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 University,edu,13719bbb4bb8bbe0cbcdad009243a926d93be433,citation,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w6/papers/Tian_Deep_LDA-Pruned_Nets_CVPR_2017_paper.pdf,Deep LDA-Pruned Nets for Efficient Facial Gender Classification,2017
58,Adience,adience,41.10427915,29.02231159,Istanbul Technical University,edu,fd53be2e0a9f33080a9db4b5a5e416e24ae8e198,citation,https://arxiv.org/pdf/1606.02909.pdf,Apparent Age Estimation Using Ensemble of Deep Learning Models,2016
59,Adience,adience,47.3804685,8.5430355,"Disney Research, Zurich",edu,017e94ad51c9be864b98c9b75582753ce6ee134f,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7892240,Rapid one-shot acquisition of dynamic VR avatars,2017
60,Adience,adience,34.1579742,-118.2894729,"Disney Research, UK",company,017e94ad51c9be864b98c9b75582753ce6ee134f,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7892240,Rapid one-shot acquisition of dynamic VR avatars,2017
61,Adience,adience,34.1619174,-118.2883702,Walt Disney Imagineering,company,017e94ad51c9be864b98c9b75582753ce6ee134f,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7892240,Rapid one-shot acquisition of dynamic VR avatars,2017
62,Adience,adience,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
63,Adience,adience,37.2830003,127.04548469,Ajou University,edu,24286ef164f0e12c3e9590ec7f636871ba253026,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8369721,Age and gender classification using wide convolutional neural network and Gabor filter,2018
64,Adience,adience,37.26728,126.9841151,Seoul National University,edu,24286ef164f0e12c3e9590ec7f636871ba253026,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8369721,Age and gender classification using wide convolutional neural network and Gabor filter,2018
65,Adience,adience,47.6543238,-122.30800894,University of Washington,edu,96e0cfcd81cdeb8282e29ef9ec9962b125f379b0,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.527,The MegaFace Benchmark: 1 Million Faces for Recognition at Scale,2016
66,Adience,adience,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
67,Adience,adience,23.0886214,-82.4481944,"Advanced Technologies Application Center, Havana, Cuba",edu,c5eba789aeb41904aa1b03fad1dc7cea5d0cd3b6,citation,https://doi.org/10.1109/BTAS.2017.8272773,Age and gender classification using local appearance descriptors from facial components,2017
68,Adience,adience,40.7240176,8.5578947,University of Sassari,edu,c5eba789aeb41904aa1b03fad1dc7cea5d0cd3b6,citation,https://doi.org/10.1109/BTAS.2017.8272773,Age and gender classification using local appearance descriptors from facial components,2017
69,Adience,adience,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
70,Adience,adience,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
71,Adience,adience,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
72,Adience,adience,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
73,Adience,adience,35.2742655,137.01327841,Chubu University,edu,5fb5d9389e2a2a4302c81bcfc068a4c8d4efe70c,citation,http://pdfs.semanticscholar.org/5fb5/d9389e2a2a4302c81bcfc068a4c8d4efe70c.pdf,Multiple Facial Attributes Estimation Based on Weighted Heterogeneous Learning,2016
74,Adience,adience,1.3484104,103.68297965,Nanyang Technological University,edu,d0471d5907d6557cf081edf4c7c2296c3c221a38,citation,https://pdfs.semanticscholar.org/d047/1d5907d6557cf081edf4c7c2296c3c221a38.pdf,A Constrained Deep Neural Network for Ordinal Regression,0
75,Adience,adience,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
76,Adience,adience,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
77,Adience,adience,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
78,Adience,adience,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
79,Adience,adience,32.77824165,34.99565673,Open University of Israel,edu,62e913431bcef5983955e9ca160b91bb19d9de42,citation,http://pdfs.semanticscholar.org/62e9/13431bcef5983955e9ca160b91bb19d9de42.pdf,Facial Landmark Detection with Tweaked Convolutional Neural Networks,2015
80,Adience,adience,34.67567405,33.04577648,Cyprus University of Technology,edu,9f3c9e41f46df9c94d714b1f080dafad6b4de1de,citation,https://doi.org/10.1109/ICT.2017.7998260,On the detection of images containing child-pornographic material,2017
81,Adience,adience,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
82,Adience,adience,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
83,Adience,adience,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
84,Adience,adience,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
85,Adience,adience,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
86,Adience,adience,34.2152538,117.1398541,China University of Mining and Technology,edu,bc6a7390135bf127b93b90a21b1fdebbfb56ad30,citation,https://doi.org/10.1109/TIFS.2017.2766039,Bimodal Vein Data Mining via Cross-Selected-Domain Knowledge Transfer,2018
87,Adience,adience,31.2284923,121.40211389,East China Normal University,edu,bc6a7390135bf127b93b90a21b1fdebbfb56ad30,citation,https://doi.org/10.1109/TIFS.2017.2766039,Bimodal Vein Data Mining via Cross-Selected-Domain Knowledge Transfer,2018
88,Adience,adience,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
89,Adience,adience,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
90,Adience,adience,36.3697191,127.362537,Korea Advanced Institute of Science and Technology,edu,2911e7f0fb6803851b0eddf8067a6fc06e8eadd6,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Jung_Joint_Fine-Tuning_in_ICCV_2015_paper.pdf,Joint Fine-Tuning in Deep Neural Networks for Facial Expression Recognition,2015
91,Adience,adience,23.7317915,90.3805625,Dhaka University,edu,026e4ee480475e63ae68570d73388f8dfd4b4cde,citation,http://pdfs.semanticscholar.org/026e/4ee480475e63ae68570d73388f8dfd4b4cde.pdf,Evaluating gender portrayal in Bangladeshi TV,2017
92,Adience,adience,40.0505672,-75.37109326,Eastern University,edu,026e4ee480475e63ae68570d73388f8dfd4b4cde,citation,http://pdfs.semanticscholar.org/026e/4ee480475e63ae68570d73388f8dfd4b4cde.pdf,Evaluating gender portrayal in Bangladeshi TV,2017
93,Adience,adience,42.3583961,-71.09567788,MIT,edu,026e4ee480475e63ae68570d73388f8dfd4b4cde,citation,http://pdfs.semanticscholar.org/026e/4ee480475e63ae68570d73388f8dfd4b4cde.pdf,Evaluating gender portrayal in Bangladeshi TV,2017
94,Adience,adience,-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
95,Adience,adience,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
96,Adience,adience,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
97,Adience,adience,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
98,Adience,adience,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
99,Adience,adience,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
100,Adience,adience,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