.02518.pdf,Deep Generative Adversarial Compression Artifact Removal,2017 -50,VOC,voc,-34.9189226,138.60423668,University of Adelaide,edu,3b01a839d174dad6f2635cff7ebe7e1aaad701a4,citation,http://pdfs.semanticscholar.org/3b01/a839d174dad6f2635cff7ebe7e1aaad701a4.pdf,Image Co-localization by Mimicking a Good Detector's Confidence Score Distribution,2016 -51,VOC,voc,31.83907195,117.26420748,University of Science and Technology of China,edu,d467035d83fb4e86c4a47b2ca87894388deb8c44,citation,https://pdfs.semanticscholar.org/d467/035d83fb4e86c4a47b2ca87894388deb8c44.pdf,Relief R-CNN : Utilizing Convolutional Feature Interrelationship for Object Detection,2016 -52,VOC,voc,30.284151,-97.73195598,University of Texas at Austin,edu,264a2b946fae4af23c646cc08fc56947b5be82cf,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2015.7301302,Robust object recognition in RGB-D egocentric videos based on Sparse Affine Hull Kernel,2015 -53,VOC,voc,34.0687788,-118.4450094,"University of California, Los Angeles",edu,480888bad59b314236f2d947ebf308ae146c98e4,citation,https://arxiv.org/pdf/1511.06881.pdf,Zoom Better to See Clearer: Human and Object Parsing with Hierarchical Auto-Zoom Net,2016 -54,VOC,voc,25.01682835,121.53846924,National Taiwan University,edu,a1ee55d529e04a80f4eae3b30d0961a985a64fa4,citation,http://www.cs.utexas.edu/~ycsu/publications/mm029-su.pdf,Enabling low bitrate mobile visual recognition: a performance versus bandwidth evaluation,2013 -55,VOC,voc,-34.9189226,138.60423668,University of Adelaide,edu,0cd736baf31dceea1cc39ac72e00b65587f5fb9e,citation,http://pdfs.semanticscholar.org/4ad0/b6f189718a7287c6e7b90eb05331e56db334.pdf,Learning Hash Functions Using Column Generation,2013 -56,VOC,voc,39.2899685,-76.62196103,University of Maryland,edu,6424574cb92b316928c37232869bfadcb5b4c20f,citation,https://arxiv.org/pdf/1711.05282.pdf,C-WSL: Count-Guided Weakly Supervised Localization,2018 -57,VOC,voc,47.6543238,-122.30800894,University of Washington,edu,51eba481dac6b229a7490f650dff7b17ce05df73,citation,http://grail.cs.washington.edu/wp-content/uploads/2016/09/yatskar2016srv.pdf,Situation Recognition: Visual Semantic Role Labeling for Image Understanding,2016 -58,VOC,voc,47.3764534,8.54770931,ETH Zürich,edu,961a5d5750f18e91e28a767b3cb234a77aac8305,citation,http://pdfs.semanticscholar.org/961a/5d5750f18e91e28a767b3cb234a77aac8305.pdf,Face Detection without Bells and Whistles,2014 -59,VOC,voc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,0c05f60998628884a9ac60116453f1a91bcd9dda,citation,http://pdfs.semanticscholar.org/7b19/80d4ac1730fd0145202a8cb125bf05d96f01.pdf,Optimizing Open-Ended Crowdsourcing: The Next Frontier in Crowdsourced Data Management,2016 -60,VOC,voc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,efa2aacb0fbee857015fad1dba72767f56be6f39,citation,https://pdfs.semanticscholar.org/efa2/aacb0fbee857015fad1dba72767f56be6f39.pdf,Aggregating Crowdsourced Image Segmentations,2018 -61,VOC,voc,37.3936717,-122.0807262,Facebook,company,efa2aacb0fbee857015fad1dba72767f56be6f39,citation,https://pdfs.semanticscholar.org/efa2/aacb0fbee857015fad1dba72767f56be6f39.pdf,Aggregating Crowdsourced Image Segmentations,2018 -62,VOC,voc,34.0687788,-118.4450094,"University of California, Los Angeles",edu,17113b0f647ce05b2e50d1d40c856370f94da7de,citation,http://pdfs.semanticscholar.org/1711/3b0f647ce05b2e50d1d40c856370f94da7de.pdf,Zoom Better to See Clearer: Human Part Segmentation with Auto Zoom Net,2015 -63,VOC,voc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,549d55a06c5402696e063ce36b411f341a64f8a9,citation,http://arxiv.org/pdf/1511.06078v1.pdf,Learning Deep Structure-Preserving Image-Text Embeddings,2016 -64,VOC,voc,33.776033,-84.39884086,Georgia Institute of Technology,edu,549d55a06c5402696e063ce36b411f341a64f8a9,citation,http://arxiv.org/pdf/1511.06078v1.pdf,Learning Deep Structure-Preserving Image-Text Embeddings,2016 -65,VOC,voc,35.9020448,139.93622009,University of Tokyo,edu,44bfa5311f0921664e9036f63cadd71049a35f35,citation,https://pdfs.semanticscholar.org/44bf/a5311f0921664e9036f63cadd71049a35f35.pdf,Faster R-CNN-Based Glomerular Detection in Multistained Human Whole Slide Images,2018 -66,VOC,voc,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,133f1f2679892d408420d8092283539010723359,citation,http://arxiv.org/pdf/1502.05082v3.pdf,What Makes for Effective Detection Proposals?,2016 -67,VOC,voc,60.18558755,24.8242733,Aalto University,edu,98d04187f091f402a90a6a9a2108393ca5f91563,citation,https://arxiv.org/pdf/1807.09828.pdf,ADVIO: An Authentic Dataset for Visual-Inertial Odometry,2018 -68,VOC,voc,61.44964205,23.85877462,Tampere University of Technology,edu,98d04187f091f402a90a6a9a2108393ca5f91563,citation,https://arxiv.org/pdf/1807.09828.pdf,ADVIO: An Authentic Dataset for Visual-Inertial Odometry,2018 -69,VOC,voc,37.4102193,-122.05965487,Carnegie Mellon University,edu,f8015e31d1421f6aee5e17fc3907070b8e0a5e59,citation,http://pdfs.semanticscholar.org/f801/5e31d1421f6aee5e17fc3907070b8e0a5e59.pdf,Towards Usable Multimedia Event Detection from Web Videos,2016 -70,VOC,voc,34.0224149,-118.28634407,University of Southern California,edu,6b9e8acef979c13fa9ecc8fe9b635b312fedbcbe,citation,https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Chang_Multiple_Structured-Instance_Learning_2014_CVPR_paper.pdf,Multiple Structured-Instance Learning for Semantic Segmentation with Uncertain Training Data,2014 -71,VOC,voc,51.4584837,-2.6097752,University of Bristol,edu,72fd97d21d6465d4bb407b6f8f3accd4419a2fb4,citation,https://pdfs.semanticscholar.org/384a/ea88ffd79295c99bcb80552f8655dbb87509.pdf,Automated Identification of Individual Great White Sharks from Unrestricted Fin Imagery,2015 -72,VOC,voc,40.0044795,116.370238,Chinese Academy of Sciences,edu,62b83bf64f200ebb9fa16dfb7108b85e390b2207,citation,https://arxiv.org/pdf/1807.11236.pdf,Semantic Labeling in Very High Resolution Images via a Self-Cascaded Convolutional Neural Network,2018 -73,VOC,voc,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,62b83bf64f200ebb9fa16dfb7108b85e390b2207,citation,https://arxiv.org/pdf/1807.11236.pdf,Semantic Labeling in Very High Resolution Images via a Self-Cascaded Convolutional Neural Network,2018 -74,VOC,voc,37.4102193,-122.05965487,Carnegie Mellon University,edu,2577211aeaaa1f2245ddc379564813bee3d46c06,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Misra_Seeing_Through_the_CVPR_2016_paper.pdf,Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels,2016 -75,VOC,voc,47.6423318,-122.1369302,Microsoft,company,2577211aeaaa1f2245ddc379564813bee3d46c06,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Misra_Seeing_Through_the_CVPR_2016_paper.pdf,Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels,2016 -76,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,3900fb44902396f94fb070be41199b4beecc9081,citation,https://arxiv.org/pdf/1612.02101.pdf,Bottom-Up Top-Down Cues for Weakly-Supervised Semantic Segmentation,2017 -77,VOC,voc,34.0687788,-118.4450094,"University of California, Los Angeles",edu,32c45df9e11e6751bcea1b928f398f6c134d22c6,citation,http://pdfs.semanticscholar.org/32c4/5df9e11e6751bcea1b928f398f6c134d22c6.pdf,Towards Unified Object Detection and Semantic Segmentation,2014 -78,VOC,voc,42.36782045,-71.12666653,Harvard University,edu,2bcd59835528c583bb5b310522a5ba6e99c58b15,citation,http://pdfs.semanticscholar.org/c0ef/596a212d0e40c79c6760673fe122e517b43c.pdf,Multi-class Open Set Recognition Using Probability of Inclusion,2014 -79,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,3920a205990abc7883c70cc96a0410a2d056c2a8,citation,http://groups.inf.ed.ac.uk/calvin/Publications/papazoglouICCV2013-camera-ready.pdf,Fast Object Segmentation in Unconstrained Video,2013 -80,VOC,voc,1.2962018,103.77689944,National University of Singapore,edu,b6810adcfd507b2e019ebc8afe4f44f953faf946,citation,https://pdfs.semanticscholar.org/b681/0adcfd507b2e019ebc8afe4f44f953faf946.pdf,ML-LocNet: Improving Object Localization with Multi-view Learning Network,2018 -81,VOC,voc,40.0141905,-83.0309143,University of Electronic Science and Technology of China,edu,b6810adcfd507b2e019ebc8afe4f44f953faf946,citation,https://pdfs.semanticscholar.org/b681/0adcfd507b2e019ebc8afe4f44f953faf946.pdf,ML-LocNet: Improving Object Localization with Multi-view Learning Network,2018 -82,VOC,voc,33.776033,-84.39884086,Georgia Institute of Technology,edu,0e08cf0b19f0600dadce0f6694420d643ea9828b,citation,http://openaccess.thecvf.com/content_iccv_2015/papers/Humayun_The_Middle_Child_ICCV_2015_paper.pdf,The Middle Child Problem: Revisiting Parametric Min-Cut and Seeds for Object Proposals,2015 -83,VOC,voc,45.5198289,-122.67797964,Oregon State University,edu,0e08cf0b19f0600dadce0f6694420d643ea9828b,citation,http://openaccess.thecvf.com/content_iccv_2015/papers/Humayun_The_Middle_Child_ICCV_2015_paper.pdf,The Middle Child Problem: Revisiting Parametric Min-Cut and Seeds for Object Proposals,2015 -84,VOC,voc,30.19331415,120.11930822,Zhejiang University,edu,81bf7a4b8b3c21d42cb82f946f762c94031e11b8,citation,https://pdfs.semanticscholar.org/81bf/7a4b8b3c21d42cb82f946f762c94031e11b8.pdf,Segmentation of Nerve on Ultrasound Images Using Deep Adversarial Network,2017 -85,VOC,voc,52.4107358,-4.05295501,Aberystwyth University,edu,30d8fbb9345cdf1096635af7d39a9b04af9b72f9,citation,https://pdfs.semanticscholar.org/30d8/fbb9345cdf1096635af7d39a9b04af9b72f9.pdf,Watching plants grow - a position paper on computer vision and Arabidopsis thaliana,2017 -86,VOC,voc,43.66333345,-79.39769975,University of Toronto,edu,87204e4e1a96b8f59cb91828199dacd192292231,citation,http://pdfs.semanticscholar.org/8720/4e4e1a96b8f59cb91828199dacd192292231.pdf,Towards Real-Time Detection and Tracking of Basketball Players using Deep Neural Networks,2017 -87,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,30a4637cbc461838c151073b265fb08e00492ff4,citation,http://faculty.ucmerced.edu/mhyang/papers/cvpr16_object_localization.pdf,Weakly Supervised Object Localization with Progressive Domain Adaptation,2016 -88,VOC,voc,50.7338124,7.1022465,University of Bonn,edu,606cfdcc43203351dbb944a3bb3719695e557e37,citation,https://pdfs.semanticscholar.org/606c/fdcc43203351dbb944a3bb3719695e557e37.pdf,Ex Paucis Plura : Learning Affordance Segmentation from Very Few Examples,2018 -89,VOC,voc,39.2899685,-76.62196103,University of Maryland,edu,47b6cd69c0746688f6e17b37d73fa12422826dbc,citation,http://pdfs.semanticscholar.org/47b6/cd69c0746688f6e17b37d73fa12422826dbc.pdf,Self corrective Perturbations for Semantic Segmentation and Classification,2017 -90,VOC,voc,38.99203005,-76.9461029,University of Maryland College Park,edu,47b6cd69c0746688f6e17b37d73fa12422826dbc,citation,http://pdfs.semanticscholar.org/47b6/cd69c0746688f6e17b37d73fa12422826dbc.pdf,Self corrective Perturbations for Semantic Segmentation and Classification,2017 -91,VOC,voc,42.8298248,-73.87719385,GE Global Research Center,edu,47b6cd69c0746688f6e17b37d73fa12422826dbc,citation,http://pdfs.semanticscholar.org/47b6/cd69c0746688f6e17b37d73fa12422826dbc.pdf,Self corrective Perturbations for Semantic Segmentation and Classification,2017 -92,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,14421119527aa5882e1552a651fbd2d73bc94637,citation,http://pdfs.semanticscholar.org/9b81/86b6bc1e05d7a473d2afebc8a12698d88691.pdf,Searching for objects driven by context,2012 -93,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,14421119527aa5882e1552a651fbd2d73bc94637,citation,http://pdfs.semanticscholar.org/9b81/86b6bc1e05d7a473d2afebc8a12698d88691.pdf,Searching for objects driven by context,2012 -94,VOC,voc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,3410a1489d04ec6fcfbb3d76d39055117931ccf0,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2013.126,Learning Collections of Part Models for Object Recognition,2013 -95,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,69b647afe6526256a93033eac14ce470204e7bae,citation,http://pdfs.semanticscholar.org/d7dd/4fb9074db71ebf9155d64b439102d4c7b0c5.pdf,Training Deep Neural Networks via Direct Loss Minimization,2016 -96,VOC,voc,43.66333345,-79.39769975,University of Toronto,edu,69b647afe6526256a93033eac14ce470204e7bae,citation,http://pdfs.semanticscholar.org/d7dd/4fb9074db71ebf9155d64b439102d4c7b0c5.pdf,Training Deep Neural Networks via Direct Loss Minimization,2016 -97,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,81825711c2aaa1b9d3ead1a300e71c4353a41382,citation,https://arxiv.org/pdf/1607.03476.pdf,End-to-end training of object class detectors for mean average precision,2016 -98,VOC,voc,39.993008,116.329882,SenseTime,company,2ce073da76e6ed87eda2da08da0e00f4f060f1a6,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.78,Deep Saliency with Encoded Low Level Distance Map and High Level Features,2016 -99,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,2313c827d3cb9a291b6a00d015c29580862bbdcc,citation,https://arxiv.org/pdf/1808.03575.pdf,Weakly- and Semi-supervised Panoptic Segmentation,2018 -100,VOC,voc,37.4102193,-122.05965487,Carnegie Mellon University,edu,839a2155995acc0a053a326e283be12068b35cb8,citation,http://pdfs.semanticscholar.org/839a/2155995acc0a053a326e283be12068b35cb8.pdf,Handcrafted Local Features are Convolutional Neural Networks,2015 -101,VOC,voc,32.0565957,118.77408833,Nanjing University,edu,634e02d6107529d672cbbdf5b97990966e289829,citation,https://arxiv.org/pdf/1802.05394.pdf,Cost-Effective Training of Deep CNNs with Active Model Adaptation,2018 -102,VOC,voc,56.45796755,-2.98214831,University of Dundee,edu,d0137881f6c791997337b9cc7f1efbd61977270d,citation,http://pdfs.semanticscholar.org/d013/7881f6c791997337b9cc7f1efbd61977270d.pdf,"University of Dundee An automated pattern recognition system for classifying indirect immunofluorescence images for HEp-2 cells and specimens Manivannan,",2016 -103,VOC,voc,42.2942142,-83.71003894,University of Michigan,edu,ed173a39f4cd980eef319116b6ba39cec1b37c42,citation,https://arxiv.org/pdf/1611.05424.pdf,Associative Embedding: End-to-End Learning for Joint Detection and Grouping,2017 -104,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,ed173a39f4cd980eef319116b6ba39cec1b37c42,citation,https://arxiv.org/pdf/1611.05424.pdf,Associative Embedding: End-to-End Learning for Joint Detection and Grouping,2017 -105,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,84cf838be40e2ab05732fbefbb93ccb2afb0cb48,citation,http://pdfs.semanticscholar.org/84cf/838be40e2ab05732fbefbb93ccb2afb0cb48.pdf,Recognizing Handwritten Characters,2016 -106,VOC,voc,37.26728,126.9841151,Seoul National University,edu,b082f440ee91e2751701401919584203b37e1e1a,citation,https://pdfs.semanticscholar.org/303c/28f1ba643a7cd88255cc379e79052fb7e7b1.pdf,SeedNet : Automatic Seed Generation with Deep Reinforcement Learning for Robust Interactive Segmentation,2018 -107,VOC,voc,22.2081469,114.25964115,University of Hong Kong,edu,6008213e4270e88cb414459de759c961469b92dd,citation,https://arxiv.org/pdf/1802.09129.pdf,"Multi-Evidence Filtering and Fusion for Multi-Label Classification, Object Detection and Semantic Segmentation Based on Weakly Supervised Learning",2018 -108,VOC,voc,33.776033,-84.39884086,Georgia Institute of Technology,edu,90b4470032f2796a347a0080bcd833c2db0e8bf0,citation,https://arxiv.org/pdf/1807.07760.pdf,Improving Image Clustering With Multiple Pretrained CNN Feature Extractors,2018 -109,VOC,voc,40.0044795,116.370238,Chinese Academy of Sciences,edu,beecaf2d6e9d102b6b2459ea38e15179a4b55ffd,citation,https://arxiv.org/pdf/1611.09587.pdf,Surveillance Video Parsing with Single Frame Supervision,2017 -110,VOC,voc,41.3868913,2.16352385,University of Barcelona,edu,0fb8317a8bf5feaf297af8e9b94c50c5ed0e8277,citation,http://pdfs.semanticscholar.org/0fb8/317a8bf5feaf297af8e9b94c50c5ed0e8277.pdf,Detecting Hands in Egocentric Videos: Towards Action Recognition,2017 -111,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,0e0179eb4b43016691f0f1473a08089dda21f8f0,citation,http://pdfs.semanticscholar.org/0e01/79eb4b43016691f0f1473a08089dda21f8f0.pdf,The Art of Detection,2016 -112,VOC,voc,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,135c957f6a80f250507c7707479e584c288f430f,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2014.498,Image-Based Synthesis and Re-synthesis of Viewpoints Guided by 3D Models,2014 -113,VOC,voc,39.00041165,-77.10327775,National Institutes of Health,edu,c72b063e23b8b45b57a42ebc2f9714297c539a6f,citation,https://arxiv.org/pdf/1801.04334.pdf,TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-rays,2018 -114,VOC,voc,36.05238585,140.11852361,National Institute of Advanced Industrial Science and Technology,edu,061ffd3967540424ac4e4066f4a605d8318bab90,citation,https://staff.aist.go.jp/takumi.kobayashi/publication/2014/CVPR2014.pdf,Dirichlet-Based Histogram Feature Transform for Image Classification,2014 -115,VOC,voc,42.3583961,-71.09567788,MIT,edu,1a2e9a56e5f71bf95a2f68b6e67e2aaa1c6bf91e,citation,http://pdfs.semanticscholar.org/1a2e/9a56e5f71bf95a2f68b6e67e2aaa1c6bf91e.pdf,FPM: Fine Pose Parts-Based Model with 3D CAD Models,2014 -116,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,c6f58adf4a5ee8499cbc9b9bc1e6f1c39f1f8eae,citation,https://pdfs.semanticscholar.org/c6f5/8adf4a5ee8499cbc9b9bc1e6f1c39f1f8eae.pdf,Earn to P Ay a Ttention,2018 -117,VOC,voc,32.87935255,-117.23110049,"University of California, San Diego",edu,3c8db2ca155ce4e15ec8a2c4c4b979de654fb296,citation,http://pages.ucsd.edu/~ztu/publication/iccv15_hed.pdf,Holistically-Nested Edge Detection,2015 -118,VOC,voc,59.34986645,18.07063213,"KTH Royal Institute of Technology, Stockholm",edu,8ccd6aaf1ee4b66c13fffbf560e3920f9bdf5f10,citation,http://pdfs.semanticscholar.org/8ccd/6aaf1ee4b66c13fffbf560e3920f9bdf5f10.pdf,A multitask deep learning model for real-time deployment in embedded systems,2017 -119,VOC,voc,53.5238572,-113.52282665,University of Alberta,edu,b4f5cf797a1c857f32e5740d53d9990bc925af2b,citation,https://pdfs.semanticscholar.org/b4f5/cf797a1c857f32e5740d53d9990bc925af2b.pdf,Review of Segmentation with Deep Learning and Discover Its Application in Ultrasound Images,2018 -120,VOC,voc,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,3bad18554678ab46bbbf9de41d36423bc8083c83,citation,http://arxiv.org/pdf/1511.07803v1.pdf,Weakly Supervised Object Boundaries,2016 -121,VOC,voc,24.7925484,120.9951183,National Tsing Hua University,edu,07191c2047b5b643dd72a0583c1d537ba59f977a,citation,http://pdfs.semanticscholar.org/0719/1c2047b5b643dd72a0583c1d537ba59f977a.pdf,Interactive Segmentation from 1-Bit Feedback,2016 -122,VOC,voc,37.26728,126.9841151,Seoul National University,edu,ae6e8851dfd9c97e37e1cbd61b21cc54d5e2b9c7,citation,https://arxiv.org/pdf/1802.04977.pdf,Paraphrasing Complex Network: Network Compression via Factor Transfer,2018 -123,VOC,voc,37.26728,126.9841151,Seoul National University,edu,5375a3344017d9502ebb4170325435de3da1fa16,citation,https://doi.org/10.1007/978-3-642-37447-0,Computer Vision – ACCV 2012,2012 -124,VOC,voc,40.0044795,116.370238,Chinese Academy of Sciences,edu,5375a3344017d9502ebb4170325435de3da1fa16,citation,https://doi.org/10.1007/978-3-642-37447-0,Computer Vision – ACCV 2012,2012 -125,VOC,voc,33.776033,-84.39884086,Georgia Institute of Technology,edu,5375a3344017d9502ebb4170325435de3da1fa16,citation,https://doi.org/10.1007/978-3-642-37447-0,Computer Vision – ACCV 2012,2012 -126,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,fdfd57d4721174eba288e501c0c120ad076cdca8,citation,https://arxiv.org/pdf/1704.07129.pdf,An Analysis of Action Recognition Datasets for Language and Vision Tasks,2017 -127,VOC,voc,32.0565957,118.77408833,Nanjing University,edu,ec83c63e28ae2a658bc76a6750e078c3a54b9760,citation,https://arxiv.org/pdf/1705.02758.pdf,Deep Descriptor Transforming for Image Co-Localization,2017 -128,VOC,voc,-34.9189226,138.60423668,University of Adelaide,edu,ec83c63e28ae2a658bc76a6750e078c3a54b9760,citation,https://arxiv.org/pdf/1705.02758.pdf,Deep Descriptor Transforming for Image Co-Localization,2017 -129,VOC,voc,59.34986645,18.07063213,"KTH Royal Institute of Technology, Stockholm",edu,b1177aad0db8bd6b605ffe0d68addaf97b1f9a6b,citation,https://pdfs.semanticscholar.org/5035/733022916db7e5965c565327e169da1e2f39.pdf,Visual Representations and Models: From Latent SVM to Deep Learning,2016 -130,VOC,voc,31.83907195,117.26420748,University of Science and Technology of China,edu,a5ae7d662ed086bc5b0c9a2c1dc54fcb23635000,citation,https://pdfs.semanticscholar.org/a5ae/7d662ed086bc5b0c9a2c1dc54fcb23635000.pdf,Relief R-CNN : Utilizing Convolutional Feature Interrelationship for Fast Object Detection Deployment,2016 -131,VOC,voc,22.53521465,113.9315911,Shenzhen