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+Database Name,Title,Journal/Pub/Conference,Year,Pages,Volume,Author1,Author2,Author3,Author4,Author5,Author 6,PDF,Priority,URL,bibtex_reference_only,notes
+LAG,Large Age-Gap Face Verification by Feature Injection in Deep Networks,Pattern Recognition Letters,2017,36-42,90,Simone Bianco,,,,,,bianco2017large-age.pdf,,http://www.ivl.disco.unimib.it/activities/large-age-gap-face-verification/,,
+YouTubeFaces,Face Recognition in Unconstrained Videos with Matched Background Similarity,IEEE Conf. on Computer Vision and Pattern Recognition (CVPR),2011,,,Lior Wolf,Tal Hassner,Itay Maoz,,,,,,,,
+LFW,Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments.,"University of Massachusetts, Amherst, Technical Report ",2007,07-49,,Gary B. Huang,Manu Ramesh,Tamara Berg,Erik Learned-Miller,,,,,http://vis-www.cs.umass.edu/lfw/lfw.pdf,,various citaton depending on various datasets provided. Citation used here was first one published in 2007
+HRT Transgender Database,Is the Eye Region More Reliable Than the Face? A Preliminary Study of Face-based Recognition on a Transgender Dataset,"In Proc. of IEEE Intl. Conf. on Biometrics: Theory, Applications, and Systems",2013,,,Gayathri Mahalingam,Karl Ricanek Jr.,,,,,,,https://pdfs.semanticscholar.org/b066/733d533250f4ddafd22c12456def7fa24f4c.pdf,,
+JAFFE,Coding Facial Expressions with Gabor Wavelets,3rd IEEE International Conference on Automatic Face and Gesture Recognition,1998,200-205,,Michael J. Lyons,Shigeru Akemastu,Miyuki Kamachi,Jiro Gyoba,,,,,http://www.kasrl.org/fg98-1.pdf,,
+CMDP,Distance Estimation of an Unknown Person from a Portrait,ECCV 2014,2014,,,X. P. Burgos-Artizzu,M.R. Ronchi,P. Perona,,,,,,,,
+WIDER,Recognize Complex Events from Static Images by Fusing Deep Channels,2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2015,,,"Xiong, Yuanjun and Zhu, Kai and Lin, Dahua and Tang, Xiaoou",,,,,,,,,,
+WIDER FACE,WIDER FACE: A Face Detection Benchmark,IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2016,,,"Yang, Shuo and Luo, Ping and Loy, Chen Change and Tang, Xiaoou",,,,,,,,,"@inproceedings{yang2016wider,
+	Author = {Yang, Shuo and Luo, Ping and Loy, Chen Change and Tang, Xiaoou},
+	Booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
+	Title = {WIDER FACE: A Face Detection Benchmark},
+	Year = {2016}}",
+300-W,300 faces In-the-wild challenge: Database and results,"Image and Vision Computing (IMAVIS), Special Issue on Facial Landmark Localisation ""In-The-Wild""",2016,,,C. Sagonas,E. Antonakos,"G, Tzimiropoulos",S. Zafeiriou,M. Pantic,,,1,,,
+300-W,300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge,"Proceedings of IEEE Int’l Conf. on Computer Vision (ICCV-W), 300 Faces in-the-Wild Challenge (300-W). Sydney, Australia",2013,,,C. Sagonas,G. Tzimiropoulos,S. Zafeiriou,M. Pantic,,,,2,,,
+300-W,A semi-automatic methodology for facial landmark annotation,"Proceedings of IEEE Int’l Conf. Computer Vision and Pattern Recognition (CVPR-W), 5th Workshop on Analysis and Modeling of Faces and Gestures (AMFG 2013). Oregon, USA,",2013,,,C. Sagonas,G. Tzimiropoulos,S. Zafeiriou,M. Pantic,,,,3,,,
+LFWP,Localizing Parts of Faces Using a Consensus of Exemplars,Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2011,,,Peter N. Belhumeur,"David W. Jacobs,",David J. Kriegman,Neeraj Kumar,,,,,http://neerajkumar.org/projects/face-parts/base/papers/nk_cvpr2011_faceparts.pdf,,
+Helen,Interactive Facial Feature Localization,ECCV,2012,,,Vuong Le,Jonathan Brandt,Zhe Lin,Lubomir Boudev,Thomas S. Huang,,,,http://www.ifp.illinois.edu/~vuongle2/helen/eccv2012_helen_final.pdf,,
+Hipsterwars,Hipster Wars: Discovering Elements of Fashion Styles.,In European Conference on Computer Vision,2014,,,M. Hadi Kiapour,Kota Yamaguchi,Alexander C. Berg,Tamara L. Berg,,,,,http://tamaraberg.com/papers/hipster_eccv14.pdf,"@inproceedings{
+            HipsterWarsECCV14,
+            title     = {Hipster Wars: Discovering Elements of Fashion Styles}
+            author    = {M. Hadi Kiapour, Kota Yamaguchi, Alexander C. Berg, Tamara L. Berg},
+            booktitle={European Conference on Computer Vision},
+            year      = {2014}
+            }",
+Adience,Age and Gender Estimation of Unfiltered Faces,"Transactions on Information Forensics and Security (IEEE-TIFS), special issue on Facial Biometrics in the Wild",2014,2170 - 2179,9,Eran Eidinger,Roee Enbar, Tal Hassner,,,,,,http://www.openu.ac.il/home/hassner/Adience/EidingerEnbarHassner_tifs.pdf,,
+AFW,"Face detection, pose estimation and landmark localization in the wild","Computer Vision and Pattern Recognition (CVPR) Providence, Rhode Island,",2012,,,X. Zhu,D. Ramanan,,,,,,,http://www.ics.uci.edu/~xzhu/paper/face-cvpr12.pdf,,
+AFLW,"Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization",,,,,Martin Koestinger,Paul Wohlhart,Peter M. Roth,Horst Bischof,,,,,https://files.icg.tugraz.at/seafhttp/files/d18813db-78c3-46a9-8614-bc0c8d428114/koestinger_befit_11.pdf,"@INPROCEEDINGS{koestinger11a,
+  author = {Martin Koestinger, Paul Wohlhart, Peter M. Roth and Horst Bischof},
+  title = {{Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization}},
+  booktitle = {{Proc. First IEEE International Workshop on Benchmarking Facial Image Analysis Technologies}},
+  year = {2011}
+} ",
+AgeDB,"AgeDB: the first manually collected, in-the-wild age database",Proceedings of IEEE Int’l Conf. on Computer Vision and Pattern Recognition (CVPR-W 2017,2017,,,S. Moschoglou,A. Papaioannou,C. Sagonas,J. Deng,I. Kotsia, S. Zafeiriou,,,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"@inproceedings{AgeDB,
+    author = {S. Moschoglou and A. Papaioannou and C. Sagonas and J. Deng and I. Kotsia and S. Zafeiriou},
+    address = {Honolulu, Hawaii},
+    booktitle = {Proceedings of IEEE Int’l Conf. on Computer Vision and Pattern Recognition (CVPR-W 2017)},
+    month = {June},
+    title = {AgeDB: the first manually collected, in-the-wild age database},
+    year = {2017},
+}",
+CAISA Webface,Learning Face Representation from Scratch,arXiv preprint arXiv:1411.7923.,2014,,,Dong Yi,Zhen Lei, Shengcai Liao,Stan Z. Li,,,,,https://arxiv.org/abs/1411.7923,,
+Caltech 10K Web Faces,Pruning Training Sets for Learning of Object Categories,Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2005,,,Anelia Angelova,Yaser Abu-Mostafa,Pietro Perona,,,,,,http://www.vision.caltech.edu/anelia/DataPruning/Angelova05DataPruning.pdf,This is a paper using the dataset (linked on the project website),
+CelebA,From Facial Parts Responses to Face Detection: A Deep Learning Approach,"in IEEE International Conference on Computer Vision (ICCV),",2015,,,S. Yang,P. Luo,C. C. Loy,X. Tang,,,,,https://arxiv.org/abs/1509.06451,"The following paper employed CelebA for face detection. (linked on the project website) @inproceedings{liu2015faceattributes,
+ author = {Ziwei Liu and Ping Luo and Xiaogang Wang and Xiaoou Tang},
+ title = {Deep Learning Face Attributes in the Wild},
+ booktitle = {Proceedings of International Conference on Computer Vision (ICCV)},
+ month = December,
+ year = {2015} 
+}",
+UMD,UMDFaces: An Annotated Face Dataset for Training Deep Networks,Arxiv preprint,2016,,,Ankan Bansal,Anirudh Nanduri,Carlos D Castillo,Rajeev Ranjan,Rama Chellappa,,,1,https://arxiv.org/abs/1611.01484v2,"@article{bansal2016umdfaces,
+			title={UMDFaces: An Annotated Face Dataset for Training Deep Networks},
+			author={Bansal, Ankan and Nanduri, Anirudh and Castillo, Carlos D and Ranjan, Rajeev and Chellappa, Rama}
+			journal={arXiv preprint arXiv:1611.01484v2},
+			year={2016}
+			}",
+UMD,The Do's and Don'ts for CNN-based Face Verification,Arxiv preprint,2017,,,Ankan Bansal,Carlos Castillo,"Rajeev Ranjan,",Rama Chellappa,,,,2,https://arxiv.org/abs/1705.07426,"@article{bansal2017dosanddonts,
+			title = {The Do's and Don'ts for CNN-based Face Verification},
+			author = {Bansal, Ankan and Castillo, Carlos and Ranjan, Rajeev and Chellappa, Rama},
+			journal = {arXiv preprint arXiv:1705.07426},
+			year = {2017}
+			}",
+COFW,Robust face landmark estimation under occlusion ,"ICCV 2013, Sydney, Australia",2013,,,X. P. Burgos-Artizzu,P. Perona,P. Dollár,,,,,,http://www.vision.caltech.edu/%7Expburgos/papers/ICCV13%20Burgos-Artizzu.pdf,,
+CMDP,Distance Estimation of an Unknown Person from a Portrait ,"ECCV 2014, Zurich, Switzerland",2014,,,X. P. Burgos-Artizzu,M.R. Ronchi,P. Perona,,,,,,http://www.vision.caltech.edu/~mronchi/papers/ECCV14_FaceDistancePortrait_PAPER.pdf,"@incollection{perona2014PortraitDistanceEstimation,
+  title={Distance Estimation of an Unknown Person from a Portrait},
+  author={Xavier P. Burgos-Artizzu, Matteo Ruggero Ronchi and Pietro Perona},
+  booktitle={Computer Vision--ECCV 2014},
+  pages={313--327},
+  year={2014},
+  publisher={Springer}
+}
+",
+FaceTracer,FaceTracer: A Search Engine for Large Collections of Images with Faces,European Conference on Computer Vision (ECCV),2008,340-353,,N. Kumar,P. N. Belhumeur,S. K. Nayar,,,,,1,http://www1.cs.columbia.edu/CAVE/publications/pdfs/Kumar_ECCV08.pdf,,
+FaceTracer,Face Swapping: Automatically Replacing Faces in Photographs,ACM Trans. on Graphics (also Proc. of ACM SIGGRAPH),2008,,,D. Bitouk,N. Kumar,S. Dhillon,P.N. Belhumeur,S. K. Nayar,,,2,http://www1.cs.columbia.edu/CAVE/publications/pdfs/Bitouk_SIGGRAPH08.pdf,,
+FDDB,FDDB: A Benchmark for Face Detection in Unconstrained Settings,"Technical Report UM-CS-2010-009, Dept. of Computer Science, University of Massachusetts",2010,,,Vidit Jain,Erik Learned-Mille,,,,,,,http://vis-www.cs.umass.edu/fddb/fddb.pdf,"@TechReport{fddbTech,
+  author = {Vidit Jain and Erik Learned-Miller},
+  title =  {FDDB: A Benchmark for Face Detection in Unconstrained Settings},
+  institution =  {University of Massachusetts, Amherst},
+  year = {2010},
+  number = {UM-CS-2010-009}
+  }",
+IMFDB,Indian Movie Face Database: A Benchmark for Face Recognition Under Wide Variations,"National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)",2013,,,Shankar Setty,et al,,,,,,,http://cvit.iiit.ac.in/projects/IMFDB/imfdb.pdf,"@InProceedings{imfdb,
+author    = {Shankar Setty, Moula Husain, Parisa Beham, Jyothi Gudavalli, Menaka Kandasamy, Radhesyam Vaddi,              Vidyagouri Hemadri, J C Karure, Raja Raju, Rajan, Vijay Kumar and C V Jawahar},
+title     = {{I}ndian {M}ovie {F}ace {D}atabase: {A} {B}enchmark for {F}ace {R}ecognition {U}nder {W}ide              {V}ariations},
+booktitle = {National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics              (NCVPRIPG)},
+month     = {Dec},
+year      = {2013}
+} ",
+imSitu,Situation Recognition: Visual Semantic Role Labeling for Image Understanding,"(1) Computer Science & Engineering, University of Washington, Seattle, WA
+(2) Allen Institute for Artificial Intelligence (AI2), Seattle, WA",,,,Mark Yatskar,Luke Zettlemoyer,Ali Farhadi,,,,,,https://homes.cs.washington.edu/~my89/publications/situations.pdf,,
+LAG,Large Age-Gap Face Verification by Feature Injection in Deep Networks,In Pattern Recognition Letters,2017,36-42,90,Simone Bianco,,,,,,,,http://www.ivl.disco.unimib.it/download/bianco2017large-age.pdf,,
+LFW-a,,,,,,,,,,,,,,,Comply with any instructions specified for the original LFW data set,
+LFW-a,Effective Face Recognition by Combining Multiple Descriptors and Learned Background Statistics,"IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 33(10),",2011,,,Lior Wolf,Tal Hassner,Yaniv Taigman,,,,,,http://www.openu.ac.il/home/hassner/projects/Patchlbp/WolfHassnerTaigman_TPAMI11.pdf,,
+MALF,Fine-grained Evaluation on Face Detection in the Wild.,Proceedings of the 11th IEEE International Conference on Automatic Face and Gesture Recognition Conference and Workshops.,2015,,,Bin Yang*,Junjie Yan*,Zhen Lei,Stan Z. Li,,,,,http://www.cbsr.ia.ac.cn/faceevaluation/faceevaluation15.pdf,"@inproceedings{faceevaluation15,
+title={Fine-grained Evaluation on Face Detection in the Wild},
+author={Yang, Bin and Yan, Junjie and Lei, Zhen and Li, Stan Z},
+booktitle={Automatic Face and Gesture Recognition (FG), 11th IEEE International 
+Conference on},
+year={2015},
+organization={IEEE}
+}",
+MegaFace 2,Level Playing Field for Million Scale Face Recognition,IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2017,,,"Nech, Aaron","Kemelmacher-Shlizerman, Ira",,,,,,If you're participating or using data from Challenge 2 please cite:,https://homes.cs.washington.edu/~kemelmi/ms.pdf,"@inproceedings{nech2017level,
+title={Level Playing Field For Million Scale Face Recognition},
+author={Nech, Aaron and Kemelmacher-Shlizerman, Ira},
+booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
+year={2017}
+}",
+MegaFace 2,The MegaFace Benchmark: 1 Million Faces for Recognition at Scale,IEEE Conference on Computer Vision and Pattern Recognition (CVPR),2016,,,"Kemelmacher-Shlizerman, Ira","Seitz, Steven M","Miller, Daniel","Brossard, Evan",,,,If you're using or participating in Challenge 1 please cite:,http://megaface.cs.washington.edu/KemelmacherMegaFaceCVPR16.pdf,"@inproceedings{kemelmacher2016megaface,
+title={The megaface benchmark: 1 million faces for recognition at scale},
+author={Kemelmacher-Shlizerman, Ira and Seitz, Steven M and Miller, Daniel and Brossard, Evan},
+booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
+pages={4873--4882},
+year={2016}
+}",
+MORPH non-commercial,MORPH: A Longitudinal Image Database of Normal Adult Age-Progression,"IEEE 7th International Conference on Automatic Face and Gesture Recognition, Southampton, UK",2006,341-345,,Karl Ricanek Jr,Tamirat Tesafaye,,,,,,,,,
+MORPH commercial,MORPH: A Longitudinal Image Database of Normal Adult Age-Progression,"IEEE 7th International Conference on Automatic Face and Gesture Recognition, Southampton, UK",2006,341-345,,Karl Ricanek Jr,Tamirat Tesafaye,,,,,,,,,
+FaceScrub,A data-driven approach to cleaning large face datasets,Proc. IEEE International Conference on Image Processing (ICIP),2014,,,H.-W. Ng,S. Winkler,,,,,,,,,
+MIFS,Spoofing Faces Using Makeup: An Investigative Study,"Proc. of 3rd IEEE International Conference on Identity, Security and Behavior Analysis (ISBA), (New Delhi, India)",2017,,,C. Chen,A. Dantcheva,T. Swearingen,A. Ross,,,,,http://www.cse.msu.edu/~rossarun/pubs/ChenFaceMakeupSpoof_ISBA2017.pdf,,
+MIW,Automatic Facial Makeup Detection with Application in Face Recognition,"Proc. of 6th IAPR International Conference on Biometrics (ICB), (Madrid, Spain)",2013,,,C. Chen,A. Dantcheva,A. Ross,,,,,,https://www.cse.msu.edu/~rossarun/pubs/ChenMakeupDetection_ICB2013.pdf,,
+VMU,Can Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?,"Proc. of 5th IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), (Washington DC, USA)",2012,,,A. Dantcheva,C. Chen,A. Ross,,,,,,https://www.cse.msu.edu/~rossarun/pubs/DantchevaChenRossFaceCosmetics_BTAS2012.pdf,,
+YMU,Can Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?,"Proc. of 5th IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), (Washington DC, USA)",2012,,,A. Dantcheva,C. Chen,A. Ross,,,,,1,https://www.cse.msu.edu/~rossarun/pubs/DantchevaChenRossFaceCosmetics_BTAS2012.pdf,,
+YMU,Automatic Facial Makeup Detection with Application in Face Recognition,"Proc. of 6th IAPR International Conference on Biometrics (ICB), (Madrid, Spain)",2013,,,C. Chen,A. Dantcheva,A. Ross,,,,,2,https://www.cse.msu.edu/~rossarun/pubs/ChenMakeupDetection_ICB2013.pdf,,
+MsCeleb,MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition,European Conference on Computer Vision,2016,,,"Guo, Yandong","Zhang, Lei","Hu, Yuxiao","He, Xiaodong","Gao, Jianfeng",,,,https://www.microsoft.com/en-us/research/wp-content/uploads/2016/08/MSCeleb-1M-a.pdf,"@INPROCEEDINGS { guo2016msceleb,
+            author = {Guo, Yandong and Zhang, Lei and Hu, Yuxiao and He, Xiaodong and Gao, Jianfeng},
+            title = {M{S}-{C}eleb-1{M}: A Dataset and Benchmark for Large Scale Face Recognition},
+            booktitle = {European Conference on Computer Vision},
+            year = {2016},
