diff options
Diffstat (limited to 'site/datasets/final/morph.csv')
| -rw-r--r-- | site/datasets/final/morph.csv | 286 |
1 files changed, 286 insertions, 0 deletions
diff --git a/site/datasets/final/morph.csv b/site/datasets/final/morph.csv new file mode 100644 index 00000000..cf7ad22b --- /dev/null +++ b/site/datasets/final/morph.csv @@ -0,0 +1,286 @@ +index,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,MORPH Commercial,morph,0.0,0.0,,,9055b155cbabdce3b98e16e5ac9c0edf00f9552f,main,http://doi.ieeecomputersociety.org/10.1109/FGR.2006.78,MORPH: a longitudinal image database of normal adult age-progression,2006 +1,MORPH Commercial,morph,34.80809035,135.45785218,Osaka University,edu,dad6b36fd515bda801f3d22a462cc62348f6aad8,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6117531,Gait-based age estimation using a whole-generation gait database,2011 +2,MORPH Commercial,morph,40.0044795,116.370238,Chinese Academy of Sciences,edu,ddd0f1c53f76d7fc20e11b7e33bbdc0437516d2b,citation,https://doi.org/10.1109/ICDSP.2016.7868598,Deep learning-based learning to rank with ties for image re-ranking,2016 +3,MORPH Commercial,morph,39.1118774,117.3497451,Civil Aviation University of China,edu,ddd0f1c53f76d7fc20e11b7e33bbdc0437516d2b,citation,https://doi.org/10.1109/ICDSP.2016.7868598,Deep learning-based learning to rank with ties for image re-ranking,2016 +4,MORPH Commercial,morph,25.0410728,121.6147562,Institute of Information Science,edu,4c71b0cdb6b80889b976e8eb4457942bd4dd7b66,citation,https://doi.org/10.1109/TIP.2014.2387379,A Learning Framework for Age Rank Estimation Based on Face Images With Scattering Transform,2015 +5,MORPH Commercial,morph,51.0267513,-1.3972576,"IBM Hursley Labs, UK",company,7123e510dea783035b02f6c35e35a1a09677c5ab,citation,https://doi.org/10.1109/ICPR.2016.7900297,Back to the future: A fully automatic method for robust age progression,2016 +6,MORPH Commercial,morph,35.9042272,-78.85565763,"IBM Research, North Carolina",company,7123e510dea783035b02f6c35e35a1a09677c5ab,citation,https://doi.org/10.1109/ICPR.2016.7900297,Back to the future: A fully automatic method for robust age progression,2016 +7,MORPH Commercial,morph,51.49887085,-0.17560797,Imperial College London,edu,7123e510dea783035b02f6c35e35a1a09677c5ab,citation,https://doi.org/10.1109/ICPR.2016.7900297,Back to the future: A fully automatic method for robust age progression,2016 +8,MORPH Commercial,morph,35.5167538,139.48342251,Tokyo Institute of Technology,edu,3083d2c6d4f456e01cbb72930dc2207af98a6244,citation,http://pdfs.semanticscholar.org/3083/d2c6d4f456e01cbb72930dc2207af98a6244.pdf,Perceived Age Estimation from Face Images,2011 +9,MORPH Commercial,morph,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 +10,MORPH Commercial,morph,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 +11,MORPH Commercial,morph,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 +12,MORPH Commercial,morph,40.6341322,-8.6599726,"University of Beira Interior, Portugal",edu,81c21f4aafab39b7f5965829ec9e0f828d6a6182,citation,https://doi.org/10.1109/BTAS.2015.7358744,Acquiring high-resolution face images in outdoor environments: A master-slave calibration algorithm,2015 +13,MORPH Commercial,morph,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 +14,MORPH Commercial,morph,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 +15,MORPH Commercial,morph,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 +16,MORPH Commercial,morph,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 +17,MORPH Commercial,morph,39.65404635,-79.96475355,West Virginia University,edu,af12a79892bd030c19dfea392f7a7ccb0e7ebb72,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2012.6247972,A study on human age estimation under facial expression changes,2012 +18,MORPH Commercial,morph,23.09461185,113.28788994,Sun Yat-Sen University,edu,2d7c2c015053fff5300515a7addcd74b523f3f66,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8323422,Age-Related Factor Guided Joint Task Modeling Convolutional Neural Network for Cross-Age Face Recognition,2018 +19,MORPH Commercial,morph,40.0044795,116.370238,Chinese Academy of Sciences,edu,b234d429c9ea682e54fca52f4b889b3170f65ffc,citation,http://doi.ieeecomputersociety.org/10.1109/TPAMI.2012.22,A Concatenational Graph Evolution Aging Model,2012 +20,MORPH Commercial,morph,39.9922379,116.30393816,Peking