University,edu,a5ae7d662ed086bc5b0c9a2c1dc54fcb23635000,citation,https://pdfs.semanticscholar.org/a5ae/7d662ed086bc5b0c9a2c1dc54fcb23635000.pdf,Relief R-CNN : Utilizing Convolutional Feature Interrelationship for Fast Object Detection Deployment,2016 -132,VOC,voc,53.38522185,-6.25740874,Dublin City University,edu,9528e2e8c20517ab916f803c0371abb4f0ed488b,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Pan_Shallow_and_Deep_CVPR_2016_paper.pdf,Shallow and Deep Convolutional Networks for Saliency Prediction,2016 -133,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,e2272f50ffa33b8e41509e4b795ad5a4eb27bb46,citation,https://arxiv.org/pdf/1607.07671.pdf,Region-based semantic segmentation with end-to-end training,2016 -134,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,b8d61dc56a4112e0317c6a7323417ee649476148,citation,https://arxiv.org/pdf/1807.05636.pdf,Cross Pixel Optical Flow Similarity for Self-Supervised Learning,2018 -135,VOC,voc,40.0044795,116.370238,Chinese Academy of Sciences,edu,db0a4af734dab1854c2e8dfe499fe0e353226e45,citation,https://pdfs.semanticscholar.org/db0a/4af734dab1854c2e8dfe499fe0e353226e45.pdf,Hot Anchors: A Heuristic Anchors Sampling Method in RCNN-Based Object Detection,2018 -136,VOC,voc,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,db0a4af734dab1854c2e8dfe499fe0e353226e45,citation,https://pdfs.semanticscholar.org/db0a/4af734dab1854c2e8dfe499fe0e353226e45.pdf,Hot Anchors: A Heuristic Anchors Sampling Method in RCNN-Based Object Detection,2018 -137,VOC,voc,33.776033,-84.39884086,Georgia Institute of Technology,edu,ffe0f43206169deef3a2bf64cec90fe35bb1a8e5,citation,http://pdfs.semanticscholar.org/ffe0/f43206169deef3a2bf64cec90fe35bb1a8e5.pdf,"Automated Processing of Imaging Data through Multi-tiered Classification of Biological Structures Illustrated Using Caenorhabditis elegans -",2015 -138,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,ffe0f43206169deef3a2bf64cec90fe35bb1a8e5,citation,http://pdfs.semanticscholar.org/ffe0/f43206169deef3a2bf64cec90fe35bb1a8e5.pdf,"Automated Processing of Imaging Data through Multi-tiered Classification of Biological Structures Illustrated Using Caenorhabditis elegans -",2015 -139,VOC,voc,45.77445695,126.67684917,Harbin Engineering University,edu,479eb6579194d4d944671dfe5e90b122ca4b58fd,citation,https://pdfs.semanticscholar.org/479e/b6579194d4d944671dfe5e90b122ca4b58fd.pdf,Structural inference embedded adversarial networks for scene parsing,2018 -140,VOC,voc,34.2469152,108.91061982,Northwestern Polytechnical University,edu,479eb6579194d4d944671dfe5e90b122ca4b58fd,citation,https://pdfs.semanticscholar.org/479e/b6579194d4d944671dfe5e90b122ca4b58fd.pdf,Structural inference embedded adversarial networks for scene parsing,2018 -141,VOC,voc,1.29500195,103.84909214,Singapore Management University,edu,d289ce63055c10937e5715e940a4bb9d0af7a8c5,citation,http://dl.acm.org/citation.cfm?id=3081360,DeepMon: Mobile GPU-based Deep Learning Framework for Continuous Vision Applications,2017 -142,VOC,voc,60.18558755,24.8242733,Aalto University,edu,061bba574c7c2ef0ba9de91afc4fcab70feddd4f,citation,http://doi.ieeecomputersociety.org/10.1109/ICCV.2017.272,Paying Attention to Descriptions Generated by Image Captioning Models,2017 -143,VOC,voc,28.59899755,-81.19712501,University of Central Florida,edu,061bba574c7c2ef0ba9de91afc4fcab70feddd4f,citation,http://doi.ieeecomputersociety.org/10.1109/ICCV.2017.272,Paying Attention to Descriptions Generated by Image Captioning Models,2017 -144,VOC,voc,34.7275714,135.2371,Kobe University,edu,ee2217f9d22d6a18aaf97f05768035c38305d1fa,citation,https://doi.org/10.1109/APSIPA.2015.7415501,Detection of facial parts via deformable part model using part annotation,2015 -145,VOC,voc,50.7791703,6.06728733,RWTH Aachen University,edu,18219d85bb14f851fc4714df19cc7f38dff8ddc3,citation,http://pdfs.semanticscholar.org/1821/9d85bb14f851fc4714df19cc7f38dff8ddc3.pdf,Online Adaptation of Convolutional Neural Networks for the 2017 DAVIS Challenge on Video Object Segmentation,2017 -146,VOC,voc,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,da44881db32c132eb9cdef524618e3c8ed340b47,citation,https://arxiv.org/pdf/1802.00383.pdf,Annotation-Free and One-Shot Learning for Instance Segmentation of Homogeneous Object Clusters,2018 -147,VOC,voc,50.7338124,7.1022465,University of Bonn,edu,cc94b423c298003f0f164e63e63177d443291a77,citation,https://arxiv.org/pdf/1805.03994.pdf,Multi-View Semantic Labeling of 3D Point Clouds for Automated Plant Phenotyping,2018 -148,VOC,voc,39.9922379,116.30393816,Peking University,edu,83a811fd947415df2413d15386dbc558f07595cb,citation,https://arxiv.org/pdf/1709.08295.pdf,Fine-grained Discriminative Localization via Saliency-guided Faster R-CNN,2017 -149,VOC,voc,-33.8809651,151.20107299,University of Technology Sydney,edu,3a5f5aca6138abcf22ede1af5572e01eb0f761d1,citation,https://pdfs.semanticscholar.org/3a5f/5aca6138abcf22ede1af5572e01eb0f761d1.pdf,Optimizing Multivariate Performance Measures from Multi-View Data,2016 -150,VOC,voc,34.2469152,108.91061982,Northwestern Polytechnical University,edu,ce300b006f42c1b64ca0e53d1cf28d11a98ece8f,citation,https://pdfs.semanticscholar.org/ce30/0b006f42c1b64ca0e53d1cf28d11a98ece8f.pdf,Learning Multi-Instance Enriched Image Representations via Non-Greedy Ratio Maximization of the l 1-Norm Distances,0 -151,VOC,voc,34.0224149,-118.28634407,University of Southern California,edu,71b038958df0b7855fc7b8b8e7dcde8537a7c1ad,citation,http://pdfs.semanticscholar.org/71b0/38958df0b7855fc7b8b8e7dcde8537a7c1ad.pdf,Kernel Methods for Unsupervised Domain Adaptation by Boqing Gong,2015 -152,VOC,voc,34.2469152,108.91061982,Northwestern Polytechnical University,edu,af7cab9b4a2a2a565a3efe0a226c517f47289077,citation,https://arxiv.org/pdf/1803.10910.pdf,Deep Unsupervised Saliency Detection: A Multiple Noisy Labeling Perspective,2018 -153,VOC,voc,-35.2776999,149.118527,Australian National University,edu,af7cab9b4a2a2a565a3efe0a226c517f47289077,citation,https://arxiv.org/pdf/1803.10910.pdf,Deep Unsupervised Saliency Detection: A Multiple Noisy Labeling Perspective,2018 -154,VOC,voc,-34.9189226,138.60423668,University of Adelaide,edu,3a6ebdfb6375093885e846153a48139ef1ecfae6,citation,http://arxiv.org/abs/1411.7466,The treasure beneath convolutional layers: Cross-convolutional-layer pooling for image classification,2015 -155,VOC,voc,51.24303255,-0.59001382,University of Surrey,edu,a7e9d230bc44dfbe56757f3025d5b4caa49032f3,citation,http://pdfs.semanticscholar.org/a7e9/d230bc44dfbe56757f3025d5b4caa49032f3.pdf,Unity in Diversity: Discovering Topics from Words - Information Theoretic Co-clustering for Visual Categorization,2012 -156,VOC,voc,37.5557271,127.0436642,Hanyang University,edu,50137d663802224e683951c48970496b38b02141,citation,http://pdfs.semanticscholar.org/5013/7d663802224e683951c48970496b38b02141.pdf,DETRAC: A New Benchmark and Protocol for Multi-Object Tracking,2015 -157,VOC,voc,37.4102193,-122.05965487,Carnegie Mellon University,edu,07de8371ad4901356145722aa29abaeafd0986b9,citation,http://pdfs.semanticscholar.org/07de/8371ad4901356145722aa29abaeafd0986b9.pdf,Towards Usable Multimedia Event Detection,2017 -158,VOC,voc,41.21002475,-73.80407056,IBM Thomas J. Watson Research Center,company,af386bb1b5e8c9f65b3ae836198a93aa860d6331,citation,https://arxiv.org/pdf/1805.04574.pdf,Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation,2018 -159,VOC,voc,17.4454957,78.34854698,International Institute of Information Technology,edu,d6b1b0e60e1764982ef95d4ade8fcaa10bfb156a,citation,http://pdfs.semanticscholar.org/d6b1/b0e60e1764982ef95d4ade8fcaa10bfb156a.pdf,A Sketch-based Approach for Multimedia Retrieval,2016 -160,VOC,voc,51.49887085,-0.17560797,Imperial College London,edu,37b3637dab65b91a5c91bb6a583e69c448823cc1,citation,https://arxiv.org/pdf/1705.05994.pdf,Learning a Hierarchical Latent-Variable Model of 3D Shapes,2018 -161,VOC,voc,39.9574,-75.19026706,Drexel University,edu,83d16fb8f53156c9e2b28d75abb6532af515440f,citation,http://pdfs.semanticscholar.org/83d1/6fb8f53156c9e2b28d75abb6532af515440f.pdf,Large-scale Document Labeling using Supervised Sequence Embedding,2012 -162,VOC,voc,45.51181205,-122.68492999,Portland State University,edu,05e45f61dc7577c50114a382abc6e952ae24cdac,citation,https://pdfs.semanticscholar.org/05e4/5f61dc7577c50114a382abc6e952ae24cdac.pdf,"Object Detection and Recognition in Natural Settings by George William Dittmar A thesis submitted in partial fulfilment of the requirements of the degree Master of Science in Computer Science Thesis Committee: Melanie Mitchell, Chair",2012 -163,VOC,voc,37.4102193,-122.05965487,Carnegie Mellon University,edu,192235f5a9e4c9d6a28ec0d333e36f294b32f764,citation,http://www.andrew.cmu.edu/user/sjayasur/iccv.pdf,Reconfiguring the Imaging Pipeline for Computer Vision,2017 -164,VOC,voc,42.4505507,-76.4783513,Cornell University,edu,192235f5a9e4c9d6a28ec0d333e36f294b32f764,citation,http://www.andrew.cmu.edu/user/sjayasur/iccv.pdf,Reconfiguring the Imaging Pipeline for Computer Vision,2017 -165,VOC,voc,50.0764296,14.41802312,Czech Technical University,edu,bd4f2e7a196c0d6033a49390ee8836f4f551b7c8,citation,http://rrc.cvc.uab.es/files/Robust-Reading-Competition-Karatzas.pdf,ICDAR 2015 competition on Robust Reading,2015 -166,VOC,voc,33.59914655,130.22359848,Kyushu University,edu,bd4f2e7a196c0d6033a49390ee8836f4f551b7c8,citation,http://rrc.cvc.uab.es/files/Robust-Reading-Competition-Karatzas.pdf,ICDAR 2015 competition on Robust Reading,2015 -167,VOC,voc,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,3d5575e9ba02128d94c20330f4525fc816411ec2,citation,https://arxiv.org/pdf/1612.02646.pdf,Learning Video Object Segmentation from Static Images,2017 -168,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,78f62042bfb3bb49ba10e142d118a9bb058b2a19,citation,http://pdfs.semanticscholar.org/78f6/2042bfb3bb49ba10e142d118a9bb058b2a19.pdf,WebSeg: Learning Semantic Segmentation from Web Searches,2018 -169,VOC,voc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,0c7aac75ccd17d696cff2e1ce95db0493f5c18a2,citation,https://arxiv.org/pdf/1809.01123.pdf,VideoMatch: Matching Based Video Object Segmentation,2018 -170,VOC,voc,3.12267405,101.65356103,University of Malaya,edu,6c78add400f749c897dc3eb93996eda1c796e91c,citation,https://arxiv.org/pdf/1410.3752.pdf,Enhanced