+            organization={Springer}}",
+PIPA,Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues,arXiv:1501.05703 [cs.CV],2015,,,Ning Zhang, Manohar Paluri,Yaniv Taigman,Rob Fergus,Lubomir Bourdev,,,,https://arxiv.org/pdf/1501.05703.pdf,"@inproceedings{piper,
+        Author = {Ning Zhang and Manohar Paluri and Yaniv Taigman and Rob Fergus and Lubomir Bourdev},
+        Title = {Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues},
+        Eprint = {arXiv:1501.05703},
+        Year = {2015}}",
+PubFig,Attribute and Simile Classifiers for Face Verification,International Conference on Computer Vision (ICCV),2009,,,Neeraj Kumar,Alexander C. Berg,Peter N. Belhumeur,Shree K. Nayar,,,,,http://www.cs.columbia.edu/CAVE/publications/pdfs/Kumar_ICCV09.pdf,,
+SCUT-FBP,SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception,arXiv:1511.02459 [cs.CV],2015,,,Duorui Xie,Lingyu Liang,Lianwen Jin,Jie Xu,Mengru Li,,,,https://arxiv.org/ftp/arxiv/papers/1511/1511.02459.pdf,,
+RaFD ,Presentation and validation of the Radboud Faces Database,Cognition & Emotion,2010,1377-1388,24.8,"Langner, O.","Dotsch, R."," Bijlstra, G.","Wigboldus, D.H.J.","Hawk, S.T.","van Knippenberg, A.",,,http://dx.doi.org/10.1080/02699930903485076,DOI: 10.1080/02699930903485076,
+MUCT,The MUCT Landmarked Face Database,Pattern Recognition Association of South Africa,2010,,,,S. Milborrow,J. Morkel,F. Nicolls,,,,,http://www.milbo.org/muct/The-MUCT-Landmarked-Face-Database.pdf,"@article{Milborrow10,
+  author={S. Milborrow and J. Morkel and F. Nicolls},
+  title={{The MUCT Landmarked Face Database}},
+  journal={Pattern Recognition Association of South Africa},
+  year=2010,
+  note={\url{http://www.milbo.org/muct}}
+}",
+IJB-A,"Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A
+",Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2015,1931-1939,07-12-June-2015,"Klare, B. F.","Klein, B.","Taborsky, E.","Blanton, A.","Cheney, J.","Allen, K., ... Jain, A. K.",,,http://ieeexplore.ieee.org/document/7298803/,"DOI: 10.1109/CVPR.2015.7298803 @inbook{882e95bdca414797b4a8e2bfcb5b1fa4,
+title = ""Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A"",
+abstract = ""Rapid progress in unconstrained face recognition has resulted in a saturation in recognition accuracy for current benchmark datasets. While important for early progress, a chief limitation in most benchmark datasets is the use of a commodity face detector to select face imagery. The implication of this strategy is restricted variations in face pose and other confounding factors. This paper introduces the IARPA Janus Benchmark A (IJB-A), a publicly available media in the wild dataset containing 500 subjects with manually localized face images. Key features of the IJB-A dataset are: (i) full pose variation, (ii) joint use for face recognition and face detection benchmarking, (iii) a mix of images and videos, (iv) wider geographic variation of subjects, (v) protocols supporting both open-set identification (1:N search) and verification (1:1 comparison), (vi) an optional protocol that allows modeling of gallery subjects, and (vii) ground truth eye and nose locations. The dataset has been developed using 1,501,267 million crowd sourced annotations. Baseline accuracies for both face detection and face recognition from commercial and open source algorithms demonstrate the challenge offered by this new unconstrained benchmark."",
+author = ""Klare, {Brendan F.} and Ben Klein and Emma Taborsky and Austin Blanton and Jordan Cheney and Kristen Allen and Patrick Grother and Alan Mah and Mark Burge and Jain, {Anil K.}"",
+year = ""2015"",
+month = ""10"",
+doi = ""10.1109/CVPR.2015.7298803"",
+isbn = ""9781467369640"",
+volume = ""07-12-June-2015"",
+pages = ""1931--1939"",
+booktitle = ""Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition"",
+publisher = ""IEEE Computer Society"",
+
+}
+",
+FaceScrub,A data-driven approach to cleaning large face datasets,"Proc. IEEE International Conference on Image Processing (ICIP), Paris, France",2014,,,H.-W. Ng,S. Winkler,,,,,,,http://vintage.winklerbros.net/Publications/icip2014a.pdf,,
+Face Research Lab London Set,Face Research Lab London Set. figshare,,2017,,,"DeBruine, Lisa","Jones, Benedict",,,,,,,https://doi.org/10.6084/m9.figshare.5047666.v3,,
+CK,Comprehensive Database for Facial Expression Analysis,"Proceedings of the Fourth IEEE International Conferenc
+e on Automatic Face and Gesture Recognition 
+(FG'00)
+",2000,484-490,,"Kanade, T.","Cohn, J. F.","Tian, Y.",,,,,,http://www.pitt.edu/~jeffcohn/biblio/Cohn-Kanade_Database.pdf,,
+Columbia Gaze Data Set,Gaze Locking: Passive Eye Contact Detection for Human–Object Interaction,ACM Symposium on User Interface Software and Technology (UIST),2013,271-280,,B.A. Smith,Q. Yin,S.K. Feiner,S.K. Nayar,,,,,http://www.cs.columbia.edu/~brian/publications/gaze_locking.html,,
+UCF Selfie,"How to Take a Good Selfie?, in Proceedings of ACM Multimedia Conference 2015 (ACMMM 2015), Brisbane, Australia",,2015,,,Mahdi M. Kalayeh,Misrak Seifu,Wesna LaLanne,Mubarak Shah,,,,,,,
+UCF101,THUMOS Challenge: Action Recognition with a Large Number of Classes,,2015,,,"Gorban, A.","Idrees, H.","Jiang, Y.-G.","Roshan Zamir, A.","Laptev, I.","Shah, M. and Sukthankar, R.",,1,http://www.thumos.info/,"@misc{THUMOS15,
+   author = ""Gorban, A. and Idrees, H. and Jiang, Y.-G. and Roshan Zamir, A. and Laptev,
+   I. and Shah, M. and Sukthankar, R."",
+   title = ""{THUMOS} Challenge: Action Recognition with a Large
+   Number of Classes"",
+   howpublished = ""\url{http://www.thumos.info/}"",
+   Year = {2015}}",
+UCF101,UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild,CRCV-TR-12-01,2012,,,"Soomro, K.","Roshan Zamir, A.","Shah, M.",,,,,2,,"@inproceedings{UCF101,
+   author = {Soomro, K. and Roshan Zamir, A. and Shah, M.},
+   booktitle = {CRCV-TR-12-01},
+   title = {{UCF101}: A Dataset of 101 Human Actions Classes From
+   Videos in The Wild},
+   year = {2012}}",
+SVW,Sports Videos in the Wild (SVW): A Video Dataset for Sports Analysis,"Proc. International Conference on Automatic Face and Gesture Recognition (FG 2015), Ljubljana, Slovenia",2015,,,Seyed Morteza Safdarnejad, Xiaoming Liu, Lalita Udpa, Brooks Andrus,"John Wood,",Dean Craven,,,http://cvlab.cse.msu.edu/pdfs/Morteza_FG2015.pdf," @inproceedings{ sports-videos-in-the-wild-svw-a-video-dataset-for-sports-analysis,
+  author = { Seyed Morteza Safdarnejad and Xiaoming Liu and Lalita Udpa and Brooks Andrus and John Wood and Dean Craven },
+  title = { Sports Videos in the Wild (SVW): A Video Dataset for Sports Analysis },
+  booktitle = { Proc. International Conference on Automatic Face and Gesture Recognition },
+  address = { Ljubljana, Slovenia },
+  month = { May },
+  year = { 2015 },
+} ",
+CK+,The Extended Cohn-Kanade Dataset (CK+): A complete expression dataset for action unit and emotion-specified expression,"Proceedings of the Third International Workshop on CVPR for Human Communicative Behavior Analysis (CVPR4HB 2010), San Francisco, USA",2010,94-101,,"Ambadar, Z.","Cohn, J.F.","Kanade, T.","Lucey, P.","Matthews, I.A.","Saragih, J.M.",,,http://ieeexplore.ieee.org/document/5543262/,"@article{Lucey2010TheEC,
+  title={The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression},
+  author={Patrick Lucey and Jeffrey F. Cohn and Takeo Kanade and Jason M. Saragih and Zara Ambadar and Iain A. Matthews},
+  journal={2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops},
+  year={2010},
+  pages={94-101}
+}",
+Names and Faces,Names and Faces ,U.C. Berkeley Technical Report,Jan. 2007,,,Tamara L. Berg,Alexander C. Berg,Jaety Edwards,Michael Maire,Ryan White,"Yee Whye Teh, Erik Learned-Miller, David A. Forsyth",,1,http://www.cs.berkeley.edu/%7Eaberg/papers/journal_berg.pdf,,
+Names and Faces,Who's in the Picture ,NIPS,2004,,,Tamara L. Berg,Alexander C. Berg,Jaety Edwards,David A. Forsyth,,,,2,http://www.cs.berkeley.edu/%7Eaberg/papers/berg_whos_in_the_picture.pdf,,
+Names and Faces,Names and Faces in the News,"Computer Vision and Pattern Recognition (CVPR), Washington D.C.",2004,848-854,,Tamara L. Berg,Alexander C. Berg,Jaety Edwards,Michael Maire,Ryan White,"Yee Whye Teh, Erik Learned-Miller, David A. Forsyth",,3,http://www.cs.berkeley.edu/%7Eaberg/papers/berg_names_and_faces.pdf,,
+YaleFaces,Eigenfaces vs. fisherfaces: Recognition using class specific linear projection,"IEEE Transactions on Pattern Analysis and Machine Intelligence, Special Issue on Face Recognition",1997,711--720,17(7),P. N. Bellhumer,J. Hespanha,D. Kriegman,,,,,,,,
+Yale Face Database B,From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose,PAMI,2001,,,Athinodoros Georghiades,Peter Belhumeur,David Kriegman,,,,,,,,
+PIE,"The CMU Pose, Illumination, and Expression Database",IEEE Transactions on Pattern Analysis and Machine Intelligence,Dec 2003,"25, No. 12",,T. Sim,S. Baker,M. Bsat,,,,,,http://www.cs.cmu.edu/~simonb/pie_db/pami.pdf,,
+IMDB,Deep expectation of real and apparent age from a single image without facial landmarks,International Journal of Computer Vision (IJCV),Jul 2016,,,Rasmus Rothe,Radu Timofte,Luc Van Gool,,,,,1,,"@article{Rothe-IJCV-2016,
+  author = {Rasmus Rothe and Radu Timofte and Luc Van Gool},
+  title = {Deep expectation of real and apparent age from a single image without facial landmarks},
+  journal = {International Journal of Computer Vision (IJCV)},
+  year = {2016},
+  month = {July},
+}",
+IMDB,DEX: Deep EXpectation of apparent age from a single image,IEEE International Conference on Computer Vision Workshops (ICCVW),Dec 2015,,,Rasmus Rothe,Radu Timofte,Luc Van Gool,,,,,2,,"@InProceedings{Rothe-ICCVW-2015,
+  author = {Rasmus Rothe and Radu Timofte and Luc Van Gool},
+  title = {DEX: Deep EXpectation of apparent age from a single image},
+  booktitle = {IEEE International Conference on Computer Vision Workshops (ICCVW)},
+  year = {2015},
+  month = {December},
+}",
+\ No newline at end of file
diff --git a/scraper/datasets/citations-20181207.csv b/scraper/datasets/citations-20181207.csv
new file mode 100644
index 00000000..48a9ce2f
--- /dev/null
+++ b/scraper/datasets/citations-20181207.csv
@@ -0,0 +1,440 @@
+key,name,title,,,,Comments,,,,publication,day,month,year,pages,vol,author1,author2,author3,author4,author5,author6,funding1,funding2,funding3,funding4,priority,notes,pdf_filename,url,bibtex_copy
+10k_US_adult_faces,10K US Adult Faces,The intrinsic memorability of face images,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+3d_rma,3D-RMA,Automatic 3D Face Authentication,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+3dddb_unconstrained,3D Dynamic,A 3D Dynamic Database for Unconstrained Face Recognition,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+3dpes,3DPeS,3DPes: 3D People Dataset for Surveillance and Forensics,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+4dfab,4DFAB,4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+50_people_one_question,50 People One Question,Merging Pose Estimates Across Space and Time,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+a_pascal_yahoo,aPascal,Describing Objects by their Attributes,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+aberdeen ,Aberdeen,,,,,OK no paper,,,,,,,,,,,,,,,,,,,,,,,,
+adience,Adience,Age and Gender Estimation of Unfiltered Faces,,,,,,,,"Transactions on Information Forensics and Security (IEEE-TIFS), special issue on Facial Biometrics in the Wild",,,2014,2170 - 2179,9,Eran Eidinger,Roee Enbar, Tal Hassner,,,,,,,,,,,http://www.openu.ac.il/home/hassner/Adience/EidingerEnbarHassner_tifs.pdf,
+afad,AFAD,Ordinal Regression with a Multiple Output CNN for Age Estimation,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+afew_va,AFEW-VA,"AFEW-VA database for valence and arousal estimation in-the-wild
+",,,,"both paper refer to database. ""Collecting..."" describes how the database was created but the statistics we use are in ""afew-va..."". ",,,,IEEE MultiMedia,,,2012,pp. 34-41,"vol. 19, no. 3",,,,,,,,,,,,,"afew-va.pdf
+Dhall_Goecke_Lucey_Gedeon_M_2012.pdf",,
+afew_va,AFEW-VA,"Collecting Large, Richly Annotated Facial-Expression Databases from Movies",,,,,,,,,,,,,,,,,,,,,,,,,,,,
+affectnet,AffectNet,"AffectNet: A New Database for Facial Expression, Valence, and Arousal Computation in the Wild",,,,,,,,,,,,,,,,,,,,,,,,,,,,
+aflw,AFLW,"Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization",,,,,,,,,,,,,,Martin Koestinger,Paul Wohlhart,Peter M. Roth,Horst Bischof,,,,,,,,,koestinger_befit_11.pdf,https://files.icg.tugraz.at/seafhttp/files/d18813db-78c3-46a9-8614-bc0c8d428114/koestinger_befit_11.pdf,"@INPROCEEDINGS{koestinger11a,
+  author = {Martin Koestinger, Paul Wohlhart, Peter M. Roth and Horst Bischof},
+  title = {{Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization}},
+  booktitle = {{Proc. First IEEE International Workshop on Benchmarking Facial Image Analysis Technologies}},
+  year = {2011}
+} "
+afw,AFW,"Face detection, pose estimation and landmark localization in the wild",,,,,,,,"Computer Vision and Pattern Recognition (CVPR) Providence, Rhode Island,",,,2012,,,X. Zhu,D. Ramanan,,,,,,,,,,,,http://www.ics.uci.edu/~xzhu/paper/face-cvpr12.pdf,
+agedb,AgeDB,"AgeDB: the first manually collected, in-the-wild age database",,,,,,,,Proceedings of IEEE Int’l Conf. on Computer Vision and Pattern Recognition (CVPR-W 2017,,,2017,,,S. Moschoglou,A. Papaioannou,C. Sagonas,J. Deng,I. Kotsia, S. Zafeiriou,,,,,,,agedb.pdf,http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf,"@inproceedings{AgeDB,
+    author = {S. Moschoglou and A. Papaioannou and C. Sagonas and J. Deng and I. Kotsia and S. Zafeiriou},
+    address = {Honolulu, Hawaii},
+    booktitle = {Proceedings of IEEE Int’l Conf. on Computer Vision and Pattern Recognition (CVPR-W 2017)},
+    month = {June},
+    title = {AgeDB: the first manually collected, in-the-wild age database},
+    year = {2017},
+}"
+alert_airport,ALERT Airport,"A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets",,,,,,,,,,,,,,,,,,,,,,,,,,,,
+am_fed,AM-FED,Affectiva MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected “In the Wild”,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+apis,APiS1.0,Pedestrian Attribute Classification in Surveillance: Database and Evaluation,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ar_facedb,AR Face,The AR Face Database,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+awe_ears,AWE Ears,Ear Recognition: More Than a Survey,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+b3d_ac,B3D(AC),A 3-D Audio-Visual Corpus of Affective Communication,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+bbc_pose,BBC Pose,Automatic and Efficient Human Pose Estimation for Sign Language Videos ,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+berkeley_pose,BPAD,Describing People: A Poselet-Based Approach to Attribute Classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+bfm,BFM,A 3D Face Model for Pose and Illumination Invariant Face Recognition,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+bio_id,BioID Face,Robust Face Detection Using the Hausdorff Distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+bjut_3d,BJUT-3D,The BJUT-3D Large-Scale Chinese Face Database,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+bosphorus,The Bosphorus,Bosphorus Database for 3D Face Analysis,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+bp4d_plus,BP4D+,Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+bp4d_spontanous,BP4D-Spontanous,A high resolution spontaneous 3D dynamic facial expression database,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+brainwash,Brainwash,Brainwash dataset,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+bu_3dfe,BU-3DFE,A 3D Facial Expression Database For Facial Behavior Research,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+buhmap_db,BUHMAP-DB ,Facial Feature Tracking and Expression Recognition for Sign Language,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+cafe,CAFE,The Child Affective Facial Expression (CAFE) Set: Validity and reliability from untrained adults,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+caltech_10k_web_faces,Caltech 10K Web Faces, Pruning Training Sets for Learning of Object Categories,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+caltech_faces,Caltech Faces,,,,,OK no paper,,,,,,,,,,,,,,,,,,,,,,,,