University,edu,b234d429c9ea682e54fca52f4b889b3170f65ffc,citation,http://doi.ieeecomputersociety.org/10.1109/TPAMI.2012.22,A Concatenational Graph Evolution Aging Model,2012 +21,MORPH Commercial,morph,40.00229045,116.32098908,Tsinghua University,edu,b234d429c9ea682e54fca52f4b889b3170f65ffc,citation,http://doi.ieeecomputersociety.org/10.1109/TPAMI.2012.22,A Concatenational Graph Evolution Aging Model,2012 +22,MORPH Commercial,morph,30.19331415,120.11930822,Zhejiang University,edu,ee65cee5151928c63d3ef36fcbb582fabb2b6d2c,citation,https://doi.org/10.1109/LSP.2016.2602538,Structure-Aware Slow Feature Analysis for Age Estimation,2016 +23,MORPH Commercial,morph,40.0141905,-83.0309143,University of Electronic Science and Technology of China,edu,ee65cee5151928c63d3ef36fcbb582fabb2b6d2c,citation,https://doi.org/10.1109/LSP.2016.2602538,Structure-Aware Slow Feature Analysis for Age Estimation,2016 +24,MORPH Commercial,morph,40.0044795,116.370238,Chinese Academy of Sciences,edu,288964068cd87d97a98b8bc927d6e0d2349458a2,citation,https://pdfs.semanticscholar.org/2889/64068cd87d97a98b8bc927d6e0d2349458a2.pdf,Mean-Variance Loss for Deep Age Estimation from a Face,0 +25,MORPH Commercial,morph,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,288964068cd87d97a98b8bc927d6e0d2349458a2,citation,https://pdfs.semanticscholar.org/2889/64068cd87d97a98b8bc927d6e0d2349458a2.pdf,Mean-Variance Loss for Deep Age Estimation from a Face,0 +26,MORPH Commercial,morph,39.65404635,-79.96475355,West Virginia University,edu,cd63759842a56bd2ede3999f6e11a74ccbec318b,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5995404,Simultaneous dimensionality reduction and human age estimation via kernel partial least squares regression,2011 +27,MORPH Commercial,morph,28.5456282,77.2731505,"IIIT Delhi, India",edu,ffc81ced9ee8223ab0adb18817321cbee99606e6,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7791157,A multibiometrics-based CAPTCHA for improved online security,2016 +28,MORPH Commercial,morph,39.65404635,-79.96475355,West Virginia University,edu,ffc81ced9ee8223ab0adb18817321cbee99606e6,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7791157,A multibiometrics-based CAPTCHA for improved online security,2016 +29,MORPH Commercial,morph,41.25713055,-72.9896696,Yale University,edu,df7312cbabb7d75d915ba0d91dea77100ded5c56,citation,https://arxiv.org/pdf/1811.06446.pdf,Preliminary Studies on a Large Face Database,2018 +30,MORPH Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,39.94976005,116.33629046,Beijing Jiaotong University,edu,4b9ec224949c79a980a5a66664d0ac6233c3d575,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7565501,Human Facial Age Estimation by Cost-Sensitive Label Ranking and Trace Norm Regularization,2017 +34,MORPH Commercial,morph,43.1576969,-77.58829158,University of Rochester,edu,4b9ec224949c79a980a5a66664d0ac6233c3d575,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7565501,Human Facial Age Estimation by Cost-Sensitive Label Ranking and Trace Norm Regularization,2017 +35,MORPH Commercial,morph,1.2962018,103.77689944,National University of Singapore,edu,4b9ec224949c79a980a5a66664d0ac6233c3d575,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7565501,Human Facial Age Estimation by Cost-Sensitive Label Ranking and Trace Norm Regularization,2017 +36,MORPH Commercial,morph,40.0044795,116.370238,Chinese Academy of Sciences,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 +37,MORPH Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,-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 Commercial,morph,-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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 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 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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,-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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 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 Commercial,morph,52.3553655,4.9501644,University of Amsterdam,edu,14014a1bdeb5d63563b68b52593e3ac1e3ce7312,citation,http://pdfs.semanticscholar.org/1401/4a1bdeb5d63563b68b52593e3ac1e3ce7312.pdf,Expression-Invariant Age Estimation,2014 +136,MORPH Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,42.718568,-84.47791571,Michigan State