Random Forest with Image/Patch-Level Learning for Image Understanding,2014 -171,VOC,voc,51.49887085,-0.17560797,Imperial College London,edu,6c78add400f749c897dc3eb93996eda1c796e91c,citation,https://arxiv.org/pdf/1410.3752.pdf,Enhanced Random Forest with Image/Patch-Level Learning for Image Understanding,2014 -172,VOC,voc,39.9922379,116.30393816,Peking University,edu,6c78add400f749c897dc3eb93996eda1c796e91c,citation,https://arxiv.org/pdf/1410.3752.pdf,Enhanced Random Forest with Image/Patch-Level Learning for Image Understanding,2014 -173,VOC,voc,-34.9189226,138.60423668,University of Adelaide,edu,b61c0b11b1c25958d202b4f7ca772e1d95ee1037,citation,http://pdfs.semanticscholar.org/b61c/0b11b1c25958d202b4f7ca772e1d95ee1037.pdf,Bridging Category-level and Instance-level Semantic Image Segmentation,2016 -174,VOC,voc,34.0224149,-118.28634407,University of Southern California,edu,79894ddf290d3c7a768d634eceb7888564b5cf19,citation,https://arxiv.org/pdf/1708.01676.pdf,Query-Guided Regression Network with Context Policy for Phrase Grounding,2017 -175,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,fec2a5a06a3aab5efe923a78d208ec747d5e4894,citation,https://arxiv.org/pdf/1805.12018.pdf,Generalizing to Unseen Domains via Adversarial Data Augmentation,2018 -176,VOC,voc,31.30104395,121.50045497,Fudan University,edu,5ac63895a7d3371a739d066bb1631fc178d8276a,citation,http://doi.acm.org/10.1145/3123266.3123379,Learning Semantic Feature Map for Visual Content Recognition,2017 -177,VOC,voc,39.2899685,-76.62196103,University of Maryland,edu,5ac63895a7d3371a739d066bb1631fc178d8276a,citation,http://doi.acm.org/10.1145/3123266.3123379,Learning Semantic Feature Map for Visual Content Recognition,2017 -178,VOC,voc,-34.40505545,150.87834655,University of Wollongong,edu,4e559f23bcf502c752f2938ad7f0182047b8d1e4,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Wang_A_Fast_Approximate_2013_CVPR_paper.pdf,A Fast Approximate AIB Algorithm for Distributional Word Clustering,2013 -179,VOC,voc,-35.2776999,149.118527,Australian National University,edu,7536b6a9f3cb4ae810e2ef6d0219134b4e546dd0,citation,http://pdfs.semanticscholar.org/7536/b6a9f3cb4ae810e2ef6d0219134b4e546dd0.pdf,Semi-Automatic Image Labelling Using Depth Information,2015 -180,VOC,voc,42.7298459,-73.67950216,Rensselaer Polytechnic Institute,edu,11b89011298e193d9e6a1d99302221c1d8645bda,citation,http://openaccess.thecvf.com/content_iccv_2015/papers/Gao_Structured_Feature_Selection_ICCV_2015_paper.pdf,Structured Feature Selection,2015 -181,VOC,voc,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,de3245c795bc50ebdb5d929c8da664341238264a,citation,https://arxiv.org/pdf/1705.08590.pdf,Generative Model With Coordinate Metric Learning for Object Recognition Based on 3D Models,2018 -182,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,cc2eaa182f33defbb33d69e9547630aab7ed9c9c,citation,http://pdfs.semanticscholar.org/ce2e/e807a63bbdffa530c80915b04d11a7f29a21.pdf,Surpassing Humans and Computers with JELLYBEAN: Crowd-Vision-Hybrid Counting Algorithms,2015 -183,VOC,voc,40.00471095,-83.02859368,Ohio State University,edu,cc2eaa182f33defbb33d69e9547630aab7ed9c9c,citation,http://pdfs.semanticscholar.org/ce2e/e807a63bbdffa530c80915b04d11a7f29a21.pdf,Surpassing Humans and Computers with JELLYBEAN: Crowd-Vision-Hybrid Counting Algorithms,2015 -184,VOC,voc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,cc2eaa182f33defbb33d69e9547630aab7ed9c9c,citation,http://pdfs.semanticscholar.org/ce2e/e807a63bbdffa530c80915b04d11a7f29a21.pdf,Surpassing Humans and Computers with JELLYBEAN: Crowd-Vision-Hybrid Counting Algorithms,2015 -185,VOC,voc,32.0565957,118.77408833,Nanjing University,edu,9c71e6f4e27b3a6f0f872ec683b0f6dfe0966c05,citation,http://pdfs.semanticscholar.org/9c71/e6f4e27b3a6f0f872ec683b0f6dfe0966c05.pdf,"Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey",2017 -186,VOC,voc,1.2962018,103.77689944,National University of Singapore,edu,b88b83d2ffd30bf3bc3be3fb7492fd88f633b2fe,citation,http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/IEEE_CVPR2013/data/Papers/4989a827.pdf,Subcategory-Aware Object Classification,2013 -187,VOC,voc,1.2962018,103.77689944,National University of Singapore,edu,b6a3802075d460093977f8566c451f950edf7a47,citation,https://pdfs.semanticscholar.org/0999/e5baf505eed0df8e2661c29354f3757b3399.pdf,Facilitating and Exploring Planar Homogeneous Texture for Indoor Scene Understanding,2016 -188,VOC,voc,51.7555205,-1.2261597,Oxford Brookes University,edu,cd6cab9357f333ad9966abc76f830c190a1b7911,citation,https://pdfs.semanticscholar.org/cd6c/ab9357f333ad9966abc76f830c190a1b7911.pdf,"Recognition, reorganisation, reconstruction and reinteraction for scene understanding",2014 -189,VOC,voc,47.3764534,8.54770931,ETH Zürich,edu,0fe8b5503681128da84a8454a4cc94470adc09ea,citation,http://pdfs.semanticscholar.org/b96a/0ccae1d15cffe3b479b2c56d9132b05cd846.pdf,Sparsity Potentials for Detecting Objects with the Hough Transform,2012 -190,VOC,voc,35.7036227,51.35125097,Sharif University of Technology,edu,0fe8b5503681128da84a8454a4cc94470adc09ea,citation,http://pdfs.semanticscholar.org/b96a/0ccae1d15cffe3b479b2c56d9132b05cd846.pdf,Sparsity Potentials for Detecting Objects with the Hough Transform,2012 -191,VOC,voc,47.6423318,-122.1369302,Microsoft,company,9bbc952adb3e3c6091d45d800e806d3373a52bac,citation,https://pdfs.semanticscholar.org/9bbc/952adb3e3c6091d45d800e806d3373a52bac.pdf,Learning Visual Classifiers using Human-centric Annotations,2015 -192,VOC,voc,35.6572957,139.54255868,University of Electro-Communications,edu,6e209d7d33c0be8afae863f4e4e9c3e86826711f,citation,http://img.cs.uec.ac.jp/pub/conf16/161204shimok_1_ppt.pdf,Weakly-supervised segmentation by combining CNN feature maps and object saliency maps,2016 -193,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,46d85e1dc7057bef62647bd9241601e9896a1b02,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/2A_040_ext.pdf,Improving object proposals with multi-thresholding straddling expansion,2015 -194,VOC,voc,35.2742655,137.01327841,Chubu University,edu,67e3fac91c699c085d47774990572d8ccdc36f15,citation,http://pdfs.semanticscholar.org/67e3/fac91c699c085d47774990572d8ccdc36f15.pdf,Multiple Skip Connections and Dilated Convolutions for Semantic Segmentation,2017 -195,VOC,voc,34.0224149,-118.28634407,University of Southern California,edu,a4f29217d2120ed1490aea7e1c5b78c3b76e972f,citation,https://arxiv.org/pdf/1610.06907.pdf,Enhanced object detection via fusion with prior beliefs from image classification,2017 -196,VOC,voc,33.776033,-84.39884086,Georgia Institute of Technology,edu,f2d07a77711a8d74bbfa48a0436dae18a698b05a,citation,http://pdfs.semanticscholar.org/f2d0/7a77711a8d74bbfa48a0436dae18a698b05a.pdf,Composite Statistical Learning and Inference for Semantic Segmentation,2014 -197,VOC,voc,40.2075951,-8.42566148,University of Coimbra,edu,f2d07a77711a8d74bbfa48a0436dae18a698b05a,citation,http://pdfs.semanticscholar.org/f2d0/7a77711a8d74bbfa48a0436dae18a698b05a.pdf,Composite Statistical Learning and Inference for Semantic Segmentation,2014 -198,VOC,voc,55.7039571,13.1902011,Lund University,edu,f2d07a77711a8d74bbfa48a0436dae18a698b05a,citation,http://pdfs.semanticscholar.org/f2d0/7a77711a8d74bbfa48a0436dae18a698b05a.pdf,Composite Statistical Learning and Inference for Semantic Segmentation,2014 -199,VOC,voc,61.44964205,23.85877462,Tampere University of Technology,edu,ff11cb09e409996020a2dc3a8afc3b535e6b2482,citation,https://arxiv.org/pdf/1807.03142.pdf,Faster Bounding Box Annotation for Object Detection in Indoor Scenes,2018 -200,VOC,voc,35.84658875,127.1350133,Chonbuk National University,edu,e103fa24d7fa297cd206b22b3bf670bfda6c65c4,citation,https://pdfs.semanticscholar.org/e103/fa24d7fa297cd206b22b3bf670bfda6c65c4.pdf,Object Detection in Very High-Resolution Aerial Images Using One-Stage Densely Connected Feature Pyramid Network,2018 -201,VOC,voc,41.8268682,-71.40123146,Brown University,edu,9a781a01b5a9c210dd2d27db8b73b7d62bc64837,citation,http://pdfs.semanticscholar.org/9a78/1a01b5a9c210dd2d27db8b73b7d62bc64837.pdf,An Attempt to Build Object Detection Models by Reusing Parts,2013 -202,VOC,voc,1.2962018,103.77689944,National University of Singapore,edu,ac559888f996923c06b1cf90db6b57b12e582289,citation,http://pdfs.semanticscholar.org/ac55/9888f996923c06b1cf90db6b57b12e582289.pdf,Benchmarking neuromorphic vision: lessons learnt from computer vision,2015 -203,VOC,voc,47.3764534,8.54770931,ETH Zürich,edu,ac559888f996923c06b1cf90db6b57b12e582289,citation,http://pdfs.semanticscholar.org/ac55/9888f996923c06b1cf90db6b57b12e582289.pdf,Benchmarking neuromorphic vision: lessons learnt from computer vision,2015 -204,VOC,voc,39.2899685,-76.62196103,University of Maryland,edu,ac559888f996923c06b1cf90db6b57b12e582289,citation,http://pdfs.semanticscholar.org/ac55/9888f996923c06b1cf90db6b57b12e582289.pdf,Benchmarking neuromorphic vision: lessons learnt from computer vision,2015 -205,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,2a4fc35acaf09517e9c63821cadd428a84832416,citation,http://www.vision.ee.ethz.ch/en/publications/papers/proceedings/eth_biwi_00905.pdf,Learning object class detectors from weakly annotated video,2012 -206,VOC,voc,22.053565,113.39913285,Jilin University,edu,cd4850de71e4e858be5f5e6ef7f48d5bf7decea6,citation,http://pdfs.semanticscholar.org/cd48/50de71e4e858be5f5e6ef7f48d5bf7decea6.pdf,Distribution Entropy Boosted VLAD for Image Retrieval,2016 -207,VOC,voc,40.4319722,-86.92389368,Purdue University,edu,34b925a111ba29f73f5c0d1b363f357958d563c1,citation,https://www.microsoft.com/en-us/research/wp-content/uploads/2015/03/Shoaib_DATE_2015.pdf,SAPPHIRE: An always-on context-aware computer vision system for portable devices,2015 -208,VOC,voc,47.6423318,-122.1369302,Microsoft,company,34b925a111ba29f73f5c0d1b363f357958d563c1,citation,https://www.microsoft.com/en-us/research/wp-content/uploads/2015/03/Shoaib_DATE_2015.pdf,SAPPHIRE: An always-on context-aware computer vision system for portable devices,2015 -209,VOC,voc,24.7925484,120.9951183,National Tsing Hua University,edu,c76b611a986a2e09df22603d93b2d9125aaff369,citation,https://arxiv.org/pdf/1810.07050.pdf,Generating Self-Guided Dense Annotations for Weakly Supervised Semantic Segmentation,2018 -210,VOC,voc,22.053565,113.39913285,Jilin