path: root/site/datasets/verified/adience.csv

id,country,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year
0,,Adience,adience,0.0,0.0,,,,main,,Age and Gender Estimation of Unfiltered Faces,2014
1,China,Adience,adience,39.993008,116.329882,SenseTime,company,c72a2ea819df9b0e8cd267eebcc6528b8741e03d,citation,https://arxiv.org/pdf/1708.09687.pdf,Quantifying Facial Age by Posterior of Age Comparisons,2017
2,China,Adience,adience,22.4162632,114.2109318,Chinese University of Hong Kong,edu,c72a2ea819df9b0e8cd267eebcc6528b8741e03d,citation,https://arxiv.org/pdf/1708.09687.pdf,Quantifying Facial Age by Posterior of Age Comparisons,2017
3,United States,Adience,adience,38.9869183,-76.9425543,"Maryland Univ., College Park, MD, USA",edu,59b6e9320a4e1de9216c6fc49b4b0309211b17e8,citation,https://pdfs.semanticscholar.org/59b6/e9320a4e1de9216c6fc49b4b0309211b17e8.pdf,Robust Representations for unconstrained Face Recognition and its Applications,2016
4,United Kingdom,Adience,adience,51.49887085,-0.17560797,Imperial College London,edu,d818568838433a6d6831adde49a58cef05e0c89f,citation,http://eprints.mdx.ac.uk/22044/1/agedb_kotsia.pdf,"AgeDB: The First Manually Collected, In-the-Wild Age Database",2017
5,China,Adience,adience,28.2290209,112.99483204,"National University of Defense Technology, China",mil,4f37f71517420c93c6841beb33ca0926354fa11d,citation,http://www.cs.newpaltz.edu/~lik/publications/Mingxing-Duan-NC-2017.pdf,A hybrid deep learning CNN-ELM for age and gender classification,2018
6,China,Adience,adience,26.88111275,112.62850666,Hunan University,edu,4f37f71517420c93c6841beb33ca0926354fa11d,citation,http://www.cs.newpaltz.edu/~lik/publications/Mingxing-Duan-NC-2017.pdf,A hybrid deep learning CNN-ELM for age and gender classification,2018
7,Italy,Adience,adience,46.0658836,11.1159894,University of Trento,edu,cb43519894258b125624dc0df655ab5357b1e42f,citation,https://arxiv.org/pdf/1802.00237.pdf,Face Aging with Contextual Generative Adversarial Nets,2017
8,China,Adience,adience,39.9041999,116.4073963,"Qihoo 360 AI Institute, Beijing, China",edu,cb43519894258b125624dc0df655ab5357b1e42f,citation,https://arxiv.org/pdf/1802.00237.pdf,Face Aging with Contextual Generative Adversarial Nets,2017
9,Singapore,Adience,adience,1.2962018,103.77689944,National University of Singapore,edu,cb43519894258b125624dc0df655ab5357b1e42f,citation,https://arxiv.org/pdf/1802.00237.pdf,Face Aging with Contextual Generative Adversarial Nets,2017
10,United States,Adience,adience,40.51865195,-74.44099801,State University of New Jersey,edu,d00e9a6339e34c613053d3b2c132fccbde547b56,citation,http://www.rci.rutgers.edu/~vmp93/Conference_pub/btas_age_2016_cameraready.pdf,A cascaded convolutional neural network for age estimation of unconstrained faces,2016
11,United States,Adience,adience,39.2899685,-76.62196103,University of Maryland,edu,d00e9a6339e34c613053d3b2c132fccbde547b56,citation,http://www.rci.rutgers.edu/~vmp93/Conference_pub/btas_age_2016_cameraready.pdf,A cascaded convolutional neural network for age estimation of unconstrained faces,2016
12,Bangladesh,Adience,adience,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
13,Bangladesh,Adience,adience,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
14,Canada,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
15,Egypt,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
16,Switzerland,Adience,adience,47.376313,8.5476699,ETH Zurich,edu,10195a163ab6348eef37213a46f60a3d87f289c5,citation,http://www.vision.ee.ethz.ch/en/publications/papers/articles/eth_biwi_01299.pdf,Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks,2016
17,United Kingdom,Adience,adience,51.49887085,-0.17560797,Imperial College London,edu,7f30a36a3faab044c095814d0ce17ea2b6638213,citation,https://arxiv.org/pdf/1802.04636.pdf,Modeling of facial aging and kinship: A survey,2018
18,United Kingdom,Adience,adience,51.59029705,-0.22963221,Middlesex University,edu,7f30a36a3faab044c095814d0ce17ea2b6638213,citation,https://arxiv.org/pdf/1802.04636.pdf,Modeling of facial aging and kinship: A survey,2018
19,United States,Adience,adience,40.9153196,-73.1270626,Stony Brook University,edu,25bf288b2d896f3c9dab7e7c3e9f9302e7d6806b,citation,https://arxiv.org/pdf/1608.06557.pdf,Neural Networks with Smooth Adaptive Activation Functions for Regression,2016
20,United States,Adience,adience,35.93006535,-84.31240032,Oak Ridge National Laboratory,edu,25bf288b2d896f3c9dab7e7c3e9f9302e7d6806b,citation,https://arxiv.org/pdf/1608.06557.pdf,Neural Networks with Smooth Adaptive Activation Functions for Regression,2016
21,Canada,Adience,adience,49.2767454,-122.91777375,Simon Fraser University,edu,880b4be9afc4d5ef75b5d77f51eadb557acbf251,citation,http://www.cs.umanitoba.ca/~ywang/papers/mmsp18.pdf,Privacy-Preserving Age Estimation for Content Rating,2018
22,Canada,Adience,adience,49.8091536,-97.13304179,University of Manitoba,edu,880b4be9afc4d5ef75b5d77f51eadb557acbf251,citation,http://www.cs.umanitoba.ca/~ywang/papers/mmsp18.pdf,Privacy-Preserving Age Estimation for Content Rating,2018