University,edu,e506cdb250eba5e70c5147eb477fbd069714765b,citation,https://pdfs.semanticscholar.org/e506/cdb250eba5e70c5147eb477fbd069714765b.pdf,Heterogeneous Face Recognition,2012 +143,MORPH Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 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 +149,MORPH Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,-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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 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 Age 'X'?,2017 +167,MORPH Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 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, Boulder",edu,4aabd6db4594212019c9af89b3e66f39f3108aac,citation,http://pdfs.semanticscholar.org/4aab/d6db4594212019c9af89b3e66f39f3108aac.pdf,The Mere Exposure Effect and Classical Conditioning,2015 +175,MORPH Commercial,morph,34.2375581,-77.9270129,University of North Carolina Wilmington,edu,73d15a975b0595e0cc2e0981a9396a89c474dc7e,citation,https://arxiv.org/pdf/1811.03680.pdf,Gender Effect on Face Recognition for a Large Longitudinal Database,2018 +176,MORPH Commercial,morph,40.00229045,116.32098908,Tsinghua University,edu,51bb86dc8748088a198b216f7e97616634147388,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6890496,Face age estimation by using Bisection Search Tree,2013 +177,MORPH Commercial,morph,1.3037257,103.7737763,"Advanced Digital Sciences Center, Singapore",edu,8cffe360a05085d4bcba111a3a3cd113d96c0369,citation,http://doi.ieeecomputersociety.org/10.1109/ICCV.2011.6126248,Learning universal multi-view age estimator using video context,2011 +178,MORPH Commercial,morph,1.3170417,103.8321041,"Facebook, Singapore",company,8cffe360a05085d4bcba111a3a3cd113d96c0369,citation,http://doi.ieeecomputersociety.org/10.1109/ICCV.2011.6126248,Learning universal multi-view age estimator using video context,2011 +179,MORPH Commercial,morph,1.2962018,103.77689944,National University of 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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,39.65404635,-79.96475355,West Virginia 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 +187,MORPH Commercial,morph,40.00229045,116.32098908,Tsinghua University,edu,a53d13b9110cddb2a5f38b9d7ed69d328e3c6db9,citation,https://doi.org/10.1109/TIP.2015.2481327,Cost-Sensitive Local Binary Feature Learning for Facial Age Estimation,2015 +188,MORPH Commercial,morph,1.3484104,103.68297965,Nanyang Technological University,edu,a53d13b9110cddb2a5f38b9d7ed69d328e3c6db9,citation,https://doi.org/10.1109/TIP.2015.2481327,Cost-Sensitive Local Binary Feature Learning for Facial Age Estimation,2015 +189,MORPH Commercial,morph,42.718568,-84.47791571,Michigan State University,edu,141cb9ee401f223220d3468592effa90f0c255fa,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7815403,Longitudinal Study of Automatic Face Recognition,2015 +190,MORPH Commercial,morph,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 +191,MORPH Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 sensitivities for age estimation,2011 +194,MORPH Commercial,morph,1.2962018,103.77689944,National University of Singapore,edu,63488398f397b55552f484409b86d812dacde99a,citation,http://pdfs.semanticscholar.org/6348/8398f397b55552f484409b86d812dacde99a.pdf,Learning Universal Multi-view Age Estimator by Video Contexts,2011 +195,MORPH Commercial,morph,40.00229045,116.32098908,Tsinghua University,edu,6adecb82edbf84a0097ff623428f4f1936e31de0,citation,https://doi.org/10.1007/s11760-011-0246-4,Client-specific A-stack model for adult face verification across aging,2011 +196,MORPH Commercial,morph,1.3037257,103.7737763,"Advanced Digital Sciences Center, Singapore",edu,fcb97ede372c5bddde7a61924ac2fd29788c82ce,citation,https://doi.org/10.1109/TSMCC.2012.2192727,Ordinary Preserving Manifold Analysis for Human Age and Head Pose Estimation,2013 +197,MORPH Commercial,morph,1.3484104,103.68297965,Nanyang Technological 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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,39.95472495,-75.15346905,Temple University,edu,f24e379e942e134d41c4acec444ecf02b9d0d3a9,citation,http://pdfs.semanticscholar.org/f24e/379e942e134d41c4acec444ecf02b9d0d3a9.pdf,Analysis of Facial Images across Age Progression by Humans,2011 +217,MORPH Commercial,morph,39.65404635,-79.96475355,West Virginia