University,edu,1927d01b6b9acf865401b544e25b62a7ddbac5fa,citation,https://pdfs.semanticscholar.org/1927/d01b6b9acf865401b544e25b62a7ddbac5fa.pdf,An Enhanced Region Proposal Network for object detection using deep learning method,2018 -211,VOC,voc,-33.8809651,151.20107299,University of Technology Sydney,edu,1ecd20f7fc34344e396825d27bc5a9871ab0d0c2,citation,https://arxiv.org/pdf/1810.09091.pdf,SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation,2018 -212,VOC,voc,42.3583961,-71.09567788,MIT,edu,26aa0aff1ea1baf848a521363cc455044690e090,citation,http://pdfs.semanticscholar.org/26aa/0aff1ea1baf848a521363cc455044690e090.pdf,A 2D + 3D Rich Data Approach to Scene Understanding,2013 -213,VOC,voc,46.0658836,11.1159894,University of Trento,edu,3548cb9ee54bd4c8b3421f1edd393da9038da293,citation,http://www.huppelen.nl/publications/2012cvprUnseenEventCompositionality.pdf,(Unseen) event recognition via semantic compositionality,2012 -214,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,25ee08db14dca641d085584909b551042618b8bf,citation,http://pdfs.semanticscholar.org/25ee/08db14dca641d085584909b551042618b8bf.pdf,Learning to Segment Instances in Videos with Spatial Propagation Network,2017 -215,VOC,voc,37.36566745,-120.42158888,"University of California, Merced",edu,25ee08db14dca641d085584909b551042618b8bf,citation,http://pdfs.semanticscholar.org/25ee/08db14dca641d085584909b551042618b8bf.pdf,Learning to Segment Instances in Videos with Spatial Propagation Network,2017 -216,VOC,voc,48.9095338,9.1831892,University of Stuttgart,edu,d0f81c31e11af1783644704321903a3d2bd83fd6,citation,https://pdfs.semanticscholar.org/d0f8/1c31e11af1783644704321903a3d2bd83fd6.pdf,3D Façade Labeling over Complex Scenarios: A Case Study Using Convolutional Neural Network and Structure-From-Motion,2018 -217,VOC,voc,50.7369302,-3.53647672,University of Exeter,edu,d0f81c31e11af1783644704321903a3d2bd83fd6,citation,https://pdfs.semanticscholar.org/d0f8/1c31e11af1783644704321903a3d2bd83fd6.pdf,3D Façade Labeling over Complex Scenarios: A Case Study Using Convolutional Neural Network and Structure-From-Motion,2018 -218,VOC,voc,38.99203005,-76.9461029,University of Maryland College Park,edu,a996f22a2d0c685f7e4972df9f45e99efc3cbb76,citation,https://arxiv.org/pdf/1708.00079.pdf,Towards the Success Rate of One: Real-Time Unconstrained Salient Object Detection,2018 -219,VOC,voc,47.05821,15.46019568,Graz University of Technology,edu,4da5f0c1d07725a06c6b4a2646e31ea3a5f14435,citation,http://pdfs.semanticscholar.org/4da5/f0c1d07725a06c6b4a2646e31ea3a5f14435.pdf,End-to-End Training of Hybrid CNN-CRF Models for Semantic Segmentation using Structured Learning,2017 -220,VOC,voc,52.3553655,4.9501644,University of Amsterdam,edu,26c58e24687ccbe9737e41837aab74e4a499d259,citation,http://www.cv-foundation.org/openaccess/content_iccv_2013/papers/Li_Codemaps_-_Segment_2013_ICCV_paper.pdf,"Codemaps - Segment, Classify and Search Objects Locally",2013 -221,VOC,voc,37.4219999,-122.0840575,Google,company,299b65d5d3914dad9aae2f936165dcebcf78db88,citation,http://doi.ieeecomputersociety.org/10.1109/ICCV.2015.203,Weakly-and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation,2015 -222,VOC,voc,-34.9189226,138.60423668,University of Adelaide,edu,cb5dcd048b0eaa78a887a014be26a8a7b1325d36,citation,https://arxiv.org/pdf/1709.04093.pdf,Joint Learning of Set Cardinality and State Distribution,2018 -223,VOC,voc,34.2469152,108.91061982,Northwestern Polytechnical University,edu,63660c50e2669a5115c2379e622549d8ed79be00,citation,http://porikli.com/mysite/pdfs/porikli%202017%20-%20Deep%20salient%20object%20detection%20by%20integrating%20multi-level%20cues.pdf,Deep Salient Object Detection by Integrating Multi-level Cues,2017 -224,VOC,voc,-35.2776999,149.118527,Australian National University,edu,63660c50e2669a5115c2379e622549d8ed79be00,citation,http://porikli.com/mysite/pdfs/porikli%202017%20-%20Deep%20salient%20object%20detection%20by%20integrating%20multi-level%20cues.pdf,Deep Salient Object Detection by Integrating Multi-level Cues,2017 -225,VOC,voc,48.14955455,11.56775314,Technical University Munich,edu,472541ccd941b9b4c52e1f088cc1152de9b3430f,citation,https://arxiv.org/pdf/1612.00197.pdf,Learning in an Uncertain World: Representing Ambiguity Through Multiple Hypotheses,2017 -226,VOC,voc,47.3764534,8.54770931,ETH Zürich,edu,9184b0c04013bfdfd82f4f271b5f017396c2f085,citation,https://pdfs.semanticscholar.org/9184/b0c04013bfdfd82f4f271b5f017396c2f085.pdf,Semantic Segmentation for Line Drawing Vectorization Using Neural Networks,2018 -227,VOC,voc,37.4102193,-122.05965487,Carnegie Mellon University,edu,57488aa24092fa7118aa5374c90b282a32473cf9,citation,https://arxiv.org/pdf/1807.01257.pdf,A Weakly Supervised Adaptive DenseNet for Classifying Thoracic Diseases and Identifying Abnormalities,2018 -228,VOC,voc,39.9492344,-75.19198985,University of Pennsylvania,edu,57488aa24092fa7118aa5374c90b282a32473cf9,citation,https://arxiv.org/pdf/1807.01257.pdf,A Weakly Supervised Adaptive DenseNet for Classifying Thoracic Diseases and Identifying Abnormalities,2018 -229,VOC,voc,32.0565957,118.77408833,Nanjing University,edu,7771807cd05f78a4591f2d0b094ddd3e0bd5339a,citation,https://arxiv.org/pdf/1707.06399.pdf,Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors,2017 -230,VOC,voc,50.7944026,-1.0971748,Cambridge University,edu,4558338873556d01fd290de6ddc55721c633a1ad,citation,http://pdfs.semanticscholar.org/4558/338873556d01fd290de6ddc55721c633a1ad.pdf,Training Constrained Deconvolutional Networks for Road Scene Semantic Segmentation,2016 -231,VOC,voc,42.3583961,-71.09567788,MIT,edu,85957b49896246bb416c0a182e52b355a8fa40b4,citation,https://arxiv.org/pdf/1806.03510.pdf,Feature Pyramid Network for Multi-Class Land Segmentation,2018 -232,VOC,voc,17.4454957,78.34854698,International Institute of Information Technology,edu,f5eb411217f729ad7ae84bfd4aeb3dedb850206a,citation,https://pdfs.semanticscholar.org/f5eb/411217f729ad7ae84bfd4aeb3dedb850206a.pdf,Tackling Low Resolution for Better Scene Understanding,2018 -233,VOC,voc,53.8338371,10.7035939,Institute of Systems and Robotics,edu,7fb8d9c36c23f274f2dd84945dd32ec2cc143de1,citation,http://pdfs.semanticscholar.org/8e44/ba779d7cdc23d597c2c6e4420129834e7e21.pdf,Semantic Segmentation with Second-Order Pooling,2012 -234,VOC,voc,50.7338124,7.1022465,University of Bonn,edu,7fb8d9c36c23f274f2dd84945dd32ec2cc143de1,citation,http://pdfs.semanticscholar.org/8e44/ba779d7cdc23d597c2c6e4420129834e7e21.pdf,Semantic Segmentation with Second-Order Pooling,2012 -235,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,b5e3beb791cc17cdaf131d5cca6ceb796226d832,citation,http://pdfs.semanticscholar.org/b5e3/beb791cc17cdaf131d5cca6ceb796226d832.pdf,Novel Dataset for Fine-Grained Image Categorization: Stanford Dogs,2012 -236,VOC,voc,39.94976005,116.33629046,Beijing Jiaotong University,edu,b5968e7bb23f5f03213178c22fd2e47af3afa04c,citation,https://arxiv.org/pdf/1705.07206.pdf,Multiple-Human Parsing in the Wild,2017 -237,VOC,voc,1.2962018,103.77689944,National University of Singapore,edu,b5968e7bb23f5f03213178c22fd2e47af3afa04c,citation,https://arxiv.org/pdf/1705.07206.pdf,Multiple-Human Parsing in the Wild,2017 -238,VOC,voc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,532c089b43983935e1001c5e35aa35440263beaf,citation,https://arxiv.org/pdf/1804.03166.pdf,G-Distillation: Reducing Overconfident Errors on Novel Samples,2018 -239,VOC,voc,33.776033,-84.39884086,Georgia Institute of Technology,edu,35fc0b28d0d674b28dd625d170bc641a36b17318,citation,http://pdfs.semanticscholar.org/35fc/0b28d0d674b28dd625d170bc641a36b17318.pdf,CSI: Composite Statistical Inference Techniques for Semantic Segmentation,2013 -240,VOC,voc,55.7039571,13.1902011,Lund University,edu,35fc0b28d0d674b28dd625d170bc641a36b17318,citation,http://pdfs.semanticscholar.org/35fc/0b28d0d674b28dd625d170bc641a36b17318.pdf,CSI: Composite Statistical Inference Techniques for Semantic Segmentation,2013 -241,VOC,voc,58.38131405,26.72078081,University of Tartu,edu,e4cb27d2a3e1153cb517d97d61de48ff0483c988,citation,https://pdfs.semanticscholar.org/e4cb/27d2a3e1153cb517d97d61de48ff0483c988.pdf,Viktoria Plemakova Vehicle Detection Based on Convolutional Neural Networks,2018 -242,VOC,voc,33.776033,-84.39884086,Georgia Institute of Technology,edu,3d0660e18c17db305b9764bb86b21a429241309e,citation,https://arxiv.org/pdf/1604.03505.pdf,Counting Everyday Objects in Everyday Scenes,2017 -243,VOC,voc,37.2381023,127.1903431,Myongji University,edu,a67da2dd79c01e8cc4029ecc5a05b97967403862,citation,https://pdfs.semanticscholar.org/a67d/a2dd79c01e8cc4029ecc5a05b97967403862.pdf,On Selecting Helpful Unlabeled Data for Improving Semi-Supervised Support Vector Machines,2014 -244,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,4ab69672e1116427d685bf7c1edb5b1fd0573b5e,citation,http://bigml.cs.tsinghua.edu.cn/~lingxi/PDFs/Xie_ACMMM12_EdgeGPP.pdf,Spatial pooling of heterogeneous features for image applications,2012 -245,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,989c7cdafa9b90ab2ea0a9d8fa60634cc698f174,citation,http://pdfs.semanticscholar.org/989c/7cdafa9b90ab2ea0a9d8fa60634cc698f174.pdf,YoloFlow Real - time Object Tracking in Video CS 229 Course Project,2016 -246,VOC,voc,3.12267405,101.65356103,University of Malaya,edu,85af6c005df806b57b306a732dcb98e096d15bfb,citation,https://arxiv.org/pdf/1805.11227.pdf,Getting to Know Low-light Images with The Exclusively Dark Dataset,2018 -247,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,cdb293381ff396d6e9c0f5e9578d411e759347fd,citation,https://pdfs.semanticscholar.org/022e/eae0edc09deb228da26d5390874f781ace0f.pdf,3 DR 2 N 2 : A Unified Approach for Single and Multiview 3 D Object Reconstruction,2016 -248,VOC,voc,51.7534538,-1.25400997,University of Oxford,edu,0e67717484684d90ae9d4e1bb9cdceb74b194910,citation,http://pdfs.semanticscholar.org/0e67/717484684d90ae9d4e1bb9cdceb74b194910.pdf,Mining Pixels: Weakly Supervised Semantic Segmentation Using Image Labels,2016 -249,VOC,voc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,5b4b84ce3518c8a14f57f5f95a1d07fb60e58223,citation,https://pdfs.semanticscholar.org/9f92/05a60ddf1135929e0747db34363b3a8c6bc8.pdf,Diagnosing Error in Object Detectors,2012 -250,VOC,voc,42.718568,-84.47791571,Michigan State