23,United States,Adience,adience,32.9820799,-96.7566278,University of Texas at Dallas,edu,e49d124a3d7eba42b0e3e79c1dd7537e6611602d,citation,https://arxiv.org/pdf/1803.05719.pdf,"SAF- BAGE: Salient Approach for Facial Soft-Biometric Classification - Age, Gender, and Facial Expression",2018
24,India,Adience,adience,23.0378743,72.55180046,Ahmedabad University,edu,e49d124a3d7eba42b0e3e79c1dd7537e6611602d,citation,https://arxiv.org/pdf/1803.05719.pdf,"SAF- BAGE: Salient Approach for Facial Soft-Biometric Classification - Age, Gender, and Facial Expression",2018
25,United States,Adience,adience,40.9153196,-73.1270626,Stony Brook University,edu,0ba402af3b8682e2aa89f76bd823ddffdf89fa0a,citation,https://arxiv.org/pdf/1611.05916.pdf,Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks,2016
26,United States,Adience,adience,42.36782045,-71.12666653,Harvard University,edu,0ba402af3b8682e2aa89f76bd823ddffdf89fa0a,citation,https://arxiv.org/pdf/1611.05916.pdf,Squared Earth Mover's Distance-based Loss for Training Deep Neural Networks,2016
27,United States,Adience,adience,39.2899685,-76.62196103,University of Maryland,edu,31f1e711fcf82c855f27396f181bf5e565a2f58d,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/papers/Ranjan_Unconstrained_Age_Estimation_ICCV_2015_paper.pdf,Unconstrained Age Estimation with Deep Convolutional Neural Networks,2015
28,United States,Adience,adience,40.47913175,-74.43168868,Rutgers University,edu,31f1e711fcf82c855f27396f181bf5e565a2f58d,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/papers/Ranjan_Unconstrained_Age_Estimation_ICCV_2015_paper.pdf,Unconstrained Age Estimation with Deep Convolutional Neural Networks,2015
29,Finland,Adience,adience,65.0592157,25.46632601,University of Oulu,edu,1fe121925668743762ce9f6e157081e087171f4c,citation,http://www.ee.oulu.fi/~jkannala/publications/cvprw2015.pdf,Unsupervised learning of overcomplete face descriptors,2015
30,United States,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
31,Italy,Adience,adience,45.47567215,9.23336232,Università degli Studi di Milano,edu,717ffde99c0d6b58675d44b4c66acedce0ca86e8,citation,https://air.unimi.it/retrieve/handle/2434/527428/913482/cisda17.pdf,Age estimation based on face images and pre-trained convolutional neural networks,2017
32,United States,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
33,United States,Adience,adience,47.00646895,-120.5367304,Central Washington University,edu,9d6e60d49e92361f8f558013065dfa67043dd337,citation,https://pdfs.semanticscholar.org/9d6e/60d49e92361f8f558013065dfa67043dd337.pdf,Applications of Computational Geometry and Computer Vision,2016
34,United States,Adience,adience,41.1664858,-73.1920564,University of Bridgeport,edu,0cece7b8989352e16a2fab8c0a0b1911c286906a,citation,https://pdfs.semanticscholar.org/0cec/e7b8989352e16a2fab8c0a0b1911c286906a.pdf,AUTOMATIC AGE ESTIMATION FROM REAL-WORLD AND WILD FACE IMAGES BY USING DEEP NEURAL NETWORKS,2017
35,United States,Adience,adience,47.00646895,-120.5367304,Central Washington University,edu,56c2fb2438f32529aec604e6fc3b06a595ddbfcc,citation,https://pdfs.semanticscholar.org/60dc/35a42ac758c5372c44f3791c951374658609.pdf,Comparison of Recent Machine Learning Techniques for Gender Recognition from Facial Images,2016
36,United States,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
37,United States,Adience,adience,42.357757,-83.06286711,Wayne State University,edu,4f1249369127cc2e2894f6b2f1052d399794919a,citation,http://www.cs.wayne.edu/~mdong/tmm18.pdf,Deep Age Estimation: From Classification to Ranking,2018
38,United States,Adience,adience,41.1664858,-73.1920564,University of Bridgeport,edu,ac9a331327cceda4e23f9873f387c9fd161fad76,citation,https://arxiv.org/pdf/1709.01664.pdf,Deep Convolutional Neural Network for Age Estimation based on VGG-Face Model,2017
39,Netherlands,Adience,adience,53.21967825,6.56251482,University of Groningen,edu,4ff4c27e47b0aa80d6383427642bb8ee9d01c0ac,citation,http://vigir.missouri.edu/~gdesouza/Research/Conference_CDs/IEEE_SSCI_2015/data/7560a188.pdf,Deep Convolutional Neural Networks and Support Vector Machines for Gender Recognition,2015
40,United Kingdom,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
41,Netherlands,Adience,adience,53.21967825,6.56251482,University of Groningen,edu,361c9ba853c7d69058ddc0f32cdbe94fbc2166d5,citation,https://pdfs.semanticscholar.org/361c/9ba853c7d69058ddc0f32cdbe94fbc2166d5.pdf,Deep Reinforcement Learning of Video Games,2017
42,South Korea,Adience,adience,37.26728,126.9841151,Seoul National University,edu,282503fa0285240ef42b5b4c74ae0590fe169211,citation,https://arxiv.org/pdf/1801.07848.pdf,Feeding Hand-Crafted Features for Enhancing the Performance of Convolutional Neural Networks,2018