+caltech_pedestrians,Caltech Pedestrians,Pedestrian Detection: A Benchmark,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+caltech_pedestrians,Caltech Pedestrians,Pedestrian Detection: An Evaluation of the State of the Art,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+camel,CAMEL,CAMEL Dataset for Visual and Thermal Infrared Multiple Object Detection and Tracking,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+cas_peal,CAS-PEAL,The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations ,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+casablanca,Casablanca,Context-aware {CNNs} for person head detection,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+casia_webface,CASIA Webface,Learning Face Representation from Scratch,,,,,,,,arXiv preprint arXiv:1411.7923.,,,2014,,,Dong Yi,Zhen Lei, Shengcai Liao,Stan Z. Li,,,,,,,,,1411.7923.pdf,https://arxiv.org/abs/1411.7923,
+caviar4reid,CAVIAR4REID,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+celeba,CelebA,Deep Learning Face Attributes in the Wild,,,,,,,,"in IEEE International Conference on Computer Vision (ICCV),",,,2015,,,S. Yang,P. Luo,C. C. Loy,X. Tang,,,,,,,,,Liu_Deep_Learning_Face_ICCV_2015_paper.pdf,https://arxiv.org/abs/1509.06451,"@inproceedings{liu2015faceattributes,
+ author = {Ziwei Liu and Ping Luo and Xiaogang Wang and Xiaoou Tang},
+ title = {Deep Learning Face Attributes in the Wild},
+ booktitle = {Proceedings of International Conference on Computer Vision (ICCV)},
+ month = December,
+ year = {2015}
+}"
+celeba_plus,CelebFaces+,"Deep Learning Face Representation from Predicting 10,000 Classes",,,,,,,,,,,,,,,,,,,,,,,,,,,,
+cfd,CFD,The Chicago face database: A free stimulus set of faces and norming data,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+chalearn,ChaLearn,ChaLearn Looking at People: A Review of Events and Resources,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+chokepoint,ChokePoint,Patch-based Probabilistic Image Quality Assessment for Face Selection and Improved Video-based Face Recognition ,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+cityscapes,Cityscapes,The Cityscapes Dataset for Semantic Urban Scene Understanding,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+cityscapes,Cityscapes,The Cityscapes Dataset,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+clothing_co_parsing,CCP,Clothing Co-Parsing by Joint Image Segmentation and Labeling,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+cmdp,CMDP,Distance Estimation of an Unknown Person from a Portrait,,,,,,,,ECCV 2014,,,2014,,,X. P. Burgos-Artizzu,M.R. Ronchi,P. Perona,,,,,,,,,,ECCV14_FaceDistancePortrait_PAPER.pdf,http://www.vision.caltech.edu/~mronchi/papers/ECCV14_FaceDistancePortrait_PAPER.pdf,"@incollection{perona2014PortraitDistanceEstimation,
+  title={Distance Estimation of an Unknown Person from a Portrait},
+  author={Xavier P. Burgos-Artizzu, Matteo Ruggero Ronchi and Pietro Perona},
+  booktitle={Computer Vision--ECCV 2014},
+  pages={313--327},
+  year={2014},
+  publisher={Springer}
+}"
+cmu_pie,CMU PIE,"The CMU Pose, Illumination, and Expression Database",,,,,,,,IEEE Transactions on Pattern Analysis and Machine Intelligence,,12,2003,"25, No. 12",,T. Sim,S. Baker,M. Bsat,,,,,,,,,,,http://www.cs.cmu.edu/~simonb/pie_db/pami.pdf,
+coco,COCO,Microsoft COCO: Common Objects in Context,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+coco_action,COCO-a,Describing Common Human Visual Actions in Images,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+coco_qa,COCO QA,Exploring Models and Data for Image Question Answering,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+cofw,COFW,Robust face landmark estimation under occlusion,,,,Paper for RCPR method includes creation of COFW dataset,,,,"ICCV 2013, Sydney, Australia",,,2013,,,X. P. Burgos-Artizzu,P. Perona,P. Dollár,,,,,,,,,,ICCV13 Burgos-Artizzu.pdf,http://www.vision.caltech.edu/%7Expburgos/papers/ICCV13%20Burgos-Artizzu.pdf,
+cohn_kanade,CK,Comprehensive Database for Facial Expression Analysis,,,,,,,,"Proceedings of the Fourth IEEE International Conferenc
+e on Automatic Face and Gesture Recognition 
+(FG'00)
+",,,2000,484-490,,"Kanade, T.","Cohn, J. F.","Tian, Y.",,,,,,,,,,download.pdf,http://www.pitt.edu/~jeffcohn/biblio/Cohn-Kanade_Database.pdf,
+cohn_kanade_plus,CK+,The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression,,,,,,,,"Proceedings of the Third International Workshop on CVPR for Human Communicative Behavior Analysis (CVPR4HB 2010), San Francisco, USA",,,2010,94-101,,"Ambadar, Z.","Cohn, J.F.","Kanade, T.","Lucey, P.","Matthews, I.A.","Saragih, J.M.",,,,,,,paper.pdf,https://ieeexplore.ieee.org/document/5543262,"@article{Lucey2010TheEC,
+  title={The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression},
+  author={Patrick Lucey and Jeffrey F. Cohn and Takeo Kanade and Jason M. Saragih and Zara Ambadar and Iain A. Matthews},
+  journal={2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops},
+  year={2010},
+  pages={94-101}
+}"
+columbia_gaze,Columbia Gaze,Gaze Locking: Passive Eye Contact Detection for Human–Object Interaction,,,,,,,,ACM Symposium on User Interface Software and Technology (UIST),,,2013,271-280,,B.A. Smith,Q. Yin,S.K. Feiner,S.K. Nayar,,,,,,,,,p271-smith.pdf,http://www.cs.columbia.edu/~brian/publications/gaze_locking.html,
+complex_activities,Ongoing Complex Activities,Recognition of Ongoing Complex Activities by Sequence Prediction over a Hierarchical Label Space,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+cuhk01,CUHK01,Human Reidentification with Transferred Metric Learning,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+cuhk02,CUHK02,Locally Aligned Feature Transforms across Views,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+cuhk03,CUHK03,DeepReID: Deep Filter Pairing Neural Network for Person Re-identification,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+cvc_01_barcelona,CVC-01,Adaptive Image Sampling and Windows Classification for On-board Pedestrian Detection,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+czech_news_agency,UFI,Unconstrained Facial Images: Database for Face Recognition under Real-world Conditions,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+d3dfacs,D3DFACS,A FACS Valid 3D Dynamic Action Unit database with Applications to 3D Dynamic Morphable Facial Modelling,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+dartmouth_children,Dartmouth Children,The Dartmouth Database of Children's Faces: Acquisition and validation of a new face stimulus set,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+data_61,Data61 Pedestrian,A Multi-Modal Graphical Model for Scene Analysis,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+deep_fashion,DeepFashion,DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+deep_fashion,DeepFashion,Fashion Landmark Detection in the Wild,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+disfa,DISFA,DISFA: A Spontaneous Facial Action Intensity Database,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+distance_nighttime,Long Distance Heterogeneous Face,Nighttime Face Recognition at Long Distance: Cross-distance and Cross-spectral Matching,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+duke_mtmc,Duke MTMC,"Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking",,,,,,,,,,,,,,,,,,,,,,,,,,,,
+emotio_net,EmotioNet Database,"EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild",,,,,,,,,,,,,,,,,,,,,,,,,,,,
+eth_andreas_ess,ETHZ Pedestrian,Depth and Appearance for Mobile Scene Analysis,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+europersons,EuroCity Persons,The EuroCity Persons Dataset: A Novel Benchmark for Object Detection,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+expw,ExpW,Learning Social Relation Traits from Face Images,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+expw,ExpW,From Facial Expression Recognition to Interpersonal Relation Prediction,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+face_research_lab,Face Research Lab London,Face Research Lab London Set. figshare,,,,OK No paper (not even on internet?),,,,,,,2017,,,"DeBruine, Lisa","Jones, Benedict",,,,,,,,,,,,https://doi.org/10.6084/m9.figshare.5047666.v3,
+face_scrub,FaceScrub,A data-driven approach to cleaning large face datasets,,,,,,,,Proc. IEEE International Conference on Image Processing (ICIP),,,2014,,,H.-W. Ng,S. Winkler,,,,,,,,,,,icip2014a.pdf,http://vintage.winklerbros.net/Publications/icip2014a.pdf,
+face_tracer,FaceTracer,FaceTracer: A Search Engine for Large Collections of Images with Faces,,,,,,,,European Conference on Computer Vision (ECCV),,,2008,340-353,,N. Kumar,P. N. Belhumeur,S. K. Nayar,,,,,,,,1,,Kumar_ECCV08.pdf,http://www1.cs.columbia.edu/CAVE/publications/pdfs/Kumar_ECCV08.pdf,
+face_tracer,FaceTracer,Face Swapping: Automatically Replacing Faces in Photographs,,,,,,,,ACM Trans. on Graphics (also Proc. of ACM SIGGRAPH),,,2008,,,D. Bitouk,N. Kumar,S. Dhillon,P.N. Belhumeur,S. K. Nayar,,,,,,2,,Bitouk_SIGGRAPH08.pdf,http://www1.cs.columbia.edu/CAVE/publications/pdfs/Bitouk_SIGGRAPH08.pdf,
+facebook,SFC,,,,,"OK no paper, private",,,,,,,,,,,,,,,,,,,,,,,,
+facebook_100,Facebook100,Scaling Up Biologically-Inspired Computer Vision: A Case Study in Unconstrained Face Recognition on Facebook,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+faceplace,Face Place,Recognizing disguised faces,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+faces94,Faces94,,,,,OK no paper,,,,,,,,,,,,,,,,,,,,,,,,
+faces95,Faces95,,,,,OK no paper,,,,,,,,,,,,,,,,,,,,,,,,
+faces96,Faces96,,,,,OK no paper,,,,,,,,,,,,,,,,,,,,,,,,
+families_in_the_wild,FIW,Visual Kinship Recognition of Families in the Wild,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+fddb,FDDB,FDDB: A Benchmark for Face Detection in Unconstrained Settings,,,,,,,,"Technical Report UM-CS-2010-009, Dept. of Computer Science, University of Massachusetts",,,2010,,,Vidit Jain,Erik Learned-Mille,,,,,,,,,,,fddb.pdf,http://vis-www.cs.umass.edu/fddb/fddb.pdf,"@TechReport{fddbTech,
+  author = {Vidit Jain and Erik Learned-Miller},
+  title =  {FDDB: A Benchmark for Face Detection in Unconstrained Settings},
+  institution =  {University of Massachusetts, Amherst},
+  year = {2010},
+  number = {UM-CS-2010-009}
+  }"
+fei,FEI,Captura e Alinhamento de Imagens: Um Banco de Faces Brasileiro,,,,"in portugese, but original paper",,,,,,,,,,,,,,,,,,,,,,,,
+feret,FERET,The FERET Verification Testing Protocol for Face Recognition Algorithms,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+feret,FERET,The FERET database and evaluation procedure for face-recognition algorithms,,,,paper not in nextcloud,,,,,,,,,,,,,,,,,,,,,,,,
+feret,FERET,FERET ( Face Recognition Technology ) Recognition Algorithm Development and Test Results,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+feret,FERET,The FERET Evaluation Methodology for Face-Recognition Algorithms,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ferplus,FER+,Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+fia,CMU FiA,The CMU Face In Action (FIA) Database,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+fiw_300,300-W,300 faces In-the-wild challenge: Database and results,,,,,,,,"Image and Vision Computing (IMAVIS), Special Issue on Facial Landmark Localisation ""In-The-Wild""",,,2016,,,C. Sagonas,E. Antonakos,"G, Tzimiropoulos",S. Zafeiriou,M. Pantic,,,,,,1,,,,
+fiw_300,300-W,300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge,,,,,,,,"Proceedings of IEEE Int’l Conf. on Computer Vision (ICCV-W), 300 Faces in-the-Wild Challenge (300-W). Sydney, Australia",,,2013,,,C. Sagonas,G. Tzimiropoulos,S. Zafeiriou,M. Pantic,,,,,,,2,,,,
+fiw_300,300-W,A semi-automatic methodology for facial landmark annotation,,,,,,,,"Proceedings of IEEE Int’l Conf. Computer Vision and Pattern Recognition (CVPR-W), 5th Workshop on Analysis and Modeling of Faces and Gestures (AMFG 2013). Oregon, USA,",,,2013,,,C. Sagonas,G. Tzimiropoulos,S. Zafeiriou,M. Pantic,,,,,,,3,,,,
+florida_inmates,Florida Inmate,,,,,"OK no paper, not official database",,,,,,,,,,,,,,,,,,,,,,,,
+frav2d,FRAV2D,,,,,OK no paper,,,,,,,,,,,,,,,,,,,,,,,,
+frav3d,FRAV3D,"MULTIMODAL 2D, 2.5D & 3D FACE VERIFICATION",,,,,,,,,,,,,,,,,,,,,,,,,,,,
+frgc,FRGC,Overview of the Face Recognition Grand Challenge,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+gallagher,Gallagher,Clothing Cosegmentation for Recognizing People,,,,,,,,IEEE Conference on Computer Vision and Pattern Recognition,,,2008,,,Andrew Gallagher,Tsuhan Chen,,,,,,,,,,,141.pdf,,
+gavab_db,Gavab,GavabDB: a 3D face database,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+geofaces,GeoFaces,GeoFaceExplorer: Exploring the Geo-Dependence of Facial Attributes,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+georgia_tech_face_database,Georgia Tech Face,Maximum likelihood training of the embedded HMM for face detection and recognition,,,,"I think this is the correct paper – database was colected 1999, this is 2000",,,,,,,,,,,,,,,,,,,,,,,,
+gmu,Google Makeup,Parallel Optimized Pearson Correlation Condition (PO-PCC) for Robust Cosmetic Makeup Facial Recognition,,,,watermarked online publication,,,,,,,,,,,,,,,,,,,,,,,,
+google,Google (private),,,,,"OK no paper, private",,,,,,,,,,,,,,,,,,,,,,,,
+graz,Graz Pedestrian,Generic Object Recognition with Boosting,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+graz,Graz Pedestrian,Weak Hypotheses and Boosting for Generic Object Detection and Recognition,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+graz,Graz Pedestrian,Object Recognition Using Segmentation for Feature Detection,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+grimace,GRIMACE,,,,,OK no paper,,,,,,,,,,,,,,,,,,,,,,,,
+h3d,H3D,Poselets: Body Part Detectors Trained Using 3D Human Pose Annotations,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+hda_plus,HDA+,The HDA+ data set for research on fully automated re-identification systems,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+hda_plus,HDA+,A Multi-camera video data set for research on High-Definition surveillance,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+helen,Helen,Interactive Facial Feature Localization,,,,,,,,ECCV,,,2012,,,Vuong Le,Jonathan Brandt,Zhe Lin,Lubomir Boudev,Thomas S. Huang,,,,,,,,eccv2012_helen_final.pdf,http://www.ifp.illinois.edu/~vuongle2/helen/eccv2012_helen_final.pdf,
+hi4d_adsip,Hi4D-ADSIP,Hi4D-ADSIP 3-D dynamic facial articulation database,,,,paper?,,,,,,,,,,,,,,,,,,,,,,,,
+hid_equinox_infrared,HID,,,,,no paper,,,,,,,,,,,,,,,,,,,,,,,,
+hipsterwars,Hipsterwars,Hipster Wars: Discovering Elements of Fashion Styles,,,,,,,,In European Conference on Computer Vision,,,2014,,,M. Hadi Kiapour,Kota Yamaguchi,Alexander C. Berg,Tamara L. Berg,,,,,,,,,hipster_eccv14.pdf,http://tamaraberg.com/papers/hipster_eccv14.pdf,"@inproceedings{
+            HipsterWarsECCV14,
+            title     = {Hipster Wars: Discovering Elements of Fashion Styles}
+            author    = {M. Hadi Kiapour, Kota Yamaguchi, Alexander C. Berg, Tamara L. Berg},
+            booktitle={European Conference on Computer Vision},
+            year      = {2014}
+            }"
+hollywood_headset,HollywoodHeads,Context-aware CNNs for person head detection,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+hrt_transgender,HRT Transgender,Is the Eye Region More Reliable Than the Face? A Preliminary Study of Face-based Recognition on a Transgender Dataset,,,,,,,,"In Proc. of IEEE Intl. Conf. on Biometrics: Theory, Applications, and Systems",,,2013,,,Gayathri Mahalingam,Karl Ricanek Jr.,,,,,,,,,,,,https://pdfs.semanticscholar.org/b066/733d533250f4ddafd22c12456def7fa24f4c.pdf,