University,edu,47203943c86e4d9355ffd99cd3d75f37211fd805,citation,http://pdfs.semanticscholar.org/be18/9c7066c4d99d617d137c975139c594ad09af.pdf,Semi-Crowdsourced Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning,2012 -251,VOC,voc,42.8298248,-73.87719385,GE Global Research Center,edu,47203943c86e4d9355ffd99cd3d75f37211fd805,citation,http://pdfs.semanticscholar.org/be18/9c7066c4d99d617d137c975139c594ad09af.pdf,Semi-Crowdsourced Clustering: Generalizing Crowd Labeling by Robust Distance Metric Learning,2012 -252,VOC,voc,39.95472495,-75.15346905,Temple University,edu,45ff38add61df32a027048624f58952a67a7c5f5,citation,http://pdfs.semanticscholar.org/45ff/38add61df32a027048624f58952a67a7c5f5.pdf,Deep Context Convolutional Neural Networks for Semantic Segmentation,2017 -253,VOC,voc,43.08250655,-77.67121663,Rochester Institute of Technology,edu,0a789733ccb300d0dd9df6174faaa7e8c64e0409,citation,http://pdfs.semanticscholar.org/0a78/9733ccb300d0dd9df6174faaa7e8c64e0409.pdf,High-Resolution Multispectral Dataset for Semantic Segmentation,2017 -254,VOC,voc,47.05821,15.46019568,Graz University of Technology,edu,9d3a6e459e0cecda20a8afd69d182877ff0224cf,citation,http://pdfs.semanticscholar.org/9d3a/6e459e0cecda20a8afd69d182877ff0224cf.pdf,A Framework for Articulated Hand Pose Estimation and Evaluation,2015 -255,VOC,voc,52.3553655,4.9501644,University of Amsterdam,edu,943a1e218b917172199e524944006aa349f58968,citation,https://arxiv.org/pdf/1807.11857.pdf,Joint Learning of Intrinsic Images and Semantic Segmentation,2018 -256,VOC,voc,40.0044795,116.370238,Chinese Academy of Sciences,edu,5f68e2131d9275d56092e9fca05bcfc65abea0d8,citation,http://doi.acm.org/10.1145/2806416.2806469,Cross-Modal Similarity Learning: A Low Rank Bilinear Formulation,2015 -257,VOC,voc,40.9153196,-73.1270626,Stony Brook University,edu,f989a20fbcc2d576c0c4514a0e5085c741580778,citation,https://arxiv.org/pdf/1612.03236.pdf,Co-localization with Category-Consistent Features and Geodesic Distance Propagation,2017 -258,VOC,voc,42.36782045,-71.12666653,Harvard University,edu,f989a20fbcc2d576c0c4514a0e5085c741580778,citation,https://arxiv.org/pdf/1612.03236.pdf,Co-localization with Category-Consistent Features and Geodesic Distance Propagation,2017 -259,VOC,voc,24.7925484,120.9951183,National Tsing Hua University,edu,cf94200a476dc15d6da95db809349db4cfd8e92c,citation,https://arxiv.org/pdf/1807.11436.pdf,Leveraging Motion Priors in Videos for Improving Human Segmentation,2018 -260,VOC,voc,-34.9189226,138.60423668,University of Adelaide,edu,25dba68e4db0ce361032126b91f734f9252cae7c,citation,https://arxiv.org/pdf/1611.08998.pdf,DeepSetNet: Predicting Sets with Deep Neural Networks,2017 -261,VOC,voc,59.34986645,18.07063213,"KTH Royal Institute of Technology, Stockholm",edu,883767948f535ea2bf8a0c03047ca9064e1b078f,citation,https://pdfs.semanticscholar.org/8837/67948f535ea2bf8a0c03047ca9064e1b078f.pdf,A Combination of Object Recognition and Localisation for an Autonomous Racecar,0 -262,VOC,voc,23.09461185,113.28788994,Sun Yat-Sen University,edu,18095a530b532a70f3b615fef2f59e6fdacb2d84,citation,https://arxiv.org/pdf/1604.02271v3.pdf,Deep Structured Scene Parsing by Learning with Image Descriptions,2016 -263,VOC,voc,45.7413921,126.62552755,Harbin Institute of Technology,edu,18095a530b532a70f3b615fef2f59e6fdacb2d84,citation,https://arxiv.org/pdf/1604.02271v3.pdf,Deep Structured Scene Parsing by Learning with Image Descriptions,2016 -264,VOC,voc,-27.47715625,153.02841004,Queensland University of Technology,edu,9397e7acd062245d37350f5c05faf56e9cfae0d6,citation,http://pdfs.semanticscholar.org/9397/e7acd062245d37350f5c05faf56e9cfae0d6.pdf,DeepFruits: A Fruit Detection System Using Deep Neural Networks,2016 -265,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,03a24d15533dae78de78fd9d5f6c9050fb97f186,citation,https://doi.org/10.1109/SSCI.2016.7850112,Pedestrian detection aided by scale-discriminative network,2016 -266,VOC,voc,-33.88890695,151.18943366,University of Sydney,edu,17d4fd92352baf6f0039ec64d43ca572c8252384,citation,https://arxiv.org/pdf/1806.07049.pdf,MoE-SPNet: A mixture-of-experts scene parsing network,2018 -267,VOC,voc,47.05821,15.46019568,Graz University of Technology,edu,30a29f6c407749e97bc7c2db5674a62773af9d27,citation,http://pdfs.semanticscholar.org/30a2/9f6c407749e97bc7c2db5674a62773af9d27.pdf,Tracking and Visual Quality Inspection in Harsh Environments (print-version),2012 -268,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,280d632ef3234c5ab06018c6eaccead75bc173b3,citation,http://pdfs.semanticscholar.org/6b1a/c8e438041ac02cc8fab5762ca069c386f473.pdf,Efficient Image and Video Co-localization with Frank-Wolfe Algorithm,2014 -269,VOC,voc,31.83907195,117.26420748,University of Science and Technology of China,edu,0f945f796a9343b51a3dc69941c0fa1a98c0f448,citation,http://pdfs.semanticscholar.org/a7ef/979ce52b9e4bcbd6ee5524dfd4e92baf6292.pdf,Local Hypersphere Coding Based on Edges between Visual Words,2012 -270,VOC,voc,-34.9189226,138.60423668,University of Adelaide,edu,0db6a58927a671c01089c53248b0e1c36bdc3231,citation,http://openaccess.thecvf.com/content_cvpr_2016/papers/Pham_Efficient_Point_Process_CVPR_2016_paper.pdf,Efficient Point Process Inference for Large-Scale Object Detection,2016 -271,VOC,voc,42.2942142,-83.71003894,University of Michigan,edu,14d0afea52c4e9b7a488f6398e4a92bd4f4b93c7,citation,https://arxiv.org/pdf/1804.07667.pdf,Rethinking the Faster R-CNN Architecture for Temporal Action Localization,2018 -272,VOC,voc,42.2942142,-83.71003894,University of Michigan,edu,8da1b0834688edb311a803532e33939e9ecf8292,citation,https://arxiv.org/pdf/1808.01244.pdf,CornerNet: Detecting Objects as Paired Keypoints,2018 -273,VOC,voc,39.2899685,-76.62196103,University of Maryland,edu,f42d3225afd9e463ddb7a355f64b54af8bd14227,citation,https://arxiv.org/pdf/1804.10343.pdf,Stacked U-Nets: A No-Frills Approach to Natural Image Segmentation,2018 -274,VOC,voc,31.83907195,117.26420748,University of Science and Technology of China,edu,a1dd88f44d045b360569a9a8721f728afbd951c3,citation,https://pdfs.semanticscholar.org/a1dd/88f44d045b360569a9a8721f728afbd951c3.pdf,Relief Impression Image Detection : Unsupervised Extracting Objects Directly From Feature Arrangements of Deep CNN,2016 -275,VOC,voc,34.0687788,-118.4450094,"University of California, Los Angeles",edu,fc027fccb19512a439fc17181c34ee1c3aad51b5,citation,https://arxiv.org/pdf/1708.03383.pdf,Joint Multi-person Pose Estimation and Semantic Part Segmentation,2017 -276,VOC,voc,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 -277,VOC,voc,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 -278,VOC,voc,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,451eed7fd8ae281d1cc76ca8cdecbaf47816e55a,citation,http://pdfs.semanticscholar.org/451e/ed7fd8ae281d1cc76ca8cdecbaf47816e55a.pdf,Close Yet Distinctive Domain Adaptation,2017 -279,VOC,voc,35.9990522,-78.9290629,Duke University,edu,992b93ab9d016640551a8cebcaf4757288154f32,citation,http://pdfs.semanticscholar.org/e38c/f96363aaf1f17c487c484ad27d3175ca4b31.pdf,Nested Pictorial Structures,2012 -280,VOC,voc,43.08250655,-77.67121663,Rochester Institute of Technology,edu,7489990ea3d6ab4c1c86c9ed9f049399961dfaef,citation,https://people.rit.edu/ndcsma/pubs/WNYISPW_Nov_2014_Chew.pdf,Normalized cutswith soft must-link constraints for image segmentation and clustering,2014 -281,VOC,voc,59.34986645,18.07063213,"KTH Royal Institute of Technology, Stockholm",edu,41199678ad9370ff8ca7e9e3c2617b62a297fac3,citation,http://pdfs.semanticscholar.org/4119/9678ad9370ff8ca7e9e3c2617b62a297fac3.pdf,Multitask Deep Learning models for real-time deployment in embedded systems,2017 -282,VOC,voc,39.7487516,30.47653071,Eskisehir Osmangazi University,edu,7fb74f5abab4830e3cdaf477230e5571d9e3ca57,citation,http://openaccess.thecvf.com/content_cvpr_2017/papers/Cevikalp_Polyhedral_Conic_Classifiers_CVPR_2017_paper.pdf,Polyhedral Conic Classifiers for Visual Object Detection and Classification,2017 -283,VOC,voc,33.776033,-84.39884086,Georgia Institute of Technology,edu,10793d1475607929fedc6d9a677911ad16843e58,citation,http://openaccess.thecvf.com/content_cvpr_2016/papers/Li_Unsupervised_Learning_of_CVPR_2016_paper.pdf,Unsupervised Learning of Edges,2016 -284,VOC,voc,31.30104395,121.50045497,Fudan University,edu,c94fd258a8f1e8f4033a7fe491f1372dcf7d3cd6,citation,https://arxiv.org/pdf/1807.04897.pdf,TS ^2 2 C: Tight Box Mining with Surrounding Segmentation Context for Weakly Supervised Object Detection,2018 -285,VOC,voc,1.2962018,103.77689944,National University of Singapore,edu,c94fd258a8f1e8f4033a7fe491f1372dcf7d3cd6,citation,https://arxiv.org/pdf/1807.04897.pdf,TS ^2 2 C: Tight Box Mining with Surrounding Segmentation Context for Weakly Supervised Object Detection,2018 -286,VOC,voc,52.5180641,13.3250425,TU Berlin,edu,2581a12189eb1a0b5b27a7fd1c2cbe44c88fcc20,citation,http://arxiv.org/pdf/1512.00172v1.pdf,Analyzing Classifiers: Fisher Vectors and Deep Neural Networks,2016 -287,VOC,voc,32.0565957,118.77408833,Nanjing University,edu,96416b1b44fb05302c6e9a8ab1b74d9204995e73,citation,http://pdfs.semanticscholar.org/9641/6b1b44fb05302c6e9a8ab1b74d9204995e73.pdf,Learning Effective Binary Visual Representations with Deep Networks,2018 -288,VOC,voc,42.3619407,-71.0904378,MIT CSAIL,edu,aa2ddae22760249729ac2c2c4e24c8b665bcd40e,citation,https://pdfs.semanticscholar.org/8c47/635ae7f1641c2bdd45026ad7dbff70c24398.pdf,Interpretable Basis Decomposition for Visual Explanation,2018 -289,VOC,voc,42.2942142,-83.71003894,University of Michigan,edu,60542b1a857024c79db8b5b03db6e79f74ec8f9f,citation,https://arxiv.org/pdf/1702.05448.pdf,Learning to Detect Human-Object Interactions,2018 -290,VOC,voc,36.3693473,120.673818,Shandong University,edu,bd8a85acaa45d4068fca584e8d9e3bd3bb4eea4d,citation,http://pdfs.semanticscholar.org/bd8a/85acaa45d4068fca584e8d9e3bd3bb4eea4d.pdf,Toward Scene Recognition by Discovering Semantic Structures and Parts,2015 -291,VOC,voc,49.2767454,-122.91777375,Simon Fraser University,edu,bd8a85acaa45d4068fca584e8d9e3bd3bb4eea4d,citation,http://pdfs.semanticscholar.org/bd8a/85acaa45d4068fca584e8d9e3bd3bb4eea4d.pdf,Toward Scene Recognition by Discovering Semantic Structures and Parts,2015 -292,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,456abee9c8d31f004b2f0a3b47222043e20f5042,citation,https://arxiv.org/pdf/1603.09188.pdf,Unsupervised