43,Italy,Adience,adience,45.518383,9.213452,University of Milano-Bicocca,edu,305346d01298edeb5c6dc8b55679e8f60ba97efb,citation,https://pdfs.semanticscholar.org/3053/46d01298edeb5c6dc8b55679e8f60ba97efb.pdf,Fine-Grained Face Annotation Using Deep Multi-Task CNN,2018
44,Canada,Adience,adience,45.5039761,-73.5749687,McGill University,edu,a760d33a21d2ab338f59d32ac7f96023bbfaa248,citation,https://arxiv.org/pdf/1803.08134.pdf,Fisher Pruning of Deep Nets for Facial Trait Classification,2018
45,Taiwan,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
46,China,Adience,adience,25.055125,102.696888,Yunnan Normal University,edu,99c57ec53f2598d63c010f791adbca386b276919,citation,https://pdfs.semanticscholar.org/99c5/7ec53f2598d63c010f791adbca386b276919.pdf,Landmark-Guided Local Deep Neural Networks for Age and Gender Classification,2018
47,South Korea,Adience,adience,37.5600406,126.9369248,Yonsei University,edu,ba0d9e1e8bc798656429fe7121afee672dddb380,citation,https://arxiv.org/pdf/1809.01990.pdf,Multi-Expert Gender Classification on Age Group by Integrating Deep Neural Networks,2018
48,Canada,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
49,United States,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
50,Singapore,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
51,United States,Adience,adience,42.357757,-83.06286711,Wayne State University,edu,28d99dc2d673d62118658f8375b414e5192eac6f,citation,http://openaccess.thecvf.com/content_cvpr_2017/papers/Chen_Using_Ranking-CNN_for_CVPR_2017_paper.pdf,Using Ranking-CNN for Age Estimation,2017
52,Israel,Adience,adience,32.77824165,34.99565673,Open University of Israel,edu,2cbb4a2f8fd2ddac86f8804fd7ffacd830a66b58,citation,http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W08/papers/Levi_Age_and_Gender_2015_CVPR_paper.pdf,Age and gender classification using convolutional neural networks,2015
53,Australia,Adience,adience,-35.2776999,149.118527,Australian National University,edu,ac2e3a889fc46ca72f9a2cdedbdd6f3d4e9e2627,citation,https://pdfs.semanticscholar.org/ac2e/3a889fc46ca72f9a2cdedbdd6f3d4e9e2627.pdf,Age detection from a single image using multitask neural networks : An overview and design proposal,2016
54,Australia,Adience,adience,-35.2776999,149.118527,CSIRO,edu,ac2e3a889fc46ca72f9a2cdedbdd6f3d4e9e2627,citation,https://pdfs.semanticscholar.org/ac2e/3a889fc46ca72f9a2cdedbdd6f3d4e9e2627.pdf,Age detection from a single image using multitask neural networks : An overview and design proposal,2016
55,Australia,Adience,adience,-35.2776999,149.118527,"Data61, CSIRO, Canberra, Australia",edu,ac2e3a889fc46ca72f9a2cdedbdd6f3d4e9e2627,citation,https://pdfs.semanticscholar.org/ac2e/3a889fc46ca72f9a2cdedbdd6f3d4e9e2627.pdf,Age detection from a single image using multitask neural networks : An overview and design proposal,2016
56,China,Adience,adience,38.8760446,115.4973873,North China Electric Power University,edu,50ff21e595e0ebe51ae808a2da3b7940549f4035,citation,https://arxiv.org/pdf/1710.02985.pdf,Age Group and Gender Estimation in the Wild With Deep RoR Architecture,2017
57,United States,Adience,adience,38.9403808,-92.3277375,University of Missouri Columbia,edu,50ff21e595e0ebe51ae808a2da3b7940549f4035,citation,https://arxiv.org/pdf/1710.02985.pdf,Age Group and Gender Estimation in the Wild With Deep RoR Architecture,2017
58,China,Adience,adience,26.88111275,112.62850666,Hunan University,edu,ec0104286c96707f57df26b4f0a4f49b774c486b,citation,http://www.cs.newpaltz.edu/~lik/publications/Mingxing-Duan-IEEE-TIFS-2018.pdf,An Ensemble CNN2ELM for Age Estimation,2018
59,China,Adience,adience,28.2290209,112.99483204,"National University of Defense Technology, China",mil,ec0104286c96707f57df26b4f0a4f49b774c486b,citation,http://www.cs.newpaltz.edu/~lik/publications/Mingxing-Duan-IEEE-TIFS-2018.pdf,An Ensemble CNN2ELM for Age Estimation,2018
60,United States,Adience,adience,42.6480516,-73.749576,State University of New York,edu,ec0104286c96707f57df26b4f0a4f49b774c486b,citation,http://www.cs.newpaltz.edu/~lik/publications/Mingxing-Duan-IEEE-TIFS-2018.pdf,An Ensemble CNN2ELM for Age Estimation,2018
61,United States,Adience,adience,37.3706254,-121.9671894,NVIDIA,company,81e628a23e434762b1208045919af48dceb6c4d2,citation,https://arxiv.org/pdf/1807.07320.pdf,Attend and Rectify: A Gated Attention Mechanism for Fine-Grained Recovery,2018
62,Spain,Adience,adience,41.5019255,2.1048538,"UAB, Barcelona, Spain",edu,81e628a23e434762b1208045919af48dceb6c4d2,citation,https://arxiv.org/pdf/1807.07320.pdf,Attend and Rectify: A Gated Attention Mechanism for Fine-Grained Recovery,2018
63,Brazil,Adience,adience,-27.5953995,-48.6154218,University of Campinas,edu,bc749f0e81eafe9e32d56336750782f45d82609d,citation,https://pdfs.semanticscholar.org/bc74/9f0e81eafe9e32d56336750782f45d82609d.pdf,Combination of Texture and Geometric Features for Age Estimation in Face Images,2018