University,edu,f24e379e942e134d41c4acec444ecf02b9d0d3a9,citation,http://pdfs.semanticscholar.org/f24e/379e942e134d41c4acec444ecf02b9d0d3a9.pdf,Analysis of Facial Images across Age Progression by Humans,2011 +218,MORPH Commercial,morph,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 +219,MORPH Commercial,morph,37.4102193,-122.05965487,Carnegie Mellon University,edu,452ea180cf4d08d7500fc4bc046fd7141fd3d112,citation,https://doi.org/10.1109/BTAS.2012.6374569,A robust approach to facial ethnicity classification on large scale face databases,2012 +220,MORPH Commercial,morph,47.3764534,8.54770931,ETH Zürich,edu,2facf3e85240042a02f289a0d40fee376c478d0f,citation,https://doi.org/10.1109/BTAS.2010.5634544,Aging face verification in score-age space using single reference image template,2010 +221,MORPH Commercial,morph,38.88140235,121.52281098,Dalian University of Technology,edu,ed70d1a9435c0b32c0c75c1a062f4f07556f7016,citation,https://doi.org/10.1109/ICIP.2015.7350774,Correlated warped Gaussian processes for gender-specific age estimation,2015 +222,MORPH Commercial,morph,40.0141905,-83.0309143,University of Electronic Science and Technology of China,edu,ed70d1a9435c0b32c0c75c1a062f4f07556f7016,citation,https://doi.org/10.1109/ICIP.2015.7350774,Correlated warped Gaussian processes for gender-specific age estimation,2015 +223,MORPH Commercial,morph,1.2962018,103.77689944,National University of Singapore,edu,0e5557a0cc58194ad53fab5dd6f4d4195d19ce4e,citation,https://doi.org/10.1109/TMM.2015.2500730,Deep Aging Face Verification With Large Gaps,2016 +224,MORPH Commercial,morph,51.52344665,-0.25973535,"North Acton, London",edu,0e5557a0cc58194ad53fab5dd6f4d4195d19ce4e,citation,https://doi.org/10.1109/TMM.2015.2500730,Deep Aging Face Verification With Large Gaps,2016 +225,MORPH Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 +238,MORPH Commercial,morph,25.0411727,121.6146518,"Academia Sinica, Taiwan",edu,635d2696aa597a278dd6563f079be06aa76a33c0,citation,https://doi.org/10.1109/ICIP.2016.7532429,Age estimation via fusion of multiple binary age grouping systems,2016 +239,MORPH Commercial,morph,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 +240,MORPH Commercial,morph,1.2988926,103.7873107,"Institute for Infocomm Research, Singapore",edu,85f7f03b79d03da5fae3a7f79d9aac228a635166,citation,https://doi.org/10.1109/WACV.2009.5403085,Age categorization via ECOC with fused gabor and LBP features,2009 +241,MORPH Commercial,morph,39.6810328,-75.7540184,University of Delaware,edu,aee3427d0814d8a398fd31f4f46941e9e5488d83,citation,http://dl.acm.org/citation.cfm?id=1924573,Face verification with aging using AdaBoost and local binary patterns,2010 +242,MORPH Commercial,morph,23.09461185,113.28788994,Sun Yat-Sen University,edu,d1b5b3e4b803dc4e50c5b80c1bc69c6d98751698,citation,https://doi.org/10.1109/LSP.2017.2661983,Modified Hidden Factor Analysis for Cross-Age Face Recognition,2017 +243,MORPH Commercial,morph,39.65404635,-79.96475355,West Virginia 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 Estimation,2017 +247,MORPH Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,-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 Commercial,morph,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 Recognition,2018 +264,MORPH Commercial,morph,40.0044795,116.370238,Chinese Academy of Sciences,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 Recognition,2018 +265,MORPH Commercial,morph,49.10184375,8.4331256,Karlsruhe Institute of Technology,edu,cfdc4d0f8e1b4b9ced35317d12b4229f2e3311ab,citation,https://pdfs.semanticscholar.org/cfdc/4d0f8e1b4b9ced35317d12b4229f2e3311ab.pdf,Quaero at TRECVID 2010: Semantic Indexing,2010 +266,MORPH Commercial,morph,42.718568,-84.47791571,Michigan State University,edu,02d650d8a3a9daaba523433fbe93705df0a7f4b1,citation,http://pdfs.semanticscholar.org/02d6/50d8a3a9daaba523433fbe93705df0a7f4b1.pdf,How Does Aging Affect Facial Components?,2012 +267,MORPH Commercial,morph,34.67567405,33.04577648,Cyprus University of 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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,-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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,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 Commercial,morph,-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 Commercial,morph,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 |