Visual Sense Disambiguation for Verbs using Multimodal Embeddings,2016 -293,VOC,voc,31.83907195,117.26420748,University of Science and Technology of China,edu,7c2f6424b0bb2c28f282fbc0b4e98bf85d5584eb,citation,http://pdfs.semanticscholar.org/a5ae/7d662ed086bc5b0c9a2c1dc54fcb23635000.pdf,Relief R-CNN: Utilizing Convolutional Feature Interrelationship for Fast Object Detection Deployment,2016 -294,VOC,voc,22.53521465,113.9315911,Shenzhen University,edu,7c2f6424b0bb2c28f282fbc0b4e98bf85d5584eb,citation,http://pdfs.semanticscholar.org/a5ae/7d662ed086bc5b0c9a2c1dc54fcb23635000.pdf,Relief R-CNN: Utilizing Convolutional Feature Interrelationship for Fast Object Detection Deployment,2016 -295,VOC,voc,37.5557271,127.0436642,Hanyang University,edu,59e9934720baf3c5df3a0e1e988202856e1f83ce,citation,https://arxiv.org/pdf/1511.04136.pdf,UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking,2015 -296,VOC,voc,40.0141905,-83.0309143,University of Electronic Science and Technology of China,edu,d58c44bd9b464d9ac1db1344445c31364925f75a,citation,https://pdfs.semanticscholar.org/d58c/44bd9b464d9ac1db1344445c31364925f75a.pdf,TBN: Convolutional Neural Network with Ternary Inputs and Binary Weights,2018 -297,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,81ba5202424906f64b77f68afca063658139fbb2,citation,https://arxiv.org/pdf/1611.09078.pdf,Social Scene Understanding: End-to-End Multi-person Action Localization and Collective Activity Recognition,2017 -298,VOC,voc,46.109237,7.08453549,IDIAP Research Institute,edu,81ba5202424906f64b77f68afca063658139fbb2,citation,https://arxiv.org/pdf/1611.09078.pdf,Social Scene Understanding: End-to-End Multi-person Action Localization and Collective Activity Recognition,2017 -299,VOC,voc,50.7338124,7.1022465,University of Bonn,edu,0b6f64c78c44dc043e2972fa7bfe2a5753768609,citation,https://doi.org/10.1109/ICPR.2016.7900008,A future for learning semantic models of man-made environments,2016 -300,VOC,voc,30.5097537,114.4062881,Huazhong University of Science and Technology,edu,016eb7b32d1fdec0899151fb03799378bf59bbe5,citation,http://pdfs.semanticscholar.org/016e/b7b32d1fdec0899151fb03799378bf59bbe5.pdf,Point Linking Network for Object Detection,2017 -301,VOC,voc,33.9928298,-81.02685168,University of South Carolina,edu,cd9d654c6a4250e0cf8bcfddc2afab9e70ee6cae,citation,http://pdfs.semanticscholar.org/cd9d/654c6a4250e0cf8bcfddc2afab9e70ee6cae.pdf,Object Detection with Mask-based Feature Encoding,2018 -302,VOC,voc,36.20304395,117.05842113,Tianjin University,edu,cd9d654c6a4250e0cf8bcfddc2afab9e70ee6cae,citation,http://pdfs.semanticscholar.org/cd9d/654c6a4250e0cf8bcfddc2afab9e70ee6cae.pdf,Object Detection with Mask-based Feature Encoding,2018 -303,VOC,voc,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,28737575297a20d431dd2b777a79a8be2c9c2bbd,citation,http://pdfs.semanticscholar.org/2873/7575297a20d431dd2b777a79a8be2c9c2bbd.pdf,Object Ranking on Deformable Part Models with Bagged LambdaMART,2014 -304,VOC,voc,40.11116745,-88.22587665,"University of Illinois, Urbana-Champaign",edu,46702e0127e16a4d6a1feda3ffc5f0f123957e87,citation,https://arxiv.org/pdf/1809.06131.pdf,Revisit Multinomial Logistic Regression in Deep Learning: Data Dependent Model Initialization for Image Recognition,2018 -305,VOC,voc,34.0687788,-118.4450094,"University of California, Los Angeles",edu,d2b2cb1d5cc1aa30cf5be7bcb0494198934caabb,citation,http://pdfs.semanticscholar.org/d2b2/cb1d5cc1aa30cf5be7bcb0494198934caabb.pdf,A Restricted Visual Turing Test for Deep Scene and Event Understanding,2015 -306,VOC,voc,37.8687126,-122.25586815,"University of California, Berkeley",edu,446fbff6a2a7c9989b0a0465f960e236d9a5e886,citation,http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Pathak_Context_Encoders_Feature_CVPR_2016_paper.pdf,Context Encoders: Feature Learning by Inpainting,2016 -307,VOC,voc,51.49887085,-0.17560797,Imperial College London,edu,291e5377df2eec4835b5c6889896941831a11c69,citation,http://pdfs.semanticscholar.org/291e/5377df2eec4835b5c6889896941831a11c69.pdf,Recovering 6D Object Pose: Multi-modal Analyses on Challenges,2017 -308,VOC,voc,40.9153196,-73.1270626,Stony Brook University,edu,b69fbf046faf685655b5fa52fef07fb77e75eff4,citation,http://pdfs.semanticscholar.org/b69f/bf046faf685655b5fa52fef07fb77e75eff4.pdf,Modeling guidance and recognition in categorical search: bridging human and computer object detection.,2013 -309,VOC,voc,39.7487516,30.47653071,Eskisehir Osmangazi University,edu,13bda03fc8984d5943ed8d02e49a779d27c84114,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2012.6248047,Efficient object detection using cascades of nearest convex model classifiers,2012 -310,VOC,voc,50.7338124,7.1022465,University of Bonn,edu,87a66ccc68374ffb704ee6fb9fa7df369718095c,citation,http://pdfs.semanticscholar.org/ea90/16fb585ba6449d3d6f98bf85fa0bcd1f4621.pdf,Multi-person Pose Estimation with Local Joint-to-Person Associations,2016 -311,VOC,voc,39.9922379,116.30393816,Peking University,edu,4960ab1cef23e5ccd60173725ea280f462164a0e,citation,https://pdfs.semanticscholar.org/4960/ab1cef23e5ccd60173725ea280f462164a0e.pdf,Video Object Segmentation by Learning Location-Sensitive Embeddings,2018 -312,VOC,voc,39.977217,116.337632,Microsoft Research Asia,company,4960ab1cef23e5ccd60173725ea280f462164a0e,citation,https://pdfs.semanticscholar.org/4960/ab1cef23e5ccd60173725ea280f462164a0e.pdf,Video Object Segmentation by Learning Location-Sensitive Embeddings,2018 -313,VOC,voc,35.9990522,-78.9290629,Duke University,edu,8856fbf333b2aba7b9f1f746e16a2b7f083ee5b8,citation,http://pdfs.semanticscholar.org/8856/fbf333b2aba7b9f1f746e16a2b7f083ee5b8.pdf,Analyzing animal behavior via classifying each video frame using convolutional neural networks,2015 -314,VOC,voc,34.1235825,108.83546,Xidian University,edu,f9f01af981f8d25f0c96ea06d88be62dabb79256,citation,https://pdfs.semanticscholar.org/f9f0/1af981f8d25f0c96ea06d88be62dabb79256.pdf,Terahertz Image Detection with the Improved Faster Region-Based Convolutional Neural Network,2018 -315,VOC,voc,37.5600406,126.9369248,Yonsei University,edu,09066d7d0bb6273bf996c8538d7b34c38ea6a500,citation,https://arxiv.org/pdf/1809.01845.pdf,"Yes, IoU loss is submodular - as a function of the mispredictions",2018 -316,VOC,voc,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,4aeebd1c9b4b936ed2e4d988d8d28e27f129e6f1,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Chiu_See_the_Difference_ICCV_2015_paper.pdf,See the Difference: Direct Pre-Image Reconstruction and Pose Estimation by Differentiating HOG,2015 -317,VOC,voc,34.0687788,-118.4450094,"University of California, Los Angeles",edu,232ff2dab49cb5a1dae1012fd7ba53382909ec18,citation,http://pdfs.semanticscholar.org/232f/f2dab49cb5a1dae1012fd7ba53382909ec18.pdf,Semantic Video Segmentation from Occlusion Relations within a Convex Optimization Framework,2013 -318,VOC,voc,50.13053055,8.69234224,University of Frankfurt,edu,465c34c3334f29de28f973b7702a235509649429,citation,http://pdfs.semanticscholar.org/465c/34c3334f29de28f973b7702a235509649429.pdf,Stereopsis via deep learning,2013 -319,VOC,voc,47.6543238,-122.30800894,University of Washington,edu,caa2ded6d8d5de97c824d29b0c7a18d220c596c8,citation,https://arxiv.org/pdf/1709.02554.pdf,Learning to Segment Breast Biopsy Whole Slide Images,2018 -320,VOC,voc,44.48116865,-73.2002179,University of Vermont,edu,caa2ded6d8d5de97c824d29b0c7a18d220c596c8,citation,https://arxiv.org/pdf/1709.02554.pdf,Learning to Segment Breast Biopsy Whole Slide Images,2018 -321,VOC,voc,42.2942142,-83.71003894,University of Michigan,edu,289d833a35c2156b7e332e67d1cb099fd0683025,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Chao_HICO_A_Benchmark_ICCV_2015_paper.pdf,HICO: A Benchmark for Recognizing Human-Object Interactions in Images,2015 -322,VOC,voc,37.8687126,-122.25586815,"University of California, Berkeley",edu,0fbdd4b8eb9e4c4cfbe5b76ab29ab8b0219fbdc0,citation,https://people.eecs.berkeley.edu/~pathak/papers/iccv15.pdf,Constrained Convolutional Neural Networks for Weakly Supervised Segmentation,2015 -323,VOC,voc,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,f94f79168c1cfaebb8eab5151e01d56478ab0b73,citation,http://pdfs.semanticscholar.org/f94f/79168c1cfaebb8eab5151e01d56478ab0b73.pdf,Optimizing Region Selection for Weakly Supervised Object Detection,2017 -324,VOC,voc,40.0044795,116.370238,Chinese Academy of Sciences,edu,6bb51f431f348b2b3e1db859827e80f97a576c30,citation,http://pdfs.semanticscholar.org/6bb5/1f431f348b2b3e1db859827e80f97a576c30.pdf,Irregular Convolutional Neural Networks,2017 -325,VOC,voc,22.42031295,114.20788644,Chinese University of Hong Kong,edu,b78e611c32dc0daf762cfa93044558cdb545d857,citation,http://pdfs.semanticscholar.org/b78e/611c32dc0daf762cfa93044558cdb545d857.pdf,Temporal Action Detection with Structured Segment Networks Supplementary Materials,2017 -326,VOC,voc,48.14955455,11.56775314,Technical University Munich,edu,bc12715a1ddf1a540dab06bf3ac4f3a32a26b135,citation,http://pdfs.semanticscholar.org/bc12/715a1ddf1a540dab06bf3ac4f3a32a26b135.pdf,Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking,2017 -327,VOC,voc,-34.9189226,138.60423668,University of Adelaide,edu,bc12715a1ddf1a540dab06bf3ac4f3a32a26b135,citation,http://pdfs.semanticscholar.org/bc12/715a1ddf1a540dab06bf3ac4f3a32a26b135.pdf,Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking,2017 -328,VOC,voc,31.20081505,121.42840681,Shanghai Jiao Tong University,edu,4d1757aacbc49c74a5d4e53259c92ab0e47544da,citation,https://arxiv.org/pdf/1805.04310.pdf,Weakly and Semi Supervised Human Body Part Parsing via Pose-Guided Knowledge Transfer,2018 -329,VOC,voc,36.1112058,140.1055176,University of Tsukuba,edu,d392098688a999c70589c995bd4427c212eff69d,citation,http://pdfs.semanticscholar.org/d392/098688a999c70589c995bd4427c212eff69d.pdf,Object Repositioning Based on the Perspective in a Single Image,2014 -330,VOC,voc,22.42031295,114.20788644,Chinese University of Hong Kong,edu,1c1f21bf136fe2eec412e5f70fd918c27c5ccb0a,citation,http://pdfs.semanticscholar.org/1c1f/21bf136fe2eec412e5f70fd918c27c5ccb0a.pdf,Object Detection and Viewpoint Estimation with Auto-masking Neural Network,2014 -331,VOC,voc,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,1c1f21bf136fe2eec412e5f70fd918c27c5ccb0a,citation,http://pdfs.semanticscholar.org/1c1f/21bf136fe2eec412e5f70fd918c27c5ccb0a.pdf,Object