64,China,Adience,adience,22.4162632,114.2109318,Chinese University of Hong Kong,edu,29db16efc3b378c50511f743e5197a4c0b9e902f,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/papers/Kuang_Deeply_Learned_Rich_ICCV_2015_paper.pdf,Deeply Learned Rich Coding for Cross-Dataset Facial Age Estimation,2015
65,China,Adience,adience,39.993008,116.329882,SenseTime,company,29db16efc3b378c50511f743e5197a4c0b9e902f,citation,http://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/papers/Kuang_Deeply_Learned_Rich_ICCV_2015_paper.pdf,Deeply Learned Rich Coding for Cross-Dataset Facial Age Estimation,2015
66,Israel,Adience,adience,32.77824165,34.99565673,Open University of Israel,edu,0dccc881cb9b474186a01fd60eb3a3e061fa6546,citation,https://arxiv.org/pdf/1411.7964.pdf,Effective face frontalization in unconstrained images,2015
67,Russia,Adience,adience,56.3244285,44.0286291,Nizhny Novgorod State Linguistic University,edu,efb56e7488148d52d3b8a2dae9f8880b273f4226,citation,https://arxiv.org/pdf/1807.07718.pdf,"Efficient Facial Representations for Age, Gender and Identity Recognition in Organizing Photo Albums using Multi-output CNN",2018
68,Russia,Adience,adience,55.694797,37.564332,"Samsung-PDMI Joint AI Center, Steklov Institute of Mathematics",company,efb56e7488148d52d3b8a2dae9f8880b273f4226,citation,https://arxiv.org/pdf/1807.07718.pdf,"Efficient Facial Representations for Age, Gender and Identity Recognition in Organizing Photo Albums using Multi-output CNN",2018
69,Ireland,Adience,adience,53.308244,-6.2241652,University College Dublin,edu,cc45fb67772898c36519de565c9bd0d1d11f1435,citation,https://forensicsandsecurity.com/papers/EvaluatingFacialAgeEstimation.pdf,Evaluating Automated Facial Age Estimation Techniques for Digital Forensics,2018
70,China,Adience,adience,37.8956594,114.9042208,"Hebei, China",edu,fca6df7d36f449d48a8d1e48a78c860d52e3baf8,citation,https://arxiv.org/pdf/1805.10445.pdf,Fine-Grained Age Estimation in the wild with Attention LSTM Networks,2018
71,China,Adience,adience,38.8760446,115.4973873,North China Electric Power University,edu,fca6df7d36f449d48a8d1e48a78c860d52e3baf8,citation,https://arxiv.org/pdf/1805.10445.pdf,Fine-Grained Age Estimation in the wild with Attention LSTM Networks,2018
72,United States,Adience,adience,38.9403808,-92.3277375,University of Missouri-Columbia,edu,fca6df7d36f449d48a8d1e48a78c860d52e3baf8,citation,https://arxiv.org/pdf/1805.10445.pdf,Fine-Grained Age Estimation in the wild with Attention LSTM Networks,2018
73,India,Adience,adience,28.5456282,77.2731505,"IIIT Delhi, India",edu,af6e351d58dba0962d6eb1baf4c9a776eb73533f,citation,https://arxiv.org/pdf/1612.07454.pdf,How to Train Your Deep Neural Network with Dictionary Learning,2016
74,Turkey,Adience,adience,41.10427915,29.02231159,Istanbul Technical University,edu,9755554b13103df634f9b1ef50a147dd02eab02f,citation,https://arxiv.org/pdf/1610.00134.pdf,How Transferable Are CNN-Based Features for Age and Gender Classification?,2016
75,United States,Adience,adience,42.3619407,-71.0904378,MIT CSAIL,edu,9755554b13103df634f9b1ef50a147dd02eab02f,citation,https://arxiv.org/pdf/1610.00134.pdf,How Transferable Are CNN-Based Features for Age and Gender Classification?,2016
76,Turkey,Adience,adience,38.029533,32.506051,"Mevlana Universitesi, Konya, Turkey",edu,eb4151eebd0b7451ca990b242cef8357bfa9db92,citation,https://pdfs.semanticscholar.org/eb41/51eebd0b7451ca990b242cef8357bfa9db92.pdf,Human Gender Prediction on Facial Images Taken by Mobile Phone using Convolutional Neural Networks,2018
77,United Kingdom,Adience,adience,53.405936,-2.9655722,Liverpool University,edu,c95d8b9bddd76b8c83c8745747e8a33feedf3941,citation,https://arxiv.org/pdf/1805.02901.pdf,Image Ordinal Classification and Understanding: Grid Dropout with Masking Label,2018
78,China,Adience,adience,28.874513,105.431827,"Sichuan Police College, Luzhou, China",gov,c95d8b9bddd76b8c83c8745747e8a33feedf3941,citation,https://arxiv.org/pdf/1805.02901.pdf,Image Ordinal Classification and Understanding: Grid Dropout with Masking Label,2018
79,China,Adience,adience,30.672721,104.098806,University of Electronic Science and Technology of China,edu,c95d8b9bddd76b8c83c8745747e8a33feedf3941,citation,https://arxiv.org/pdf/1805.02901.pdf,Image Ordinal Classification and Understanding: Grid Dropout with Masking Label,2018
80,India,Adience,adience,19.1334302,72.9132679,"Indian Institute of Technology Bombay, Mumbai, India",edu,bb33376961f6663df848ae9bf055c9afd9182443,citation,https://arxiv.org/pdf/1901.01151.pdf,Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision,2019
81,United States,Adience,adience,47.6423318,-122.1369302,Microsoft,company,bb33376961f6663df848ae9bf055c9afd9182443,citation,https://arxiv.org/pdf/1901.01151.pdf,Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision,2019
82,United States,Adience,adience,42.3889785,-72.5286987,University of Massachusetts,edu,bb33376961f6663df848ae9bf055c9afd9182443,citation,https://arxiv.org/pdf/1901.01151.pdf,Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision,2019