+hrt_transgender,HRT Transgender,Investigating the Periocular-Based Face Recognition Across Gender Transformation,,,,,,,,IEEE Trans. On Information Forensics and Security,,,2014,pp. 2180 – 2192,"vol. 9, no. 12",Gayathri Mahalingam,Karl Ricanek Jr.,Midori M. Albert,,,,,,,,,,,https://ieeexplore.ieee.org/document/6915725,
+hrt_transgender,HRT Transgender,Face recognition across gender transformation using SVM Classifier,,,,"Paper used for statistics, not mentionned in citations",,,,,,,,,,,,,,,,,,,,,,Face_Recognition_Across_Gender_Transformation_Usin.pdf,,
+ifad,IFAD,Indian Face Age Database: A Database for Face Recognition with Age Variation,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ifdb,IFDB,"Iranian Face Database with age, pose and expression",,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ifdb,IFDB,Iranian Face Database and Evaluation with a New Detection Algorithm,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+iit_dehli_ear,IIT Dehli Ear,Automated human identification using ear imaging,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ijb_a,IJB-A,Pushing the Frontiers of Unconstrained Face Detection and Recognition: IARPA Janus Benchmark A ,,,,,,,,Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition,,,2015,1931-1939,07-12-June-2015,"Klare, B. F.","Klein, B.","Taborsky, E.","Blanton, A.","Cheney, J.","Allen, K., ... Jain, A. K.",,,,,,,Klare_Pushing_the_Frontiers_2015_CVPR_paper.pdf,http://ieeexplore.ieee.org/document/7298803/,"DOI: 10.1109/CVPR.2015.7298803 @inbook{882e95bdca414797b4a8e2bfcb5b1fa4,
+title = ""Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A"",
+abstract = ""Rapid progress in unconstrained face recognition has resulted in a saturation in recognition accuracy for current benchmark datasets. While important for early progress, a chief limitation in most benchmark datasets is the use of a commodity face detector to select face imagery. The implication of this strategy is restricted variations in face pose and other confounding factors. This paper introduces the IARPA Janus Benchmark A (IJB-A), a publicly available media in the wild dataset containing 500 subjects with manually localized face images. Key features of the IJB-A dataset are: (i) full pose variation, (ii) joint use for face recognition and face detection benchmarking, (iii) a mix of images and videos, (iv) wider geographic variation of subjects, (v) protocols supporting both open-set identification (1:N search) and verification (1:1 comparison), (vi) an optional protocol that allows modeling of gallery subjects, and (vii) ground truth eye and nose locations. The dataset has been developed using 1,501,267 million crowd sourced annotations. Baseline accuracies for both face detection and face recognition from commercial and open source algorithms demonstrate the challenge offered by this new unconstrained benchmark."",
+author = ""Klare, {Brendan F.} and Ben Klein and Emma Taborsky and Austin Blanton and Jordan Cheney and Kristen Allen and Patrick Grother and Alan Mah and Mark Burge and Jain, {Anil K.}"",
+year = ""2015"",
+month = ""10"",
+doi = ""10.1109/CVPR.2015.7298803"",
+isbn = ""9781467369640"",
+volume = ""07-12-June-2015"",
+pages = ""1931--1939"",
+booktitle = ""Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition"",
+publisher = ""IEEE Computer Society"",
+
+}
+"
+ijb_b,IJB-B,IARPA Janus Benchmark-B Face Dataset,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ijb_c,IJB-C,IARPA Janus Benchmark C,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ilids_mcts,,"Imagery Library for Intelligent Detection Systems: 
+The i-LIDS User Guide",,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ilids_vid_reid,iLIDS-VID,Person Re-Identication by Video Ranking,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+images_of_groups,Images of Groups,Understanding Groups of Images of People,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+imdb_wiki,IMDB,Deep expectation of real and apparent age from a single image without facial landmarks,,,,,,,,International Journal of Computer Vision (IJCV),,6,2016,,,Rasmus Rothe,Radu Timofte,Luc Van Gool,,,,,,,,1,,eth_biwi_01299.pdf,,"@article{Rothe-IJCV-2016,
+  author = {Rasmus Rothe and Radu Timofte and Luc Van Gool},
+  title = {Deep expectation of real and apparent age from a single image without facial landmarks},
+  journal = {International Journal of Computer Vision (IJCV)},
+  year = {2016},
+  month = {July},
+}"
+imdb_wiki,IMDB,DEX: Deep EXpectation of apparent age from a single image,,,,,,,,IEEE International Conference on Computer Vision Workshops (ICCVW),,12,2015,,,Rasmus Rothe,Radu Timofte,Luc Van Gool,,,,,,,,2,,eth_biwi_01229.pdf,,"@InProceedings{Rothe-ICCVW-2015,
+  author = {Rasmus Rothe and Radu Timofte and Luc Van Gool},
+  title = {DEX: Deep EXpectation of apparent age from a single image},
+  booktitle = {IEEE International Conference on Computer Vision Workshops (ICCVW)},
+  year = {2015},
+  month = {December},
+}"
+imfdb,IMFDB,Indian Movie Face Database: A Benchmark for Face Recognition Under Wide Variations,,,,,,,,"National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)",,,2013,,,Shankar Setty,et al,,,,,,,,,,,imfdb.pdf,http://cvit.iiit.ac.in/projects/IMFDB/imfdb.pdf,"@InProceedings{imfdb,
+author    = {Shankar Setty, Moula Husain, Parisa Beham, Jyothi Gudavalli, Menaka Kandasamy, Radhesyam Vaddi,              Vidyagouri Hemadri, J C Karure, Raja Raju, Rajan, Vijay Kumar and C V Jawahar},
+title     = {{I}ndian {M}ovie {F}ace {D}atabase: {A} {B}enchmark for {F}ace {R}ecognition {U}nder {W}ide              {V}ariations},
+booktitle = {National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics              (NCVPRIPG)},
+month     = {Dec},
+year      = {2013}
+} "
+imm_face,IMM Face Dataset,The IMM Face Database - An Annotated Dataset of 240 Face Images,,,,,,,,"Informatics and Mathematical Modelling, Technical University of Denmark, DTU",,5,2004,,,Michael M. Nordstrøm,Mads Larsen,Janusz Sierakowski,Mikkel B. Stegmann,,,,,,,,,imm3160.pdf,,
+immediacy,Immediacy,Multi-task Recurrent Neural Network for Immediacy Prediction,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+imsitu,imSitu,Situation Recognition: Visual Semantic Role Labeling for Image Understanding,,,,,,,,"(1) Computer Science & Engineering, University of Washington, Seattle, WA
+(2) Allen Institute for Artificial Intelligence (AI2), Seattle, WA",,,,,,Mark Yatskar,Luke Zettlemoyer,Ali Farhadi,,,,,,,,,,situations.pdf,https://homes.cs.washington.edu/~my89/publications/situations.pdf,
+inria_person,INRIA Pedestrian,Histograms of Oriented Gradients for Human Detection,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+iqiyi,iQIYI-VID dataset ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+jaffe,JAFFE,Coding Facial Expressions with Gabor Wavelets,,,,,,,,3rd IEEE International Conference on Automatic Face and Gesture Recognition,,,1998,200-205,,Michael J. Lyons,Shigeru Akemastu,Miyuki Kamachi,Jiro Gyoba,,,,,,,,,fg98-1.pdf,http://www.kasrl.org/fg98-1.pdf,
+jiku_mobile,Jiku Mobile Video Dataset,The Jiku Mobile Video Dataset,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+jpl_pose,JPL-Interaction dataset,First-Person Activity Recognition: What Are They Doing to Me?,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+karpathy_instagram,Karpathy Instagram,,,,,OK no paper,,,,,,,,,,,,,,,,,,,,,,,,
+kdef,KDEF,The Karolinska Directed Emotional Faces – KDEF,,,,"this is the original paper form 1998 with this title, couldn't find it though, so not in nextcloud folder",,,,,,,,,,,,,,,,,,,,,,,,
+kin_face,UB KinFace,Genealogical Face Recognition based on UB KinFace Database,,,,"this is the original paper title, couldn't find it though, so not in nextcloud folder",,,,,,,,,,,,,,,,,,,,,,,,
+kin_face,UB KinFace,Kinship Verification through Transfer Learning,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+kin_face,UB KinFace,Understanding Kin Relationships in a Photo,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+kinectface,KinectFaceDB,KinectFaceDB: A Kinect Database for Face Recognition,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+kitti,KITTI,Vision meets Robotics: The KITTI Dataset,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+lag,LAG,Large Age-Gap Face Verification by Feature Injection in Deep Networks,,,,,,,,Pattern Recognition Letters,,,2017,36-42,90,Simone Bianco,,,,,,,,,,,,bianco2017large-age.pdf,http://www.ivl.disco.unimib.it/activities/large-age-gap-face-verification/,"@article{bianco2017large-age,
+  author = {Bianco, Simone},
+  year = {2017},
+  pages = {36-42},
+  title = {Large Age-Gap Face Verification by Feature Injection in Deep Networks},
+  volume = {90},
+  journal = {Pattern Recognition Letters},
+  doi = {10.1016/j.patrec.2017.03.006}}"
+large_scale_person_search,Large Scale Person Search,End-to-End Deep Learning for Person Search,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+leeds_sports_pose,Leeds Sports Pose,Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+leeds_sports_pose_extended,Leeds Sports Pose Extended,Learning Effective Human Pose Estimation from Inaccurate Annotation,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+lfw,LFW,Labeled Faces in the Wild: A Survey,,,,,,,, ,,,,,,,,,,,,,,,,,,,,
+lfw,LFW,Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments,,,,,,,,"University of Massachusetts, Amherst, Technical Report ",,,2007,07-49,,Gary B. Huang,Manu Ramesh,Tamara Berg,Erik Learned-Miller,,,,,,,,various citaton depending on various datasets provided. Citation used here was first one published in 2007,lfw.pdf,http://vis-www.cs.umass.edu/lfw/lfw.pdf,
+lfw,LFW,Labeled Faces in the Wild: Updates and New Reporting Procedures,,,,,,,, ,,,,,,,,,,,,,,,,,,,,
+lfw_a,LFW-a,"Effective Unconstrained Face Recognition by
+ Combining Multiple Descriptors and Learned
+ Background Statistics",,,,,,,,"IEEE Trans. on Pattern Analysis and Machine Intelligence (TPAMI), 33(10),",,,2011,,,Lior Wolf,Tal Hassner,Yaniv Taigman,,,,,,,,,,jpatchlbp.pdf,http://www.openu.ac.il/home/hassner/projects/Patchlbp/WolfHassnerTaigman_TPAMI11.pdf,Comply with any instructions specified for the original LFW data set
+lfw_p,LFWP,Localizing Parts of Faces Using a Consensus of Exemplars,,,,,,,,Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition (CVPR),,,2011,,,Peter N. Belhumeur,"David W. Jacobs,",David J. Kriegman,Neeraj Kumar,,,,,,,,,nk_cvpr2011_faceparts.pdf,http://neerajkumar.org/projects/face-parts/base/papers/nk_cvpr2011_faceparts.pdf,
+m2vts,m2vts,The M2VTS Multimodal Face Database (Release 1.00),,,,,,,,,,,,,,,,,,,,,,,,,,,,
+m2vtsdb_extended,xm2vtsdb,XM2VTSDB: The Extended M2VTS Database,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+mafl,MAFL,Facial Landmark Detection by Deep Multi-task Learning,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+mafl,MAFL,Learning Deep Representation for Face Alignment with Auxiliary Attributes,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+malf,MALF,Fine-grained Evaluation on Face Detection in the Wild.,,,,,,,,Proceedings of the 11th IEEE International Conference on Automatic Face and Gesture Recognition Conference and Workshops.,,,2015,,,Bin Yang*,Junjie Yan*,Zhen Lei,Stan Z. Li,,,,,,,,,faceevaluation15.pdf,http://www.cbsr.ia.ac.cn/faceevaluation/faceevaluation15.pdf,"@inproceedings{faceevaluation15,
+title={Fine-grained Evaluation on Face Detection in the Wild},
+author={Yang, Bin and Yan, Junjie and Lei, Zhen and Li, Stan Z},
+booktitle={Automatic Face and Gesture Recognition (FG), 11th IEEE International 
+Conference on},
+year={2015},
+organization={IEEE}
+}"
+mapillary,Mapillary,The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+market_1501,Market 1501,Scalable Person Re-identification: A Benchmark,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+market1203,Market 1203,Orientation Driven Bag of Appearances for Person Re-identification,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+mars,MARS,MARS: A Video Benchmark for Large-Scale Person Re-identification,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+mcgill,McGill Real World,Hierarchical Temporal Graphical Model for Head Pose Estimation and Subsequent Attribute Classification in Real-World Videos,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+mcgill,McGill Real World,Robust Semi-automatic Head Pose Labeling for Real-World Face Video Sequences,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+meds,Multiple Encounter Dataset,,,,,OK no paper,,,,,,,,,,,,,,,,,,,,,,,,
+megaage,MegaAge,Quantifying Facial Age by Posterior of Age Comparisons,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+megaface,MegaFace,The MegaFace Benchmark: 1 Million Faces for Recognition at Scale ,,,,The 2 papers refer to respectively: MF and MF2 ,,,,IEEE Conference on Computer Vision and Pattern Recognition (CVPR),,,2017,,,"Nech, Aaron","Kemelmacher-Shlizerman, Ira",,,,,,,,,If you're participating or using data from Challenge 2 please cite:,,1705.00393.pdf,https://homes.cs.washington.edu/~kemelmi/ms.pdf,"@inproceedings{nech2017level,
+title={Level Playing Field For Million Scale Face Recognition},
+author={Nech, Aaron and Kemelmacher-Shlizerman, Ira},
+booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
+year={2017}
+}"
+megaface,MegaFace,Level Playing Field for Million Scale Face Recognition ,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+mifs,MIFS,Spoofing Faces Using Makeup: An Investigative Study,,,,,,,,"Proc. of 3rd IEEE International Conference on Identity, Security and Behavior Analysis (ISBA), (New Delhi, India)",,,2017,,,C. Chen,A. Dantcheva,T. Swearingen,A. Ross,,,,,,,,,,http://www.cse.msu.edu/~rossarun/pubs/ChenFaceMakeupSpoof_ISBA2017.pdf,
+mikki,MIKKI dataset,,,,,OK no paper,,,,,,,,,,,,,,,,,,,,,,,,
+mit_cbcl,MIT CBCL,Component-based Face Recognition with 3D Morphable Models,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+mit_cbcl_ped,CBCL,,,,,OK no paper,,,,,,,,,,,,,,,,,,,,,,,,
+mit_cbclss,CBCLSS,,,,,OK no paper,,,,,,,,,,,,,,,,,,,,,,,,
+miw,MIW,Automatic Facial Makeup Detection with Application in Face Recognition,,,,,,,,"Proc. of 6th IAPR International Conference on Biometrics (ICB), (Madrid, Spain)",,,2013,,,C. Chen,A. Dantcheva,A. Ross,,,,,,,,,,,https://www.cse.msu.edu/~rossarun/pubs/ChenMakeupDetection_ICB2013.pdf,
+mmi_facial_expression,MMI Facial Expression Dataset,WEB-BASED DATABASE FOR FACIAL EXPRESSION ANALYSIS,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+moments_in_time,Moments in Time,Moments in Time Dataset: one million videos for event understanding,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+morph,MORPH Commercial,MORPH: A Longitudinal Image Database of Normal Adult Age-Progression,,,,same pdf as morph non commercial,,,,"IEEE 7th International Conference on Automatic Face and Gesture Recognition, Southampton, UK",,,2006,341-345,,Karl Ricanek Jr,Tamirat Tesafaye,,,,,,,,,,,,,
+morph_nc,MORPH Non-Commercial,MORPH: A Longitudinal Image Database of Normal Adult Age-Progression,,,,same pdf as morph commercial,,,,"IEEE 7th International Conference on Automatic Face and Gesture Recognition, Southampton, UK",,,2006,341-345,,Karl Ricanek Jr,Tamirat Tesafaye,,,,,,,,,,,,,
+mot,MOT,Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics,,,,these 3 citations are from the MOT17,,,,,,,,,,,,,,,,,,,,,,,,
+mot,MOT,"Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking",,,,,,,,,,,,,,,,,,,,,,,,,,,,