Detection and Viewpoint Estimation with Auto-masking Neural Network,2014 -332,VOC,voc,51.49887085,-0.17560797,Imperial College London,edu,72e9acdd64e71fc2084acaf177aafaa2e075bd8c,citation,http://pdfs.semanticscholar.org/72e9/acdd64e71fc2084acaf177aafaa2e075bd8c.pdf,The 2017 Hands in the Million Challenge on 3D Hand Pose Estimation,2017 -333,VOC,voc,51.49887085,-0.17560797,Imperial College London,edu,0209389b8369aaa2a08830ac3b2036d4901ba1f1,citation,https://arxiv.org/pdf/1612.01202v2.pdf,DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild,2017 -334,VOC,voc,51.5231607,-0.1282037,University College London,edu,0209389b8369aaa2a08830ac3b2036d4901ba1f1,citation,https://arxiv.org/pdf/1612.01202v2.pdf,DenseReg: Fully Convolutional Dense Shape Regression In-the-Wild,2017 -335,VOC,voc,50.7338124,7.1022465,University of Bonn,edu,07b8a9a225b738c4074a50cf80ee5fe516878421,citation,https://arxiv.org/pdf/1807.09169.pdf,Convolutional Simplex Projection Network for Weakly Supervised Semantic Segmentation,2018 -336,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,1bd1645a629f1b612960ab9bba276afd4cf7c666,citation,http://arxiv.org/pdf/1506.04878.pdf,End-to-End People Detection in Crowded Scenes,2016 -337,VOC,voc,49.2579566,7.04577417,Max Planck Institute for Informatics,edu,1bd1645a629f1b612960ab9bba276afd4cf7c666,citation,http://arxiv.org/pdf/1506.04878.pdf,End-to-End People Detection in Crowded Scenes,2016 -338,VOC,voc,43.7776426,11.259765,University of Florence,edu,1bbe0371ca22c2fdb6e0d098049bbf6430324bdb,citation,http://doi.acm.org/10.1145/2906152,"Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval",2016 -339,VOC,voc,37.43131385,-122.16936535,Stanford University,edu,1bbe0371ca22c2fdb6e0d098049bbf6430324bdb,citation,http://doi.acm.org/10.1145/2906152,"Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval",2016 -340,VOC,voc,34.7275714,135.2371,Kobe University,edu,9954f7ee5288724184f9420e39cca9165efa6822,citation,http://www.me.cs.scitec.kobe-u.ac.jp/~takigu/pdf/2015/Th5_4.pdf,Estimation of object functions using deformable part model,2015 -341,VOC,voc,48.14955455,11.56775314,Technical University Munich,edu,e212b2bc41645fe467a73d004067fcf1ca77d87f,citation,http://pdfs.semanticscholar.org/e212/b2bc41645fe467a73d004067fcf1ca77d87f.pdf,Deep Active Contours,2016 -342,VOC,voc,55.94951105,-3.19534913,University of Edinburgh,edu,51c4ecf4539f56c4b1035b890f743b3a91dd758b,citation,http://arxiv.org/abs/1504.06434,Situational object boundary detection,2015 -343,VOC,voc,37.8687126,-122.25586815,"University of California, Berkeley",edu,007e86cb55f0ba0415a7764a1e9f9566c1e8784b,citation,http://pdfs.semanticscholar.org/2677/3023b17ba560bad6a679930710a9049abca5.pdf,Adversarial Feature Learning,2016 -344,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,54d97ea9a5f92761dddd148fb0e602c2293e7c16,citation,https://pdfs.semanticscholar.org/54d9/7ea9a5f92761dddd148fb0e602c2293e7c16.pdf,Associating Inter-image Salient Instances for Weakly Supervised Semantic Segmentation,2018 -345,VOC,voc,51.4879961,-3.17969747,Cardiff University,edu,54d97ea9a5f92761dddd148fb0e602c2293e7c16,citation,https://pdfs.semanticscholar.org/54d9/7ea9a5f92761dddd148fb0e602c2293e7c16.pdf,Associating Inter-image Salient Instances for Weakly Supervised Semantic Segmentation,2018 -346,VOC,voc,51.5231607,-0.1282037,University College London,edu,0e923b74fd41f73f57e22f66397feeea67e834f0,citation,http://pdfs.semanticscholar.org/0e92/3b74fd41f73f57e22f66397feeea67e834f0.pdf,Invariant encoding schemes for visual recognition,2012 -347,VOC,voc,34.0224149,-118.28634407,University of Southern California,edu,93cba94ff0ff96f865ce24ea01e9c006369d75ff,citation,https://arxiv.org/pdf/1803.03879.pdf,Knowledge Aided Consistency for Weakly Supervised Phrase Grounding,2018 -348,VOC,voc,35.704514,51.40972058,Amirkabir University of Technology,edu,24fc311970e097efc317c0f98d2df37b828bfbad,citation,https://arxiv.org/pdf/1709.08019v2.pdf,Semi-supervised hierarchical semantic object parsing,2017 -349,VOC,voc,37.4102193,-122.05965487,Carnegie Mellon University,edu,5c4d4fd37e8c80ae95c00973531f34a6d810ea3a,citation,https://arxiv.org/pdf/1603.09439.pdf,The Open World of Micro-Videos,2016 -350,VOC,voc,37.26728,126.9841151,Seoul National University,edu,71b973c87965e4086e75fd2379dd1bd8e3f8231e,citation,https://arxiv.org/pdf/1606.02393.pdf,Progressive Attention Networks for Visual Attribute Prediction,2018 -351,VOC,voc,37.4102193,-122.05965487,Carnegie Mellon University,edu,20c02e98602f6adf1cebaba075d45cef50de089f,citation,https://arxiv.org/pdf/1808.07507.pdf,Video Jigsaw: Unsupervised Learning of Spatiotemporal Context for Video Action Recognition,2018 -352,VOC,voc,33.776033,-84.39884086,Georgia Institute of Technology,edu,20c02e98602f6adf1cebaba075d45cef50de089f,citation,https://arxiv.org/pdf/1808.07507.pdf,Video Jigsaw: Unsupervised Learning of Spatiotemporal Context for Video Action Recognition,2018 -353,VOC,voc,47.6543238,-122.30800894,University of Washington,edu,c17ed26650a67e80151f5312fa15b5c423acc797,citation,http://pdfs.semanticscholar.org/c17e/d26650a67e80151f5312fa15b5c423acc797.pdf,Multiple-Kernel Based Vehicle Tracking Using 3D Deformable Model and Camera Self-Calibration,2017 -354,VOC,voc,36.05238585,140.11852361,Institute of Industrial Science,edu,c17ed26650a67e80151f5312fa15b5c423acc797,citation,http://pdfs.semanticscholar.org/c17e/d26650a67e80151f5312fa15b5c423acc797.pdf,Multiple-Kernel Based Vehicle Tracking Using 3D Deformable Model and Camera Self-Calibration,2017 -355,VOC,voc,35.9020448,139.93622009,University of Tokyo,edu,c17ed26650a67e80151f5312fa15b5c423acc797,citation,http://pdfs.semanticscholar.org/c17e/d26650a67e80151f5312fa15b5c423acc797.pdf,Multiple-Kernel Based Vehicle Tracking Using 3D Deformable Model and Camera Self-Calibration,2017 -356,VOC,voc,47.6423318,-122.1369302,Microsoft,company,c17ed26650a67e80151f5312fa15b5c423acc797,citation,http://pdfs.semanticscholar.org/c17e/d26650a67e80151f5312fa15b5c423acc797.pdf,Multiple-Kernel Based Vehicle Tracking Using 3D Deformable Model and Camera Self-Calibration,2017 -357,VOC,voc,31.21051105,29.91314562,Alexandria University,edu,0ce08f1cc6684495d12c2da157a056c7b88ffcd9,citation,http://pdfs.semanticscholar.org/0ce0/8f1cc6684495d12c2da157a056c7b88ffcd9.pdf,Multi-Modality Feature Transform: An Interactive Image Segmentation Approach,2015 -358,VOC,voc,1.3484104,103.68297965,Nanyang Technological University,edu,567078a51ea63b70396dca5dabb50a10a736d991,citation,https://pdfs.semanticscholar.org/1b5a/3bdb174df1ff36c1c101739d6daaec07760d.pdf,Conditional Generative Adversarial Network for Structured Domain Adaptation,2018 -359,VOC,voc,43.0008093,-78.7889697,University at Buffalo,edu,567078a51ea63b70396dca5dabb50a10a736d991,citation,https://pdfs.semanticscholar.org/1b5a/3bdb174df1ff36c1c101739d6daaec07760d.pdf,Conditional Generative Adversarial Network for Structured Domain Adaptation,2018 -360,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,6e4e5ef25f657de8fb383c8dfeb8e229eea28bb9,citation,https://arxiv.org/pdf/1707.01691.pdf,RON: Reverse Connection with Objectness Prior Networks for Object Detection,2017 -361,VOC,voc,50.0764296,14.41802312,Czech Technical University,edu,cf528f9fe6588b71efa94c219979ce111fc9c1c9,citation,http://pdfs.semanticscholar.org/cf52/8f9fe6588b71efa94c219979ce111fc9c1c9.pdf,On Evaluation of 6D Object Pose Estimation,2016 -362,VOC,voc,22.2081469,114.25964115,University of Hong Kong,edu,3b67645cd512898806aaf1df1811035f2d957f6b,citation,https://arxiv.org/pdf/1705.04043.pdf,SCNet: Learning Semantic Correspondence,2017 -363,VOC,voc,26.513188,80.23651945,Indian Institute of Technology Kanpur,edu,ef2e36daf429899bb48d80ce6804731c3f99bb85,citation,http://pdfs.semanticscholar.org/f7bd/b4df0fb5b3ff9fa0ebfe7c2a9ddc34c09a5c.pdf,"Debnath, Banerjee, Namboodiri: Adapting Ransac-svm to Detect Outliers for Robust Classification",2015 -364,VOC,voc,40.0044795,116.370238,Chinese Academy of Sciences,edu,79a3a07661b8c6a36070fd767344e15c847a30ef,citation,http://pdfs.semanticscholar.org/79a3/a07661b8c6a36070fd767344e15c847a30ef.pdf,Contextual Pooling in Image Classification,2012 -365,VOC,voc,13.0222347,77.56718325,Indian Institute of Science Bangalore,edu,5aa7f33cdc00787284b609aa63f5eb5c0a3212f6,citation,http://pdfs.semanticscholar.org/5aa7/f33cdc00787284b609aa63f5eb5c0a3212f6.pdf,Multiplicative mixing of object identity and image attributes in single inferior temporal neurons,2018 -366,VOC,voc,51.5247272,-0.03931035,Queen Mary University of London,edu,38f88655debf4bf32978a7b39fbd56aea6ee5752,citation,https://arxiv.org/pdf/1712.03162.pdf,Class Rectification Hard Mining for Imbalanced Deep Learning,2017 -367,VOC,voc,36.1244756,-97.05004383,Oklahoma State University,edu,7b3b2912c1d7a70839bc71a150e33f8634d0fff3,citation,https://pdfs.semanticscholar.org/7b3b/2912c1d7a70839bc71a150e33f8634d0fff3.pdf,Convolutional Neural Network-Based Embarrassing Situation Detection under Camera for Social Robot in Smart Homes,2018 -368,VOC,voc,40.00229045,116.32098908,Tsinghua University,edu,acdc333f7b32d987e65ce15f21db64e850ca9471,citation,https://pdfs.semanticscholar.org/acdc/333f7b32d987e65ce15f21db64e850ca9471.pdf,Direct Loss Minimization for Training Deep Neural Nets,2015 -369,VOC,voc,43.66333345,-79.39769975,University of Toronto,edu,acdc333f7b32d987e65ce15f21db64e850ca9471,citation,https://pdfs.semanticscholar.org/acdc/333f7b32d987e65ce15f21db64e850ca9471.pdf,Direct Loss Minimization for Training Deep Neural Nets,2015 -370,VOC,voc,28.2290209,112.99483204,"National University of Defense Technology, China",edu,da4137396f26bf3e76d04eeed0c94e11b7824aa6,citation,https://arxiv.org/pdf/1711.06828.pdf,Transferable Semi-Supervised Semantic Segmentation,2018 -371,VOC,voc,1.2962018,103.77689944,National University of Singapore,edu,da4137396f26bf3e76d04eeed0c94e11b7824aa6,citation,https://arxiv.org/pdf/1711.06828.pdf,Transferable Semi-Supervised Semantic Segmentation,2018 -372,VOC,voc,40.11571585,-88.22750772,Beckman Institute,edu,da4137396f26bf3e76d04eeed0c94e11b7824aa6,citation,https://arxiv.org/pdf/1711.06828.pdf,Transferable Semi-Supervised Semantic Segmentation,2018 -373,VOC,voc,40.9153196,-73.1270626,Stony Brook 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