83,India,Adience,adience,19.1334302,72.9132679,"Indian Institute of Technology Bombay, Mumbai, India",edu,d278e020be85a1ccd90aa366b70c43884dd3f798,citation,https://arxiv.org/pdf/1805.11191.pdf,Learning From Less Data: Diversified Subset Selection and Active Learning in Image Classification Tasks,2018
84,Germany,Adience,adience,53.1013476,8.8611632,"Deutsches Forschungszentrum für Künstliche Intelligenz GmbH, Robotics Innovation Center, 28359 Bremen, Germany",edu,0cfca73806f443188632266513bac6aaf6923fa8,citation,https://arxiv.org/pdf/1805.04756.pdf,Predictive Uncertainty in Large Scale Classification using Dropout - Stochastic Gradient Hamiltonian Monte Carlo,2018
85,Chile,Adience,adience,-33.4411279,-70.6407933,Universidad Catolica de Chile,edu,0cfca73806f443188632266513bac6aaf6923fa8,citation,https://arxiv.org/pdf/1805.04756.pdf,Predictive Uncertainty in Large Scale Classification using Dropout - Stochastic Gradient Hamiltonian Monte Carlo,2018
86,Italy,Adience,adience,43.7776426,11.259765,University of Florence,edu,e5563a0d6a2312c614834dc784b5cc7594362bff,citation,https://pdfs.semanticscholar.org/e556/3a0d6a2312c614834dc784b5cc7594362bff.pdf,Real-Time Demographic Profiling from Face Imagery with Fisher Vectors,2018
87,India,Adience,adience,20.1438995,85.6762033,Indian Institute of Technology Bhubaneswar,edu,b46d49cb7aade5ab7be51bd7a0ce3aa6f7c6b9ed,citation,https://arxiv.org/pdf/1712.01661.pdf,Recognizing Gender from Human Facial Regions using Genetic Algorithm,2018
88,India,Adience,adience,29.8542626,77.8880002,"Indian institute of Technology Roorkee, India",edu,b46d49cb7aade5ab7be51bd7a0ce3aa6f7c6b9ed,citation,https://arxiv.org/pdf/1712.01661.pdf,Recognizing Gender from Human Facial Regions using Genetic Algorithm,2018
89,India,Adience,adience,22.714846,88.4161884,"Institute of Engineering & Management, Kolkata",edu,b46d49cb7aade5ab7be51bd7a0ce3aa6f7c6b9ed,citation,https://arxiv.org/pdf/1712.01661.pdf,Recognizing Gender from Human Facial Regions using Genetic Algorithm,2018
90,United Kingdom,Adience,adience,51.49887085,-0.17560797,Imperial College London,edu,7173871866fc7e555e9123d1d7133d20577054e8,citation,https://arxiv.org/pdf/1807.08108.pdf,Simultaneous Adversarial Training - Learn from Others Mistakes,2018
91,India,Adience,adience,28.5456282,77.2731505,"IIIT Delhi, India",edu,5e39deb4bff7b887c8f3a44dfe1352fbcde8a0bd,citation,https://arxiv.org/pdf/1810.06221.pdf,Supervised COSMOS Autoencoder: Learning Beyond the Euclidean Loss!,2018
92,India,Adience,adience,30.7649646,76.7750066,"Infosys Ltd., Chandigarh, India",company,5e39deb4bff7b887c8f3a44dfe1352fbcde8a0bd,citation,https://arxiv.org/pdf/1810.06221.pdf,Supervised COSMOS Autoencoder: Learning Beyond the Euclidean Loss!,2018
93,United States,Adience,adience,30.6108365,-96.352128,Texas A&M University,edu,5e39deb4bff7b887c8f3a44dfe1352fbcde8a0bd,citation,https://arxiv.org/pdf/1810.06221.pdf,Supervised COSMOS Autoencoder: Learning Beyond the Euclidean Loss!,2018
94,China,Adience,adience,45.7413921,126.62552755,Harbin Institute of Technology,edu,c5fff7adc5084d69390918daf09e832ec191144b,citation,,Deep learning application based on embedded GPU,2017
95,China,Adience,adience,26.085573,119.372442,Fujian University of Technology,edu,c5fff7adc5084d69390918daf09e832ec191144b,citation,,Deep learning application based on embedded GPU,2017
96,China,Adience,adience,25.28164,110.337304,Guilin University of Electronic Technology,edu,c5fff7adc5084d69390918daf09e832ec191144b,citation,,Deep learning application based on embedded GPU,2017
97,United States,Adience,adience,32.8536333,-117.2035286,Kyung Hee University,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017
98,Saudi Arabia,Adience,adience,24.7246403,46.62335012,King Saud University,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017
99,Bangladesh,Adience,adience,23.7289899,90.3982682,Institute of Information Technology,edu,854b1f0581f5d3340f15eb79452363cbf38c04c8,citation,,Directional Age-Primitive Pattern (DAPP) for Human Age Group Recognition and Age Estimation,2017
100,Turkey,Adience,adience,38.6747649,39.1866925,"Firat Üniversitesi Elaziğ, Türkiye",edu,ecfb93de88394a244896bfe6ee7bf39fb250b820,citation,,Gender recognition from face images with deep learning,2017
101,Turkey,Adience,adience,38.6812759,39.196083,"Enerji Sistemleri Müh. Bölümü Teknoloji Fakültesi, Firat Üniversitesi Elaziğ, Türkiye",edu,ecfb93de88394a244896bfe6ee7bf39fb250b820,citation,,Gender recognition from face images with deep learning,2017
102,Turkey,Adience,adience,38.6774755,39.2030121,"Enformatik Bölümü Firat Üniversitesi Elaziğ, Türkiye",edu,ecfb93de88394a244896bfe6ee7bf39fb250b820,citation,,Gender recognition from face images with deep learning,2017
103,Canada,Adience,adience,49.2593879,-122.9151893,"AltumView Systems Inc., Burnaby, BC, Canada",company,b44f03b5fa8c6275238c2d13345652e6ff7e6ea9,citation,,Lapped convolutional neural networks for embedded systems,2017