+mot,MOT,Learning to associate: HybridBoosted multi-target tracker for crowded scene,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+mpi_large,Large MPI Facial Expression,The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+mpi_small,Small MPI Facial Expression,The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+mpii_gaze,MPIIGaze,Appearance-based Gaze Estimation in the Wild,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+mpii_human_pose,MPII Human Pose,2D Human Pose Estimation: New Benchmark and State of the Art Analysis,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+mr2,MR2,The MR2: A multi-racial mega-resolution database of facial stimuli,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+mrp_drone,MRP Drone,Investigating Open-World Person Re-identification Using a Drone,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+msceleb,MsCeleb,MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition,,,,,,,,European Conference on Computer Vision,,,2016,,,"Guo, Yandong","Zhang, Lei","Hu, Yuxiao","He, Xiaodong","Gao, Jianfeng",,,,,,,,,https://www.microsoft.com/en-us/research/wp-content/uploads/2016/08/MSCeleb-1M-a.pdf,"@INPROCEEDINGS { guo2016msceleb,
+            author = {Guo, Yandong and Zhang, Lei and Hu, Yuxiao and He, Xiaodong and Gao, Jianfeng},
+            title = {M{S}-{C}eleb-1{M}: A Dataset and Benchmark for Large Scale Face Recognition},
+            booktitle = {European Conference on Computer Vision},
+            year = {2016},
+            organization={Springer}}"
+msmt_17,MSMT17,Person Transfer GAN to Bridge Domain Gap for Person Re-Identification,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+mtfl,MTFL,Facial Landmark Detection by Deep Multi-task Learning,,,,same paper as in MAFL,,,,,,,,,,,,,,,,,,,,,,,,
+mtfl,MTFL,Learning Deep Representation for Face Alignment with Auxiliary Attributes,,,,same papers as in MAFL,,,,,,,,,,,,,,,,,,,,,,,,
+muct,MUCT,The MUCT Landmarked Face Database,,,,,,,,Pattern Recognition Association of South Africa,,,2010,,,,S. Milborrow,J. Morkel,F. Nicolls,,,,,,,,,,http://www.milbo.org/muct/The-MUCT-Landmarked-Face-Database.pdf,"@article{Milborrow10,
+  author={S. Milborrow and J. Morkel and F. Nicolls},
+  title={{The MUCT Landmarked Face Database}},
+  journal={Pattern Recognition Association of South Africa},
+  year=2010,
+  note={\url{http://www.milbo.org/muct}}
+}"
+mug_faces,MUG Faces,The MUG Facial Expression Database,,,,,,,,Procedings of 11th International Workshop on Image Analysis for Multimedia Interactive Services,12,4,2010,,,N. Aifanti,C. Papachristou,A. Delopoulos,,,,,,,,,,,,
+multi_pie,MULTIPIE,Multi-PIE,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+names_and_faces_news,News Dataset,Names and Faces,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+nd_2006,ND-2006,Using a Multi-Instance Enrollment Representation to Improve 3D Face Recognition,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+nist_mid_mugshot,MID,,,,,OK no paper,,,,,,,,,,,,,,,,,,,,,,,,
+nova_emotions,Novaemötions Dataset,Crowdsourcing facial expressions for affective-interaction,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+nova_emotions,Novaemötions Dataset,Competitive affective gamming: Winning with a smile,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+nudedetection,Nude Detection,A Bag-of-Features Approach based on Hue-SIFT Descriptor for Nude Detection,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+orl,ORL,Parameterisation of a Stochastic Model for Human Face Identification,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+penn_fudan,Penn Fudan,Object Detection Combining Recognition and Segmentation,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+peta,PETA,Pedestrian Attribute Recognition At Far Distance,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+pets,PETS 2017,PETS 2017: Dataset and Challenge,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+pilot_parliament,PPB,Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classication,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+pipa,PIPA,Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues,,,,,,,,arXiv:1501.05703 [cs.CV],,,2015,,,Ning Zhang, Manohar Paluri,Yaniv Taigman,Rob Fergus,Lubomir Bourdev,,,,,,,,,https://arxiv.org/pdf/1501.05703.pdf,"@inproceedings{piper,
+        Author = {Ning Zhang and Manohar Paluri and Yaniv Taigman and Rob Fergus and Lubomir Bourdev},
+        Title = {Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues},
+        Eprint = {arXiv:1501.05703},
+        Year = {2015}}"
+pku,PKU,Swiss-System Based Cascade Ranking for Gait-based Person Re-identification,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+pku_reid,PKU-Reid,Orientation driven bag of appearances for person re-identification,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+pornodb,Pornography DB,Pooling in Image Representation: the Visual Codeword Point of View,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+precarious,Precarious,Expecting the Unexpected: Training Detectors for Unusual Pedestrians With Adversarial Imposters,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+prid,PRID,Person Re-Identification by Descriptive and Discriminative Classification,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+prw,PRW,Person Re-identification in the Wild,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+psu,PSU,Vision-based Analysis of Small Groups in Pedestrian Crowds,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+pubfig,PubFig,Attribute and Simile Classifiers for Face Verification,,,,,,,,International Conference on Computer Vision (ICCV),,,2009,,,Neeraj Kumar,Alexander C. Berg,Peter N. Belhumeur,Shree K. Nayar,,,,,,,,,,http://www.cs.columbia.edu/CAVE/publications/pdfs/Kumar_ICCV09.pdf,
+pubfig_83,pubfig83,Scaling Up Biologically-Inspired Computer Vision: A Case Study in Unconstrained Face Recognition on Facebook,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+put_face,Put Face,The PUT face database,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+qmul_grid,GRID,Multi-Camera Activity Correlation Analysis,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+qmul_grid,GRID,Time-delayed correlation analysis for multi-camera activity understanding,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+qmul_ilids,QMUL-iLIDS,,,,,OK no paper,,,,,,,,,,,,,,,,,,,,,,,,
+qmul_surv_face,QMUL-SurvFace,Surveillance Face Recognition Challenge,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+rafd,RaFD,Presentation and validation of the Radboud Faces Database,,,,,,,,Cognition & Emotion,,,2010,1377-1388,24.8,"Langner, O.","Dotsch, R."," Bijlstra, G.","Wigboldus, D.H.J.","Hawk, S.T.","van Knippenberg, A.",,,,,,,,http://dx.doi.org/10.1080/02699930903485076,DOI: 10.1080/02699930903485076
+raid,RAiD,Consistent Re-identification in a Camera Network,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+rap_pedestrian,RAP,A Richly Annotated Dataset for Pedestrian Attribute Recognition,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+reseed,ReSEED,ReSEED: Social Event dEtection Dataset,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+saivt,SAIVT SoftBio,A Database for Person Re-Identification in Multi-Camera Surveillance Networks,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+sarc3d,Sarc3D,SARC3D: a new 3D body model for People Tracking and Re-identification,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+scface,SCface,SCface – surveillance cameras face database,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+scut_fbp,SCUT-FBP,SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception,,,,,,,,arXiv:1511.02459 [cs.CV],,,2015,,,Duorui Xie,Lingyu Liang,Lianwen Jin,Jie Xu,Mengru Li,,,,,,,,,https://arxiv.org/ftp/arxiv/papers/1511/1511.02459.pdf,
+scut_head,SCUT HEAD,Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+sdu_vid,SDU-VID,A Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+sdu_vid,SDU-VID,Local descriptors encoded by Fisher vectors for person re-identification,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+sdu_vid,SDU-VID,Person reidentification by video ranking,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+sheffield,Sheffield Face,Face Recognition: From Theory to Applications ,,,,OK no paper,,,,,,,,,,,,,,,,,,,,,,,,
+shinpuhkan_2014,Shinpuhkan 2014,Shinpuhkan2014: A Multi-Camera Pedestrian Dataset for Tracking People across Multiple Cameras,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+social_relation,Social Relation,From Facial Expression Recognition to Interpersonal Relation Prediction,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+social_relation,Social Relation,Learning Social Relation Traits from Face Images,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+soton,SOTON HiD,On a Large Sequence-Based Human Gait Database,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+sports_videos_in_the_wild,SVW,Sports Videos in the Wild (SVW): A Video Dataset for Sports Analysis,,,,,,,,"Proc. International Conference on Automatic Face and Gesture Recognition (FG 2015), Ljubljana, Slovenia",,,2015,,,Seyed Morteza Safdarnejad, Xiaoming Liu, Lalita Udpa, Brooks Andrus,"John Wood,",Dean Craven,,,,,,,,http://cvlab.cse.msu.edu/pdfs/Morteza_FG2015.pdf," @inproceedings{ sports-videos-in-the-wild-svw-a-video-dataset-for-sports-analysis,
+  author = { Seyed Morteza Safdarnejad and Xiaoming Liu and Lalita Udpa and Brooks Andrus and John Wood and Dean Craven },
+  title = { Sports Videos in the Wild (SVW): A Video Dataset for Sports Analysis },
+  booktitle = { Proc. International Conference on Automatic Face and Gesture Recognition },
+  address = { Ljubljana, Slovenia },
+  month = { May },
+  year = { 2015 },
+} "
+stair_actions,STAIR Action,STAIR Actions: A Video Dataset of Everyday Home Actions,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+stanford_drone,Stanford Drone,Learning Social Etiquette: Human Trajectory Prediction In Crowded Scenes,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+stickmen_buffy,Buffy Stickmen,Learning to Parse Images of Articulated Objects,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+stickmen_buffy,Buffy Stickmen,Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+stickmen_family,We Are Family Stickmen,We Are Family: Joint Pose Estimation of Multiple Persons,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+stickmen_pascal,Stickmen PASCAL,Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+stickmen_pascal,Stickmen PASCAL,Learning to Parse Images of Articulated Objects,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+stirling_esrc_3s,Stirling/ESRC 3D Face,,,,,no paper published yet (they say to cite the URL),,,,,,,,,,,,,,,,,,,,,,,,
+sun_attributes,SUN,The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+sun_attributes,SUN,"SUN Attribute Database: 
+Discovering, Annotating, and Recognizing Scene Attributes",,,,,,,,,,,,,,,,,,,,,,,,,,,,
+svs,SVS,Pedestrian Attribute Classification in Surveillance: Database and Evaluation,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+texas_3dfrd,Texas 3DFRD,Texas 3D Face Recognition Database,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+texas_3dfrd,Texas 3DFRD,Anthropometric 3D Face Recognition,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+tiny_faces,TinyFace,Low-Resolution Face Recognition,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+tiny_images,Tiny Images,80 million tiny images: a large dataset for non-parametric object and scene recognition,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+towncenter,TownCenter,Stable Multi-Target Tracking in Real-Time Surveillance Video,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+tud_brussels,TUD-Brussels,Multi-Cue Onboard Pedestrian Detection,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+tud_campus,TUD-Campus, People-Tracking-by-Detection and People-Detection-by-Tracking,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+tud_crossing,TUD-Crossing, People-Tracking-by-Detection and People-Detection-by-Tracking,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+tud_motionpairs,TUD-Motionparis,Multi-Cue Onboard Pedestrian Detection,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+tud_multiview,TUD-Multiview,Monocular 3D Pose Estimation and Tracking by Detection,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+tud_pedestrian,TUD-Pedestrian,People-Tracking-by-Detection and People-Detection-by-Tracking,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+tud_stadtmitte,TUD-Stadtmitte,Monocular 3D Pose Estimation and Tracking by Detection,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+tvhi,TVHI,High Five: Recognising human interactions in TV shows,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+twinsburg_twins,ND-TWINS-2009-2010,,,,,OK No Paper,,,,,,,,,,,,,,,,,,,,,,,,
+uccs,UCCS,Large scale unconstrained open set face database,,,,need research access to the paper,,,,,,,,,,,,,,,,,,,,,,,,
+ucf_101,UCF101,UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild,,,,,,,,CRCV-TR-12-01,,,2012,,,"Soomro, K.","Roshan Zamir, A.","Shah, M.",,,,,,,,2,,,,"@inproceedings{UCF101,
+   author = {Soomro, K. and Roshan Zamir, A. and Shah, M.},
+   booktitle = {CRCV-TR-12-01},
+   title = {{UCF101}: A Dataset of 101 Human Actions Classes From
+   Videos in The Wild},
+   year = {2012}}"
+ucf_crowd,UCF-CC-50,Multi-Source Multi-Scale Counting in Extremely Dense Crowd Images,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ucf_selfie,UCF Selfie,How to Take a Good Selfie?,,,,,,,," in Proceedings of ACM Multimedia Conference 2015 (ACMMM 2015), Brisbane, Australia",,,2015,,,Mahdi M. Kalayeh,Misrak Seifu,Wesna LaLanne,Mubarak Shah,,,,,,,,,,,
+ufdd,UFDD,Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+umb,UMB,UMB-DB: A Database of Partially Occluded 3D Faces,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+umd_faces,UMD,UMDFaces: An Annotated Face Dataset for Training Deep Networks,,,,,,,,Arxiv preprint,,,2016,,,Ankan Bansal,Anirudh Nanduri,Carlos D Castillo,Rajeev Ranjan,Rama Chellappa,,,,,,1,,,https://arxiv.org/abs/1611.01484v2,"@article{bansal2016umdfaces,
+			title={UMDFaces: An Annotated Face Dataset for Training Deep Networks},
+			author={Bansal, Ankan and Nanduri, Anirudh and Castillo, Carlos D and Ranjan, Rajeev and Chellappa, Rama}
+			journal={arXiv preprint arXiv:1611.01484v2},
+			year={2016}
+			}"
+umd_faces,UMD,The Do's and Don'ts for CNN-based Face Verification,,,,,,,,Arxiv preprint,,,2017,,,Ankan Bansal,Carlos Castillo,"Rajeev Ranjan,",Rama Chellappa,,,,,,,2,,,https://arxiv.org/abs/1705.07426,"@article{bansal2017dosanddonts,
+			title = {The Do's and Don'ts for CNN-based Face Verification},
+			author = {Bansal, Ankan and Castillo, Carlos and Ranjan, Rajeev and Chellappa, Rama},
+			journal = {arXiv preprint arXiv:1705.07426},
+			year = {2017}
+			}"
+unbc_shoulder_pain,UNBC-McMaster Pain,PAINFUL DATA: The UNBC-McMaster Shoulder Pain Expression Archive Database,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+urban_tribes,Urban Tribes,From Bikers to Surfers: Visual Recognition of Urban Tribes,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+used,USED Social Event Dataset,USED: A Large-scale Social Event Detection Dataset,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+v47,V47,Re-identification of Pedestrians with Variable Occlusion and Scale,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+vadana,VADANA,VADANA: A dense dataset for facial image analysis,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+vgg_celebs_in_places,CIP,Faces in Places: Compound Query Retrieval  ,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+vgg_faces,VGG Face,Deep Face Recognition,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+vgg_faces2,VGG Face2,VGGFace2: A dataset for recognising faces across pose and age,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+violent_flows,Violent Flows,Violent Flows: Real-Time Detection of Violent Crowd Behavior,,,,,,,,,,,2012,,,T. Hassner,,,,,,,,,,,,,,"T. Hassner, Y. Itcher, and O. Kliper-Gross, Violent Flows: Real-Time Detection of Violent Crowd Behavior, 3rd IEEE International Workshop on Socially Intelligent Surveillance and Monitoring (SISM) at the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), Rhode Island, June 2012 ."