104,Korea,Adience,adience,37.2830003,127.04548469,Ajou University,edu,24286ef164f0e12c3e9590ec7f636871ba253026,citation,,Age and gender classification using wide convolutional neural network and Gabor filter,2018
105,South Korea,Adience,adience,37.26728,126.9841151,Seoul National University,edu,24286ef164f0e12c3e9590ec7f636871ba253026,citation,,Age and gender classification using wide convolutional neural network and Gabor filter,2018
106,China,Adience,adience,23.143197,113.34009651,South China Normal University,edu,dc6ad30c7a4bc79bb06b4725b16e202d3d7d8935,citation,,Age classification with deep learning face representation,2017
107,China,Adience,adience,23.0502042,113.39880323,South China University of Technology,edu,dc6ad30c7a4bc79bb06b4725b16e202d3d7d8935,citation,,Age classification with deep learning face representation,2017
108,China,Adience,adience,30.724051,104.026606,Sichuan Film and Television University,edu,9215d36c501d6ee57d74c1eeb1475efd800d92d3,citation,,An optimization framework of video advertising: using deep learning algorithm based on global image information,2018
109,China,Adience,adience,30.578908,104.27712,Sichuan Tourism University,edu,9215d36c501d6ee57d74c1eeb1475efd800d92d3,citation,,An optimization framework of video advertising: using deep learning algorithm based on global image information,2018
110,Romania,Adience,adience,46.7677955,23.5912762,Babes Bolyai University,edu,7aa32e0639e0750e9eee3ce16e51e9f94241ae88,citation,,Automatic gender recognition for “in the wild” facial images using convolutional neural networks,2017
111,Romania,Adience,adience,46.7723581,23.5852075,Technical University,edu,7aa32e0639e0750e9eee3ce16e51e9f94241ae88,citation,,Automatic gender recognition for “in the wild” facial images using convolutional neural networks,2017
112,United States,Adience,adience,38.926761,-92.29193783,University of Missouri,edu,0e71d712f771196189b01f0088cc3497d174493b,citation,,Fine-Grained Age Group Classification in the wild,2018
113,China,Adience,adience,38.8760446,115.4973873,North China Electric Power University,edu,0e71d712f771196189b01f0088cc3497d174493b,citation,,Fine-Grained Age Group Classification in the wild,2018
114,China,Adience,adience,22.304572,114.17976285,Hong Kong Polytechnic University,edu,dc2f16f967eac710cb9b7553093e9c977e5b761d,citation,,Learning a lightweight deep convolutional network for joint age and gender recognition,2016
115,China,Adience,adience,23.09461185,113.28788994,Sun Yat-Sen University,edu,dc2f16f967eac710cb9b7553093e9c977e5b761d,citation,,Learning a lightweight deep convolutional network for joint age and gender recognition,2016
116,South Korea,Adience,adience,36.3721427,127.36039,KAIST,edu,92d051d4680eb41eb172d23cb8c93eed7677af56,citation,,Adversarial Spatial Frequency Domain Critic Learning for Age and Gender Classification,2018
117,Thailand,Adience,adience,14.0785,100.6140362,"Asian Institute of Technology (AIT), Pathum Thani 12120, Thailand",edu,984edce0b961418d81203ec477b9bfa5a8197ba3,citation,,Customer and target individual face analysis for retail analytics,2018
118,China,Adience,adience,39.98177,116.330086,National Laboratory of Pattern Recognition,edu,d80159bbe1d576d147ca9adbc9339a05fe3bab28,citation,,"Demographic Analysis from Biometric Data: Achievements, Challenges, and New Frontiers",2018
119,South Korea,Adience,adience,36.383765,127.36694,"Electronics and Telecommunications Research Institute, Daejeon, South Korea",edu,771e27c4b53f58622e6f03788b5102e5e70b1e49,citation,,Facial Attribute Recognition by Recurrent Learning With Visual Fixation,2018
120,China,Adience,adience,28.874513,105.431827,"Sichuan Police College, Luzhou, China",gov,7587a09d924cab41822a07cd1a988068b74baabb,citation,,Image scoring: Patch based CNN model for small or medium dataset,2017
121,Brazil,Adience,adience,-22.8148374,-47.0647708,University of Campinas (UNICAMP),edu,b161d261fabb507803a9e5834571d56a3b87d147,citation,http://www.smc2017.org/SMC2017_Papers/media/files/1077.pdf,Gender recognition from face images using a geometric descriptor,2017
122,China,Adience,adience,40.00229045,116.32098908,Tsinghua University,edu,2149d49c84a83848d6051867290d9c8bfcef0edb,citation,,Label-Sensitive Deep Metric Learning for Facial Age Estimation,2018
123,United States,Adience,adience,40.4319722,-86.92389368,Purdue University,edu,07a1e6d26028b28185b7a3eee86752c240a24261,citation,,MODE: automated neural network model debugging via state differential analysis and input selection,2018
124,Germany,Adience,adience,-35.4354218,-71.6199998,"Geospatial Laboratory, Universidad Católica del Maule, Talca, Chile",edu,3a05415356bd574cad1a9f1be21214e428bbc81b,citation,,Multinomial Naive Bayes for real-time gender recognition,2016
125,Cyprus,Adience,adience,34.67567405,33.04577648,Cyprus University of Technology,edu,9f3c9e41f46df9c94d714b1f080dafad6b4de1de,citation,,On the detection of images containing child-pornographic material,2017