+viper,VIPeR,"Evaluating Appearance Models for Recognition, Reacquisition, and Tracking",,,,,,,,,,,,,,,,,,,,,,,,,,,,
+visual_phrases,Phrasal Recognition,Recognition using Visual Phrases,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+vmu,VMU,Can Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?,,,,,,,,"Proc. of 5th IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), (Washington DC, USA)",,,2012,,,A. Dantcheva,C. Chen,A. Ross,,,,,,,,,,,https://www.cse.msu.edu/~rossarun/pubs/DantchevaChenRossFaceCosmetics_BTAS2012.pdf,
+voc,VOC,The PASCAL Visual Object Classes (VOC) Challenge,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+vqa,VQA,VQA: Visual Question Answering,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+ward,WARD,Re-identify people in wide area camera network,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+who_goes_there,WGT,Who Goes There? Approaches to Mapping Facial Appearance Diversity,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+wider,WIDER,Recognize Complex Events from Static Images by Fusing Deep Channels,,,,,,,,2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR),,,2015,,,"Xiong, Yuanjun and Zhu, Kai and Lin, Dahua and Tang, Xiaoou",,,,,,,,,,,,,,
+wider_attribute,WIDER Attribute,Human Attribute Recognition by Deep Hierarchical Contexts,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+wider_face,WIDER FACE,WIDER FACE: A Face Detection Benchmark,,,,,,,,IEEE Conference on Computer Vision and Pattern Recognition (CVPR),,,2016,,,"Yang, Shuo and Luo, Ping and Loy, Chen Change and Tang, Xiaoou",,,,,,,,,,,,,,"@inproceedings{yang2016wider,
+	Author = {Yang, Shuo and Luo, Ping and Loy, Chen Change and Tang, Xiaoou},
+	Booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
+	Title = {WIDER FACE: A Face Detection Benchmark},
+	Year = {2016}}"
+wildtrack,WildTrack,WILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+wlfdb,,WLFDB: Weakly Labeled Face Databases,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+yale_faces,YaleFaces,Acquiring Linear Subspaces for Face Recognition under Variable Lighting,,,,"combined yale_faces, yale_faces_b, yale_faces_b_ext",,,,PAMI,,,2001,,,Athinodoros Georghiades,Peter Belhumeur,David Kriegman,,,,,,,,,,,,
+yale_faces,YaleFaces,From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose,,,,"combined yale_faces, yale_faces_b, yale_faces_b_ext",,,,,,,,,,,,,,,,,,,,,,,,
+yawdd,YawDD,YawDD: A Yawning Detection Dataset,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+yfcc_100m,YFCC100M,YFCC100M: The New Data in Multimedia Research,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+york_3d,UOY 3D Face Database,Three-Dimensional Face Recognition: An Eigensurface Approach,,,,,,,,,,,,,,,,,,,,,,,,,,,,
+youtube_faces,YouTubeFaces,Face Recognition in Unconstrained Videos with Matched Background Similarity,,,,,,,,IEEE Conf. on Computer Vision and Pattern Recognition (CVPR),,,2011,,,Lior Wolf,Tal Hassner,Itay Maoz,,,,,,,,,,,,
+youtube_makeup,YMU,Can Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?,,,,,,,,"Proc. of 5th IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), (Washington DC, USA)",,,2012,,,A. Dantcheva,C. Chen,A. Ross,,,,,,,,1,,,https://www.cse.msu.edu/~rossarun/pubs/DantchevaChenRossFaceCosmetics_BTAS2012.pdf,
+youtube_makeup,YMU,Automatic Facial Makeup Detection with Application in Face Recognition,,,,,,,,"Proc. of 6th IAPR International Conference on Biometrics (ICB), (Madrid, Spain)",,,2013,,,C. Chen,A. Dantcheva,A. Ross,,,,,,,,2,,,https://www.cse.msu.edu/~rossarun/pubs/ChenMakeupDetection_ICB2013.pdf,
+youtube_poses,YouTube Pose,Personalizing Human Video Pose Estimation,,,,The paper doesn't specifically introduce the dataset but it's the only one talking about it,,,,,,,,,,,,,,,,,,,,,,,,
+\ No newline at end of file
diff --git a/scraper/datasets/citations.csv b/scraper/datasets/citations.csv
new file mode 120000
index 00000000..c8019514
--- /dev/null
+++ b/scraper/datasets/citations.csv
@@ -0,0 +1 @@
+citations-20181207.csv
+\ No newline at end of file
diff --git a/scraper/datasets/scholar/entries/300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge.csv b/scraper/datasets/scholar/entries/300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge.csv
new file mode 100644
index 00000000..38f502f9
--- /dev/null
+++ b/scraper/datasets/scholar/entries/300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge.csv
@@ -0,0 +1 @@
+300 faces in-the-wild challenge: The first facial landmark localization challenge|http://openaccess.thecvf.com/content_iccv_workshops_2013/W11/papers/Sagonas_300_Faces_in-the-Wild_2013_ICCV_paper.pdf|2013|396|15|7861246476672124064|http://openaccess.thecvf.com/content_iccv_workshops_2013/W11/papers/Sagonas_300_Faces_in-the-Wild_2013_ICCV_paper.pdf|http://scholar.google.com/scholar?cites=7861246476672124064&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=7861246476672124064&hl=en&as_sdt=0,5|None|Automatic facial point detection plays arguably the most important role in face analysis. Several methods have been proposed which reported their results on databases of both constrained and unconstrained conditions. Most of these databases provide annotations with different mark-ups and in some cases the are problems related to the accuracy of the fiducial points. The aforementioned issues as well as the lack of a evaluation protocol makes it difficult to compare performance between different systems. In this paper, we present the …
diff --git a/scraper/datasets/scholar/entries/300 faces In-the-wild challenge: Database and results.csv b/scraper/datasets/scholar/entries/300 faces In-the-wild challenge: Database and results.csv
new file mode 100644
index 00000000..eaaf1a93
--- /dev/null
+++ b/scraper/datasets/scholar/entries/300 faces In-the-wild challenge: Database and results.csv
@@ -0,0 +1 @@
+300 faces in-the-wild challenge: Database and results|http://scholar.google.com/https://www.sciencedirect.com/science/article/pii/S0262885616000147|2016|141|9|4741451765657920988|None|http://scholar.google.com/scholar?cites=4741451765657920988&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=4741451765657920988&hl=en&as_sdt=0,5|None|Computer Vision has recently witnessed great research advance towards automatic facial points detection. Numerous methodologies have been proposed during the last few years that achieve accurate and efficient performance. However, fair comparison between these methodologies is infeasible mainly due to two issues.(a) Most existing databases, captured under both constrained and unconstrained (in-the-wild) conditions have been annotated using different mark-ups and, in most cases, the accuracy of the annotations is low.(b) Most …
diff --git a/scraper/datasets/scholar/entries/A data-driven approach to cleaning large face datasets.csv b/scraper/datasets/scholar/entries/A data-driven approach to cleaning large face datasets.csv
new file mode 100644
index 00000000..c1bf1f38
--- /dev/null
+++ b/scraper/datasets/scholar/entries/A data-driven approach to cleaning large face datasets.csv
@@ -0,0 +1 @@
+A data-driven approach to cleaning large face datasets|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/7025068/|2014|163|8|9390951279725836807|None|http://scholar.google.com/scholar?cites=9390951279725836807&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=9390951279725836807&hl=en&as_sdt=0,5|None|Large face datasets are important for advancing face recognition research, but they are tedious to build, because a lot of work has to go into cleaning the huge amount of raw data. To facilitate this task, we describe an approach to building face datasets that starts with detecting faces in images returned from searches for public figures on the Internet, followed by discarding those not belonging to each queried person. We formulate the problem of identifying the faces to be removed as a quadratic programming problem, which exploits the …
diff --git a/scraper/datasets/scholar/entries/A semi-automatic methodology for facial landmark annotation.csv b/scraper/datasets/scholar/entries/A semi-automatic methodology for facial landmark annotation.csv
new file mode 100644
index 00000000..31bf7b39
--- /dev/null
+++ b/scraper/datasets/scholar/entries/A semi-automatic methodology for facial landmark annotation.csv
@@ -0,0 +1 @@
+A semi-automatic methodology for facial landmark annotation|http://scholar.google.com/https://www.cv-foundation.org/openaccess/content_cvpr_workshops_2013/W16/papers/Sagonas_A_Semi-automatic_Methodology_2013_CVPR_paper.pdf|2013|225|16|15744661091744891|http://scholar.google.com/https://www.cv-foundation.org/openaccess/content_cvpr_workshops_2013/W16/papers/Sagonas_A_Semi-automatic_Methodology_2013_CVPR_paper.pdf|http://scholar.google.com/scholar?cites=15744661091744891&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=15744661091744891&hl=en&as_sdt=0,5|None|Developing powerful deformable face models requires massive, annotated face databases on which techniques can be trained, validated and tested. Manual annotation of each facial image in terms of landmarks requires a trained expert and the workload is usually enormous. Fatigue is one of the reasons that in some cases annotations are inaccurate. This is why, the majority of existing facial databases provide annotations for a relatively small subset of the training images. Furthermore, there is hardly any correspondence between the …
diff --git a/scraper/datasets/scholar/entries/Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization.csv b/scraper/datasets/scholar/entries/Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization.csv
new file mode 100644
index 00000000..035e5e0f
--- /dev/null
+++ b/scraper/datasets/scholar/entries/Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization.csv
@@ -0,0 +1 @@
+Annotated facial landmarks in the wild: A large-scale, real-world database for facial landmark localization|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/6130513/|2011|402|12|2106290919498044015|None|http://scholar.google.com/scholar?cites=2106290919498044015&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=2106290919498044015&hl=en&as_sdt=0,5|None|Face alignment is a crucial step in face recognition tasks. Especially, using landmark localization for geometric face normalization has shown to be very effective, clearly improving the recognition results. However, no adequate databases exist that provide a sufficient number of annotated facial landmarks. The databases are either limited to frontal views, provide only a small number of annotated images or have been acquired under controlled conditions. Hence, we introduce a novel database overcoming these limitations …
diff --git a/scraper/datasets/scholar/entries/Attribute and Simile Classifiers for Face Verification.csv b/scraper/datasets/scholar/entries/Attribute and Simile Classifiers for Face Verification.csv
new file mode 100644
index 00000000..1d6e856b
--- /dev/null
+++ b/scraper/datasets/scholar/entries/Attribute and Simile Classifiers for Face Verification.csv
@@ -0,0 +1,2 @@
+Attribute and simile classifiers for face verification|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/5459250/|2009|1231|22|4063408445858122425|None|http://scholar.google.com/scholar?cites=4063408445858122425&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=4063408445858122425&hl=en&as_sdt=0,5|None|We present two novel methods for face verification. Our first method-“attribute” classifiers-uses binary classifiers trained to recognize the presence or absence of describable aspects of visual appearance (eg, gender, race, and age). Our second method-“simile” classifiers-removes the manual labeling required for attribute classification and instead learns the similarity of faces, or regions of faces, to specific reference people. Neither method requires costly, often brittle, alignment between image pairs; yet, both methods produce compact …
+Attribute and simile classifiers for face verification|None|2009|10|0|7848300437118808957|None|http://scholar.google.com/scholar?cites=7848300437118808957&as_sdt=2005&sciodt=0,5&hl=en|None|None|None
diff --git a/scraper/datasets/scholar/entries/Automatic Facial Makeup Detection with Application in Face Recognition.csv b/scraper/datasets/scholar/entries/Automatic Facial Makeup Detection with Application in Face Recognition.csv
new file mode 100644
index 00000000..074471b7
--- /dev/null
+++ b/scraper/datasets/scholar/entries/Automatic Facial Makeup Detection with Application in Face Recognition.csv
@@ -0,0 +1 @@
+Automatic facial makeup detection with application in face recognition|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/6612994/|2013|83|9|6724137544116293607|None|http://scholar.google.com/scholar?cites=6724137544116293607&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=6724137544116293607&hl=en&as_sdt=0,5|None|Facial makeup has the ability to alter the appearance of a person. Such an alteration can degrade the accuracy of automated face recognition systems, as well as that of meth-ods estimating age and beauty from faces. In this work, we design a method to automatically detect the presence of makeup in face images. The proposed algorithm extracts a feature vector that captures the shape, texture and color characteristics of the input face, and employs a classifier to determine the presence or absence of makeup. Besides extracting …
diff --git a/scraper/datasets/scholar/entries/Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues.csv b/scraper/datasets/scholar/entries/Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues.csv
new file mode 100644
index 00000000..0b36206c
--- /dev/null
+++ b/scraper/datasets/scholar/entries/Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues.csv
@@ -0,0 +1 @@
+Beyond frontal faces: Improving person recognition using multiple cues|http://scholar.google.com/https://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Zhang_Beyond_Frontal_Faces_2015_CVPR_paper.html|2015|70|13|4032136205953773331|None|http://scholar.google.com/scholar?cites=4032136205953773331&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=4032136205953773331&hl=en&as_sdt=0,5|None|We explore the task of recognizing peoples' identities in photo albums in an unconstrained setting. To facilitate this, we introduce the new People In Photo Albums (PIPA) dataset, consisting of over 60000 instances of~ 2000 individuals collected from public Flickr photo albums. With only about half of the person images containing a frontal face, the recognition task is very challenging due to the large variations in pose, clothing, camera viewpoint, image resolution and illumination. We propose the Pose Invariant PErson Recognition …
diff --git a/scraper/datasets/scholar/entries/Can Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?.csv b/scraper/datasets/scholar/entries/Can Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?.csv
new file mode 100644
index 00000000..86c81060
--- /dev/null
+++ b/scraper/datasets/scholar/entries/Can Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?.csv
@@ -0,0 +1 @@
+Can facial cosmetics affect the matching accuracy of face recognition systems?|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/6374605/|2012|90|12|13294356886705558975|None|http://scholar.google.com/scholar?cites=13294356886705558975&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=13294356886705558975&hl=en&as_sdt=0,5|None|The matching performance of automated face recognition has significantly improved over the past decade. At the same time several challenges remain that significantly affect the deployment of such systems in security applications. In this work, we study the impact of a commonly used face altering technique that has received limited attention in the biometric literature, viz., non-permanent facial makeup. Towards understanding its impact, we first assemble two databases containing face images of subjects, before and after applying …
diff --git a/scraper/datasets/scholar/entries/Coding Facial Expressions with Gabor Wavelets.csv b/scraper/datasets/scholar/entries/Coding Facial Expressions with Gabor Wavelets.csv
new file mode 100644
index 00000000..36b9e0cf
--- /dev/null
+++ b/scraper/datasets/scholar/entries/Coding Facial Expressions with Gabor Wavelets.csv
@@ -0,0 +1 @@
+Coding facial expressions with gabor wavelets|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/670949/|1998|1623|14|1158641084116311050|None|http://scholar.google.com/scholar?cites=1158641084116311050&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=1158641084116311050&hl=en&as_sdt=0,5|None|A method for extracting information about facial expressions from images is presented. Facial expression images are coded using a multi-orientation multi-resolution set of Gabor filters which are topographically ordered and aligned approximately with the face. The similarity space derived from this representation is compared with one derived from semantic ratings of the images by human observers. The results show that it is possible to construct a facial expression classifier with Gabor coding of the facial images as the input …
diff --git a/scraper/datasets/scholar/entries/Comprehensive Database for Facial Expression Analysis.csv b/scraper/datasets/scholar/entries/Comprehensive Database for Facial Expression Analysis.csv
new file mode 100644
index 00000000..e97d2c56
--- /dev/null
+++ b/scraper/datasets/scholar/entries/Comprehensive Database for Facial Expression Analysis.csv
@@ -0,0 +1 @@
+Comprehensive database for facial expression analysis|http://scholar.google.com/https://www.computer.org/csdl/proceedings/fg/2000/0580/00/05800046.pdf|2000|2538|29|17655514864522744044|http://scholar.google.com/https://www.computer.org/csdl/proceedings/fg/2000/0580/00/05800046.pdf|http://scholar.google.com/scholar?cites=17655514864522744044&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=17655514864522744044&hl=en&as_sdt=0,5|None|Within the past decade, significant effort has occurred in developing methods of facial expression analysis. Because most investigators have used relatively limited data sets, the generalizability of these various methods remains unknown. We describe the problem space for facial expression analysis, which includes level of description, transitions among expression, eliciting conditions, reliability and validity of training and test data, individual differences in subjects, head orientation and scene complexity, image characteristics, and …
diff --git a/scraper/datasets/scholar/entries/DEX: Deep EXpectation of apparent age from a single image.csv b/scraper/datasets/scholar/entries/DEX: Deep EXpectation of apparent age from a single image.csv
new file mode 100644
index 00000000..b3728548
--- /dev/null
+++ b/scraper/datasets/scholar/entries/DEX: Deep EXpectation of apparent age from a single image.csv
@@ -0,0 +1 @@
+Dex: Deep expectation of apparent age from a single image|http://scholar.google.com/https://www.cv-foundation.org/openaccess/content_iccv_2015_workshops/w11/html/Rothe_DEX_Deep_EXpectation_ICCV_2015_paper.html|2015|155|15|12384435539194835187|None|http://scholar.google.com/scholar?cites=12384435539194835187&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=12384435539194835187&hl=en&as_sdt=0,5|None|In this paper we tackle the estimation of apparent age in still face images with deep learning. Our convolutional neural networks (CNNs) use the VGG-16 architecture and are pretrained on ImageNet for image classification. In addition, due to the limited number of apparent age annotated images, we explore the benefit of finetuning over crawled Internet face images with available age. We crawled 0.5 million images of celebrities from IMDB and Wikipedia that we make public. This is the largest public dataset for age prediction to date. We pose the …
diff --git a/scraper/datasets/scholar/entries/Deep expectation of real and apparent age from a single image without facial landmarks.csv b/scraper/datasets/scholar/entries/Deep expectation of real and apparent age from a single image without facial landmarks.csv
new file mode 100644
index 00000000..ed47fdef
--- /dev/null
+++ b/scraper/datasets/scholar/entries/Deep expectation of real and apparent age from a single image without facial landmarks.csv
@@ -0,0 +1 @@
+Deep expectation of real and apparent age from a single image without facial landmarks|http://scholar.google.com/https://link.springer.com/article/10.1007/s11263-016-0940-3|2018|135|7|11164967779616636427|None|http://scholar.google.com/scholar?cites=11164967779616636427&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=11164967779616636427&hl=en&as_sdt=0,5|None|In this paper we propose a deep learning solution to age estimation from a single face image without the use of facial landmarks and introduce the IMDB-WIKI dataset, the largest public dataset of face images with age and gender labels. If the real age estimation research spans over decades, the study of apparent age estimation or the age as perceived by other humans from a face image is a recent endeavor. We tackle both tasks with our convolutional neural networks (CNNs) of VGG-16 architecture which are pre-trained on ImageNet for …
diff --git a/scraper/datasets/scholar/entries/Distance Estimation of an Unknown Person from a Portrait .csv b/scraper/datasets/scholar/entries/Distance Estimation of an Unknown Person from a Portrait .csv
new file mode 100644
index 00000000..5cd26552
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+++ b/scraper/datasets/scholar/entries/Distance Estimation of an Unknown Person from a Portrait .csv
@@ -0,0 +1 @@
+Distance estimation of an unknown person from a portrait|http://scholar.google.com/https://link.springer.com/chapter/10.1007/978-3-319-10590-1_21|2014|7|9|11199246855168438175|None|http://scholar.google.com/scholar?cites=11199246855168438175&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=11199246855168438175&hl=en&as_sdt=0,5|None|We propose the first automated method for estimating distance from frontal pictures of unknown faces. Camera calibration is not necessary, nor is the reconstruction of a 3D representation of the shape of the head. Our method is based on estimating automatically the position of face and head landmarks in the image, and then using a regressor to estimate distance from such measurements. We collected and annotated a dataset of frontal portraits of 53 individuals spanning a number of attributes (sex, age, race, hair), each …
diff --git a/scraper/datasets/scholar/entries/Eigenfaces vs. fisherfaces: Recognition using class specific linear projection.csv b/scraper/datasets/scholar/entries/Eigenfaces vs. fisherfaces: Recognition using class specific linear projection.csv
new file mode 100644
index 00000000..252d269c
--- /dev/null
+++ b/scraper/datasets/scholar/entries/Eigenfaces vs. fisherfaces: Recognition using class specific linear projection.csv
@@ -0,0 +1,2 @@
+Eigenfaces vs. fisherfaces: Recognition using class specific linear projection|http://www.dtic.mil/docs/citations/AD1015508|1997|13228|67|13084856655998519010|None|http://scholar.google.com/scholar?cites=13084856655998519010&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=13084856655998519010&hl=en&as_sdt=0,5|None|We develop a face recognition algorithm which is insensitive to large variation in lighting direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as a coordinate in a high-dimensional space. We take advantage of the observation that the images of a particular face, under varying illumination but fixed pose, lie in a 3D linear subspace of the high dimensional image spaceif the face is a Lambertian surface without shadowing. However, since faces are not truly Lambertian surfaces and do …
+Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection|http://scholar.google.com/https://link.springer.com/chapter/10.1007/BFb0015522|1996|609|8|10500235270745853797|None|http://scholar.google.com/scholar?cites=10500235270745853797&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=10500235270745853797&hl=en&as_sdt=0,5|None|We develop a face recognition algorithm which is insensitive to gross variation in lighting direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as a coordinate in a high-dimensional space. We take advantage of the observation that the images of a particular face under varying illumination direction lie in a 3-D linear subspace of the high dimensional feature space—if the face is a Lambertian surface without self-shadowing. However, since faces are not truly Lambertian surfaces and do …
diff --git a/scraper/datasets/scholar/entries/FDDB: A Benchmark for Face Detection in Unconstrained Settings.csv b/scraper/datasets/scholar/entries/FDDB: A Benchmark for Face Detection in Unconstrained Settings.csv
new file mode 100644
index 00000000..eeb0adb1
--- /dev/null
+++ b/scraper/datasets/scholar/entries/FDDB: A Benchmark for Face Detection in Unconstrained Settings.csv
@@ -0,0 +1 @@
+Fddb: A benchmark for face detection in unconstrained settings|http://www.cs.umass.edu/~elm/papers/fddb.pdf|2010|525|13|17267836250801810690|http://www.cs.umass.edu/~elm/papers/fddb.pdf|http://scholar.google.com/scholar?cites=17267836250801810690&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=17267836250801810690&hl=en&as_sdt=0,5|None|Despite the maturity of face detection research, it remains difficult to compare different algorithms for face detection. This is partly due to the lack of common evaluation schemes. Also, existing data sets for evaluating face detection algorithms do not capture some aspects of face appearances that are manifested in real-world scenarios. In this work, we address both of these issues. We present a new data set of face images with more faces and more accurate annotations for face regions than in previous data sets. We also propose two …
diff --git a/scraper/datasets/scholar/entries/Face Recognition in Unconstrained Videos with Matched Background Similarity.csv b/scraper/datasets/scholar/entries/Face Recognition in Unconstrained Videos with Matched Background Similarity.csv
new file mode 100644
index 00000000..2f1e41af
--- /dev/null
+++ b/scraper/datasets/scholar/entries/Face Recognition in Unconstrained Videos with Matched Background Similarity.csv
@@ -0,0 +1,2 @@
+Face recognition in unconstrained videos with matched background similarity|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/5995566/|2011|657|10|5401801956686441353|None|http://scholar.google.com/scholar?cites=5401801956686441353&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=5401801956686441353&hl=en&as_sdt=0,5|None|Recognizing faces in unconstrained videos is a task of mounting importance. While obviously related to face recognition in still images, it has its own unique characteristics and algorithmic requirements. Over the years several methods have been suggested for this problem, and a few benchmark data sets have been assembled to facilitate its study. However, there is a sizable gap between the actual application needs and the current state of the art. In this paper we make the following contributions.(a) We present a comprehensive …
+Face Recognition in Unconstrained Videos with Matched Background Similarity|http://www.cs.tau.ac.il/thesis/thesis/Maoz.Itay-MSc.Thesis.pdf|2012|0|0|None|http://www.cs.tau.ac.il/thesis/thesis/Maoz.Itay-MSc.Thesis.pdf|None|None|None|Recognizing faces in unconstrained videos is a task of mounting importance. While obviously related to face recognition in still images, it has its own unique characteristics and algorithmic requirements. Over the years several methods have been suggested for this problem, and a few benchmark data sets have been assembled to facilitate its study. However, there is a sizable gap between the actual application needs and the current state of the art. In this work we make the following contributions:(a) We present a comprehensive …
diff --git a/scraper/datasets/scholar/entries/Face Swapping: Automatically Replacing Faces in Photographs.csv b/scraper/datasets/scholar/entries/Face Swapping: Automatically Replacing Faces in Photographs.csv
new file mode 100644
index 00000000..de202138
--- /dev/null
+++ b/scraper/datasets/scholar/entries/Face Swapping: Automatically Replacing Faces in Photographs.csv
@@ -0,0 +1 @@
+Face swapping: automatically replacing faces in photographs|http://scholar.google.com/https://dl.acm.org/citation.cfm?id=1360638|2008|228|9|8277329192835426026|None|http://scholar.google.com/scholar?cites=8277329192835426026&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=8277329192835426026&hl=en&as_sdt=0,5|None|In this paper, we present a complete system for automatic face replacement in images. Our system uses a large library of face images created automatically by downloading images from the internet, extracting faces using face detection software, and aligning each extracted face to a common coordinate system. This library is constructed off-line, once, and can be efficiently accessed during face replacement. Our replacement algorithm has three main stages. First, given an input image, we detect all faces that are present, align them to the …
diff --git a/scraper/datasets/scholar/entries/Face detection, pose estimation and landmark localization in the wild.csv b/scraper/datasets/scholar/entries/Face detection, pose estimation and landmark localization in the wild.csv
new file mode 100644
index 00000000..43da8a92
--- /dev/null
+++ b/scraper/datasets/scholar/entries/Face detection, pose estimation and landmark localization in the wild.csv
@@ -0,0 +1 @@
+Face detection, pose estimation, and landmark localization in the wild|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/6248014/|2012|1693|7|4876235110904982186|None|http://scholar.google.com/scholar?cites=4876235110904982186&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=4876235110904982186&hl=en&as_sdt=0,5|None|We present a unified model for face detection, pose estimation, and landmark estimation in real-world, cluttered images. Our model is based on a mixtures of trees with a shared pool of parts; we model every facial landmark as a part and use global mixtures to capture topological changes due to viewpoint. We show that tree-structured models are surprisingly effective at capturing global elastic deformation, while being easy to optimize unlike dense graph structures. We present extensive results on standard face benchmarks, as well as a …
diff --git a/scraper/datasets/scholar/entries/FaceTracer: A Search Engine for Large Collections of Images with Faces.csv b/scraper/datasets/scholar/entries/FaceTracer: A Search Engine for Large Collections of Images with Faces.csv
new file mode 100644
index 00000000..a03e78e4
--- /dev/null
+++ b/scraper/datasets/scholar/entries/FaceTracer: A Search Engine for Large Collections of Images with Faces.csv
@@ -0,0 +1 @@
+Facetracer: A search engine for large collections of images with faces|http://scholar.google.com/https://link.springer.com/10.1007/978-3-540-88693-8_25|2008|324|15|10337130688446550899|None|http://scholar.google.com/scholar?cites=10337130688446550899&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=10337130688446550899&hl=en&as_sdt=0,5|None|We have created the first image search engine based entirely on faces. Using simple text queries such as “smiling men with blond hair and mustaches,” users can search through over 3.1 million faces which have been automatically labeled on the basis of several facial attributes. Faces in our database have been extracted and aligned from images downloaded from the internet using a commercial face detector, and the number of images and attributes continues to grow daily. Our classification approach uses a novel combination of Support …
diff --git a/scraper/datasets/scholar/entries/Fine-grained Evaluation on Face Detection in the Wild..csv b/scraper/datasets/scholar/entries/Fine-grained Evaluation on Face Detection in the Wild..csv
new file mode 100644
index 00000000..249cea3a
--- /dev/null
+++ b/scraper/datasets/scholar/entries/Fine-grained Evaluation on Face Detection in the Wild..csv
@@ -0,0 +1 @@
+Fine-grained evaluation on face detection in the wild|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/7163158/|2015|24|7|6318135921321197431|None|http://scholar.google.com/scholar?cites=6318135921321197431&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=6318135921321197431&hl=en&as_sdt=0,5|None|Current evaluation datasets for face detection, which is of great value in real-world applications, are still somewhat out-of-date. We propose a new face detection dataset MALF (short for Multi-Attribute Labelled Faces), which contains 5,250 images collected from the Internet and~ 12,000 labelled faces. The MALF dataset highlights in two main features: 1) It is the largest dataset for evaluation of face detection in the wild, and the annotation of multiple facial attributes makes it possible for fine-grained performance analysis. 2) To …
diff --git a/scraper/datasets/scholar/entries/From Facial Parts Responses to Face Detection: A Deep Learning Approach.csv b/scraper/datasets/scholar/entries/From Facial Parts Responses to Face Detection: A Deep Learning Approach.csv
new file mode 100644
index 00000000..e22f032b
--- /dev/null
+++ b/scraper/datasets/scholar/entries/From Facial Parts Responses to Face Detection: A Deep Learning Approach.csv
@@ -0,0 +1 @@
+From facial parts responses to face detection: A deep learning approach|http://scholar.google.com/https://www.cv-foundation.org/openaccess/content_iccv_2015/html/Yang_From_Facial_Parts_ICCV_2015_paper.html|2015|213|12|1818335115841631894|None|http://scholar.google.com/scholar?cites=1818335115841631894&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=1818335115841631894&hl=en&as_sdt=0,5|None|In this paper, we propose a novel deep convolutional network (DCN) that achieves outstanding performance on FDDB, PASCAL Face, and AFW. Specifically, our method achieves a high recall rate of 90.99% on the challenging FDDB benchmark, outperforming the state-of-the-art method by a large margin of 2.91%. Importantly, we consider finding faces from a new perspective through scoring facial parts responses by their spatial structure and arrangement. The scoring mechanism is carefully formulated considering challenging …
diff --git a/scraper/datasets/scholar/entries/Indian Movie Face Database: A Benchmark for Face Recognition Under Wide Variations.csv b/scraper/datasets/scholar/entries/Indian Movie Face Database: A Benchmark for Face Recognition Under Wide Variations.csv
new file mode 100644
index 00000000..b9feb021
--- /dev/null
+++ b/scraper/datasets/scholar/entries/Indian Movie Face Database: A Benchmark for Face Recognition Under Wide Variations.csv
@@ -0,0 +1 @@