126,United States,Adience,adience,32.8536333,-117.2035286,Kyung Hee University,edu,9d4692e243e25eb465a0480376beb60a5d2f0f13,citation,,Positional Ternary Pattern (PTP): An edge based image descriptor for human age recognition,2016
127,Italy,Adience,adience,43.7192522,10.4239948,"Istituto di Informatica e Telematica, Consiglio Nazionale delle Ricerche, Pisa, Italy",edu,17de5a9ce09f4834629cd76b8526071a956c9c6d,citation,,Smart Parental Advisory: A Usage Control and Deep Learning-Based Framework for Dynamic Parental Control on Smart TV,2017
128,Vietnam,Adience,adience,20.8368539,106.6942087,Vietnam Maritime University,edu,d38b32d91d56b01c77ef4dd7d625ce5217c6950b,citation,,Unconstrained gender classification by multi-resolution LPQ and SIFT,2016
129,Poland,Adience,adience,50.0657033,19.91895867,AGH University of Science and Technology,edu,cca476114c48871d05537abb303061de5ab010d6,citation,,A compact deep convolutional neural network architecture for video based age and gender estimation,2016
130,Singapore,Adience,adience,1.3483099,103.6831347,"NTU, Singapore",edu,8a917903b0a1d47f24bc7776ab0bd00aa8ec88f3,citation,,A Constrained Deep Neural Network for Ordinal Regression,2018
131,Spain,Adience,adience,41.5008957,2.111553,Autonomous University of Barcelona,edu,c9c2de3628be7e249722b12911bebad84b567ce6,citation,,Age and gender recognition in the wild with deep attention,2017
132,China,Adience,adience,38.8637191,115.5148326,"Hebei Information Engineering School, Baoding, China",edu,ea227e47b8a1e8f55983c34a17a81e5d3fa11cfd,citation,,Age group classification in the wild with deep RoR architecture,2017
133,China,Adience,adience,38.8760446,115.4973873,North China Electric Power University,edu,ea227e47b8a1e8f55983c34a17a81e5d3fa11cfd,citation,,Age group classification in the wild with deep RoR architecture,2017
134,United States,Adience,adience,38.9403808,-92.3277375,University of Missouri Columbia,edu,ea227e47b8a1e8f55983c34a17a81e5d3fa11cfd,citation,,Age group classification in the wild with deep RoR architecture,2017
135,India,Adience,adience,29.8542626,77.8880002,"Indian institute of Technology Roorkee, India",edu,f4003cbbff3b3d008aa64c76fed163c10d9c68bd,citation,,Compass local binary patterns for gender recognition of facial photographs and sketches,2016
136,Malaysia,Adience,adience,3.12267405,101.65356103,"University of Malaya, Kuala Lumpur",edu,d4d1ac1cfb2ca703c4db8cc9a1c7c7531fa940f9,citation,,"Gender estimation based on supervised HOG, Action Units and unsupervised CNN feature extraction",2017
137,United Kingdom,Adience,adience,51.5247272,-0.03931035,Queen Mary University of London,edu,d7fd3dedb6b260702ed5e4b9175127815286e8da,citation,,Knowledge sharing: From atomic to parametrised context and shallow to deep models,2017
138,Taiwan,Adience,adience,25.0421852,121.6145477,"Academia Sinica, Taipei, Taiwan",edu,aa6f7c3daed31d331ef626758e990cbc04632852,citation,,Merging Deep Neural Networks for Mobile Devices,2018
139,China,Adience,adience,22.4162632,114.2109318,Chinese University of Hong Kong,edu,aaa2b45153051e23d5a35ccf9af8ecabc0fe24cd,citation,https://pdfs.semanticscholar.org/aaa2/b45153051e23d5a35ccf9af8ecabc0fe24cd.pdf,1 How Good can Human Predict Facial Age ?,2017
140,China,Adience,adience,39.993008,116.329882,SenseTime,company,aaa2b45153051e23d5a35ccf9af8ecabc0fe24cd,citation,https://pdfs.semanticscholar.org/aaa2/b45153051e23d5a35ccf9af8ecabc0fe24cd.pdf,1 How Good can Human Predict Facial Age ?,2017
141,China,Adience,adience,39.993008,116.329882,SenseTime,company,8fee9b8c44626c4ac6b96ef183394bc4f36dc95f,citation,https://arxiv.org/pdf/1708.09687.pdf,Quantifying Facial Age by Posterior of Age Comparisons,2017
142,China,Adience,adience,22.4162632,114.2109318,Chinese University of Hong Kong,edu,8fee9b8c44626c4ac6b96ef183394bc4f36dc95f,citation,https://arxiv.org/pdf/1708.09687.pdf,Quantifying Facial Age by Posterior of Age Comparisons,2017
143,United States,Adience,adience,40.9153196,-73.1270626,Stony Brook University,edu,14e9158daf17985ccbb15c9cd31cf457e5551990,citation,https://pdfs.semanticscholar.org/14e9/158daf17985ccbb15c9cd31cf457e5551990.pdf,ConvNets with Smooth Adaptive Activation Functions for Regression,2017
144,United States,Adience,adience,35.93006535,-84.31240032,Oak Ridge National Laboratory,edu,14e9158daf17985ccbb15c9cd31cf457e5551990,citation,https://pdfs.semanticscholar.org/14e9/158daf17985ccbb15c9cd31cf457e5551990.pdf,ConvNets with Smooth Adaptive Activation Functions for Regression,2017
145,United States,Adience,adience,40.90826665,-73.11520891,Stony Brook University Hospital,edu,14e9158daf17985ccbb15c9cd31cf457e5551990,citation,https://pdfs.semanticscholar.org/14e9/158daf17985ccbb15c9cd31cf457e5551990.pdf,ConvNets with Smooth Adaptive Activation Functions for Regression,2017
146,India,Adience,adience,28.5456282,77.2731505,"IIIT Delhi, India",edu,c43d3ad956118ea1d26d39903097e2db86eae822,citation,https://arxiv.org/pdf/1904.01219.pdf,Deep Learning for Face Recognition: Pride or Prejudiced?,2019
147,Ireland,Adience,adience,53.27639715,-9.05829961,National University of Ireland Galway,edu,e08038b14165536c52ffe950d90d0f43be9c8f15,citation,https://arxiv.org/pdf/1703.08383.pdf,Smart Augmentation Learning an Optimal Data Augmentation Strategy,2017
148,Taiwan,Adience,adience,25.0410728,121.6147562,Institute of Information Science,edu,39539b7fcf1c637b04de84b23dc9c85a8b2f9f40,citation,https://arxiv.org/pdf/1805.04980.pdf,Unifying and Merging Well-trained Deep Neural Networks for Inference Stage,2018
149,Taiwan,Adience,adience,25.021321,121.5360683,MOST Joint Research Center for AI Technology and All Vista Healthcare,company,39539b7fcf1c637b04de84b23dc9c85a8b2f9f40,citation,https://arxiv.org/pdf/1805.04980.pdf,Unifying and Merging Well-trained Deep Neural Networks for Inference Stage,2018
150,India,Adience,adience,28.5449756,77.1926284,"IIT Delhi, India",edu,0dc61f199539cd15f847b688740be49b39e3d520,citation,https://pdfs.semanticscholar.org/0dc6/1f199539cd15f847b688740be49b39e3d520.pdf,Age Group Determination from Face Using Texture Classification based on Probabilistic Non-Extensive Entropy,2017
151,Spain,Adience,adience,41.5008957,2.111553,Autonomous University of Barcelona,edu,d7f6eaa5caa0d187cd1fe51d5bc27343921e7539,citation,https://arxiv.org/pdf/1807.07320.pdf,Attend and Rectify: A Gated Attention Mechanism for Fine-Grained Recovery,2018
152,Singapore,Adience,adience,1.2962018,103.77689944,National University of Singapore,edu,3b50a85ba29f0f7eb49fb275be86e6c2b4f8fa4b,citation,https://pdfs.semanticscholar.org/3b50/a85ba29f0f7eb49fb275be86e6c2b4f8fa4b.pdf,Image ordinal classification with deep multi-view learning,2018
153,China,Adience,adience,28.874513,105.431827,"Sichuan Police College, Luzhou, China",gov,3b50a85ba29f0f7eb49fb275be86e6c2b4f8fa4b,citation,https://pdfs.semanticscholar.org/3b50/a85ba29f0f7eb49fb275be86e6c2b4f8fa4b.pdf,Image ordinal classification with deep multi-view learning,2018
154,China,Adience,adience,30.788537,103.888902,"UESTC, Chengdu, China",edu,3b50a85ba29f0f7eb49fb275be86e6c2b4f8fa4b,citation,https://pdfs.semanticscholar.org/3b50/a85ba29f0f7eb49fb275be86e6c2b4f8fa4b.pdf,Image ordinal classification with deep multi-view learning,2018
155,United States,Adience,adience,32.8536333,-117.2035286,Kyung Hee University,edu,73b83ef7ee5f929be51a91096b57c098008f384e,citation,https://pdfs.semanticscholar.org/73b8/3ef7ee5f929be51a91096b57c098008f384e.pdf,Mining Wrinkle-Patterns with Local EdgePrototypic Pattern (LEPP) Descriptor for the Recognition of Human Age-groups,2018
156,India,Adience,adience,10.9365094,76.9562405,"Sri Krishna College of Engineering and Technology, Coimbatore, India",edu,ece46e3a126953f639149fc233bddcd44d8afad1,citation,https://pdfs.semanticscholar.org/ece4/6e3a126953f639149fc233bddcd44d8afad1.pdf,Semantic-Based Facial Image-Retrieval System with Aid of Adaptive Particle Swarm Optimization and Squared Euclidian Distance,2015
157,Canada,Adience,adience,45.5010087,-73.6157778,University of Montreal,edu,3540625bc996601a9d04c4027169b7fcad1b9eae,citation,https://pdfs.semanticscholar.org/3540/625bc996601a9d04c4027169b7fcad1b9eae.pdf,TECHNIQUES IN ORDINAL CLASSIFICATION AND IMAGE-TO-IMAGE TRANSLATION,2018
158,Canada,Adience,adience,45.5307147,-73.6135931,"Institute for Learning, Algorithms Montreal, Canada",edu,e8d0eb3c3bf64b38ec04e982745147428459e2d2,citation,https://arxiv.org/pdf/1705.05278.pdf,Unimodal Probability Distributions for Deep Ordinal Classification,2017
159,China,Adience,adience,45.7413921,126.62552755,Harbin Institute of Technology,edu,09e353946fb6adf1621f33041853c58aecfd183b,citation,,Deep convolutional neural networks-based age and gender classification with facial images,2017
160,China,Adience,adience,26.085573,119.372442,Fujian University of Technology,edu,09e353946fb6adf1621f33041853c58aecfd183b,citation,,Deep convolutional neural networks-based age and gender classification with facial images,2017
161,China,Adience,adience,25.28164,110.337304,Guilin University of Electronic Technology,edu,09e353946fb6adf1621f33041853c58aecfd183b,citation,,Deep convolutional neural networks-based age and gender classification with facial images,2017
162,Israel,Adience,adience,32.77824165,34.99565673,Open University of Israel,edu,1be498d4bbc30c3bfd0029114c784bc2114d67c0,citation,,Age and Gender Estimation of Unfiltered Faces,2014
163,South Korea,Adience,adience,37.5509442,126.9410023,Sogang University,edu,0deea943ac4dc1be822c02f97d0c6c97e201ba8d,citation,,Age category estimation using matching convolutional neural network,2018
164,Taiwan,Adience,adience,25.0411727,121.6146518,"Insititute of Information Science, Academia Sinica, Taipei, Taiwan",edu,3d0444be5be1d19d93e91519e48e314b3035e4cf,citation,,Joint Estimation of Age and Gender from Unconstrained Face Images Using Lightweight Multi-Task CNN for Mobile Applications,2018