+Indian movie face database: a benchmark for face recognition under wide variations|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/6776225/|2013|29|7|10194316221634175118|None|http://scholar.google.com/scholar?cites=10194316221634175118&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=10194316221634175118&hl=en&as_sdt=0,5|None|Recognizing human faces in the wild is emerging as a critically important, and technically challenging computer vision problem. With a few notable exceptions, most previous works in the last several decades have focused on recognizing faces captured in a laboratory setting. However, with the introduction of databases such as LFW and Pubfigs, face recognition community is gradually shifting its focus on much more challenging unconstrained settings. Since its introduction, LFW verification benchmark is getting a lot of attention with various …
diff --git a/scraper/datasets/scholar/entries/Is the Eye Region More Reliable Than the Face? A Preliminary Study of Face-based Recognition on a Transgender Dataset.csv b/scraper/datasets/scholar/entries/Is the Eye Region More Reliable Than the Face? A Preliminary Study of Face-based Recognition on a Transgender Dataset.csv
new file mode 100644
index 00000000..a47bb51d
--- /dev/null
+++ b/scraper/datasets/scholar/entries/Is the Eye Region More Reliable Than the Face? A Preliminary Study of Face-based Recognition on a Transgender Dataset.csv
@@ -0,0 +1 @@
+Is the eye region more reliable than the face? A preliminary study of face-based recognition on a transgender dataset|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/6712710/|2013|10|4|3607022377504716214|None|http://scholar.google.com/scholar?cites=3607022377504716214&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=3607022377504716214&hl=en&as_sdt=0,5|None|In this work we investigate a truly novel and extremely unique biometric problem: face-based recognition for transgender persons. A transgender person is someone who under goes a gender transformation via hormone replacement therapy; that is, a male becomes a female by suppressing natural testosterone production and exogenously increasing estrogen. Transgender hormone replacement therapy causes physical changes in the body and face. This work provides a preliminary investigation into the effects of these changes on face …
diff --git a/scraper/datasets/scholar/entries/Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments..csv b/scraper/datasets/scholar/entries/Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments..csv
new file mode 100644
index 00000000..23c90284
--- /dev/null
+++ b/scraper/datasets/scholar/entries/Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments..csv
@@ -0,0 +1 @@
+Labeled faces in the wild: A database forstudying face recognition in unconstrained environments|http://scholar.google.com/https://hal.inria.fr/inria-00321923/|2008|3014|37|6713997626354918066|None|http://scholar.google.com/scholar?cites=6713997626354918066&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=6713997626354918066&hl=en&as_sdt=0,5|None|Most face databases have been created under controlled conditions to facilitate the study of specific parameters on the face recognition problem. These parameters include such variables as position, pose, lighting, background, camera quality, and gender. While there are many applications for face recognition technology in which one can control the parameters of image acquisition, there are also many applications in which the practitioner has little or no control over such parameters. This database, Labeled Faces in the Wild, is …
diff --git a/scraper/datasets/scholar/entries/Large Age-Gap Face Verification by Feature Injection in Deep Networks.csv b/scraper/datasets/scholar/entries/Large Age-Gap Face Verification by Feature Injection in Deep Networks.csv
new file mode 100644
index 00000000..9cd388eb
--- /dev/null
+++ b/scraper/datasets/scholar/entries/Large Age-Gap Face Verification by Feature Injection in Deep Networks.csv
@@ -0,0 +1 @@
+Large age-gap face verification by feature injection in deep networks|http://scholar.google.com/https://www.sciencedirect.com/science/article/pii/S0167865517300727|2017|12|8|6980699793307007950|None|http://scholar.google.com/scholar?cites=6980699793307007950&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=6980699793307007950&hl=en&as_sdt=0,5|None|This paper introduces a new method for face verification across large age gaps and also a dataset containing variations of age in the wild, the Large Age-Gap (LAG) dataset, with images ranging from child/young to adult/old. The proposed method exploits a deep convolutional neural network (DCNN) pre-trained for the face recognition task on a large dataset and then fine-tuned for the large age-gap face verification task. Fine-tuning is performed in a Siamese architecture using a contrastive loss function. A feature injection …
diff --git a/scraper/datasets/scholar/entries/Level Playing Field for Million Scale Face Recognition.csv b/scraper/datasets/scholar/entries/Level Playing Field for Million Scale Face Recognition.csv
new file mode 100644
index 00000000..f7130a67
--- /dev/null
+++ b/scraper/datasets/scholar/entries/Level Playing Field for Million Scale Face Recognition.csv
@@ -0,0 +1 @@
+Level playing field for million scale face recognition|http://openaccess.thecvf.com/content_cvpr_2017/papers/Nech_Level_Playing_Field_CVPR_2017_paper.pdf|2017|35|11|12932836311624990730|http://openaccess.thecvf.com/content_cvpr_2017/papers/Nech_Level_Playing_Field_CVPR_2017_paper.pdf|http://scholar.google.com/scholar?cites=12932836311624990730&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=12932836311624990730&hl=en&as_sdt=0,5|None|Face recognition has the perception of a solved problem, however when tested at the million-scale exhibits dramatic variation in accuracies across the different algorithms [11]. Are the algorithms very different? Is access to good/big training data their secret weapon? Where should face recognition improve? To address those questions, we created a benchmark, MF2, that requires all algorithms to be trained on same data, and tested at the million scale. MF2 is a public large-scale set with 672K identities and 4.7 M photos created with the goal to …
diff --git a/scraper/datasets/scholar/entries/Localizing Parts of Faces Using a Consensus of Exemplars.csv b/scraper/datasets/scholar/entries/Localizing Parts of Faces Using a Consensus of Exemplars.csv
new file mode 100644
index 00000000..0fa7a800
--- /dev/null
+++ b/scraper/datasets/scholar/entries/Localizing Parts of Faces Using a Consensus of Exemplars.csv
@@ -0,0 +1 @@
+Localizing parts of faces using a consensus of exemplars|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/6412675/|2013|740|13|8801930631236620204|None|http://scholar.google.com/scholar?cites=8801930631236620204&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=8801930631236620204&hl=en&as_sdt=0,5|None|We present a novel approach to localizing parts in images of human faces. The approach combines the output of local detectors with a nonparametric set of global models for the part locations based on over 1,000 hand-labeled exemplar images. By assuming that the global models generate the part locations as hidden variables, we derive a Bayesian objective function. This function is optimized using a consensus of models for these hidden variables. The resulting localizer handles a much wider range of expression, pose, lighting, and …
diff --git a/scraper/datasets/scholar/entries/MORPH: A Longitudinal Image Database of Normal Adult Age-Progression.csv b/scraper/datasets/scholar/entries/MORPH: A Longitudinal Image Database of Normal Adult Age-Progression.csv
new file mode 100644
index 00000000..a41ffc41
--- /dev/null
+++ b/scraper/datasets/scholar/entries/MORPH: A Longitudinal Image Database of Normal Adult Age-Progression.csv
@@ -0,0 +1 @@
+Morph: A longitudinal image database of normal adult age-progression|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/1613043/|2006|673|6|4438087728034206462|None|http://scholar.google.com/scholar?cites=4438087728034206462&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=4438087728034206462&hl=en&as_sdt=0,5|None|This paper details MORPH a longitudinal face database developed for researchers investigating all facets of adult age-progression, eg face modeling, photo-realistic animation, face recognition, etc. This database contributes to several active research areas, most notably face recognition, by providing: the largest set of publicly available longitudinal images; longitudinal spans from a few months to over twenty years; and, the inclusion of key physical parameters that affect aging appearance. The direct contribution of this data corpus …
diff --git a/scraper/datasets/scholar/entries/MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition.csv b/scraper/datasets/scholar/entries/MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition.csv
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+Ms-celeb-1m: A dataset and benchmark for large-scale face recognition|http://scholar.google.com/https://link.springer.com/chapter/10.1007/978-3-319-46487-9_6|2016|189|6|7096719334274798105|None|http://scholar.google.com/scholar?cites=7096719334274798105&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=7096719334274798105&hl=en&as_sdt=0,5|None|In this paper, we design a benchmark task and provide the associated datasets for recognizing face images and link them to corresponding entity keys in a knowledge base. More specifically, we propose a benchmark task to recognize one million celebrities from their face images, by using all the possibly collected face images of this individual on the web as training data. The rich information provided by the knowledge base helps to conduct disambiguation and improve the recognition accuracy, and contributes to various real-world …
diff --git a/scraper/datasets/scholar/entries/Presentation and validation of the Radboud Faces Database.csv b/scraper/datasets/scholar/entries/Presentation and validation of the Radboud Faces Database.csv
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+Presentation and validation of the Radboud Faces Database|http://scholar.google.com/https://www.tandfonline.com/doi/abs/10.1080/02699930903485076|2010|1052|10|9283473996856444608|None|http://scholar.google.com/scholar?cites=9283473996856444608&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=9283473996856444608&hl=en&as_sdt=0,5|None|Many research fields concerned with the processing of information contained in human faces would benefit from face stimulus sets in which specific facial characteristics are systematically varied while other important picture characteristics are kept constant. Specifically, a face database in which displayed expressions, gaze direction, and head orientation are parametrically varied in a complete factorial design would be highly useful in many research domains. Furthermore, these stimuli should be standardised in several …
diff --git a/scraper/datasets/scholar/entries/Pruning Training Sets for Learning of Object Categories.csv b/scraper/datasets/scholar/entries/Pruning Training Sets for Learning of Object Categories.csv
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+Pruning training sets for learning of object categories|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/1467308/|2005|98|19|6629732990128685315|None|http://scholar.google.com/scholar?cites=6629732990128685315&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=6629732990128685315&hl=en&as_sdt=0,5|None|Training datasets for learning of object categories are often contaminated or imperfect. We explore an approach to automatically identify examples that are noisy or troublesome for learning and exclude them from the training set. The problem is relevant to learning in semi-supervised or unsupervised setting, as well as to learning when the training data is contaminated with wrongly labeled examples or when correctly labeled, but hard to learn examples, are present. We propose a fully automatic mechanism for noise cleaning …
diff --git a/scraper/datasets/scholar/entries/Recognize Complex Events from Static Images by Fusing Deep Channels.csv b/scraper/datasets/scholar/entries/Recognize Complex Events from Static Images by Fusing Deep Channels.csv
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+Recognize complex events from static images by fusing deep channels|http://scholar.google.com/https://www.cv-foundation.org/openaccess/content_cvpr_2015/app/1B_054.pdf|2015|62|18|14301800130435949971|http://scholar.google.com/https://www.cv-foundation.org/openaccess/content_cvpr_2015/app/1B_054.pdf|http://scholar.google.com/scholar?cites=14301800130435949971&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=14301800130435949971&hl=en&as_sdt=0,5|None|A considerable portion of web images capture events that occur in our personal lives or social activities. In this paper, we aim to develop an effective method for recognizing events from such images. Despite the sheer amount of study on event recognition, most existing methods rely on videos and are not directly applicable to this task. Generally, events are complex phenomena that involve interactions among people and objects, and therefore analysis of event photos requires techniques that can go beyond recognizing individual …
diff --git a/scraper/datasets/scholar/entries/Robust face landmark estimation under occlusion .csv b/scraper/datasets/scholar/entries/Robust face landmark estimation under occlusion .csv
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+Robust face landmark estimation under occlusion|http://scholar.google.com/https://www.cv-foundation.org/openaccess/content_iccv_2013/html/Burgos-Artizzu_Robust_Face_Landmark_2013_ICCV_paper.html|2013|441|16|6035683787196907858|None|http://scholar.google.com/scholar?cites=6035683787196907858&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=6035683787196907858&hl=en&as_sdt=0,5|None|Human faces captured in real-world conditions present large variations in shape and occlusions due to differences in pose, expression, use of accessories such as sunglasses and hats and interactions with objects (eg food). Current face landmark estimation approaches struggle under such conditions since they fail to provide a principled way of handling outliers. We propose a novel method, called Robust Cascaded Pose Regression (RCPR) which reduces exposure to outliers by detecting occlusions explicitly and using …
diff --git a/scraper/datasets/scholar/entries/SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception.csv b/scraper/datasets/scholar/entries/SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception.csv
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+++ b/scraper/datasets/scholar/entries/SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception.csv
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+SCUT-FBP: A benchmark dataset for facial beauty perception|http://scholar.google.com/https://arxiv.org/abs/1511.02459|2015|17|4|3066282784180910292|None|http://scholar.google.com/scholar?cites=3066282784180910292&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=3066282784180910292&hl=en&as_sdt=0,5|None|In this paper, a novel face dataset with attractiveness ratings, namely, the SCUT-FBP dataset, is developed for automatic facial beauty perception. This dataset provides a benchmark to evaluate the performance of different methods for facial attractiveness prediction, including the state-of-the-art deep learning method. The SCUT-FBP dataset contains face portraits of 500 Asian female subjects with attractiveness ratings, all of which have been verified in terms of rating distribution, standard deviation, consistency, and self …
diff --git a/scraper/datasets/scholar/entries/Situation Recognition: Visual Semantic Role Labeling for Image Understanding.csv b/scraper/datasets/scholar/entries/Situation Recognition: Visual Semantic Role Labeling for Image Understanding.csv
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+++ b/scraper/datasets/scholar/entries/Situation Recognition: Visual Semantic Role Labeling for Image Understanding.csv
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+Situation recognition: Visual semantic role labeling for image understanding|http://scholar.google.com/https://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Yatskar_Situation_Recognition_Visual_CVPR_2016_paper.html|2016|53|9|14769542088507071062|None|http://scholar.google.com/scholar?cites=14769542088507071062&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=14769542088507071062&hl=en&as_sdt=0,5|None|This paper introduces situation recognition, the problem of producing a concise summary of the situation an image depicts including:(1) the main activity (eg, clipping),(2) the participating actors, objects, substances, and locations (eg, man, shears, sheep, wool, and field) and most importantly (3) the roles these participants play in the activity (eg, the man is clipping, the shears are his tool, the wool is being clipped from the sheep, and the clipping is in a field). We use FrameNet, a verb and role lexicon developed by linguists, to define a …
diff --git a/scraper/datasets/scholar/entries/Spoofing Faces Using Makeup: An Investigative Study.csv b/scraper/datasets/scholar/entries/Spoofing Faces Using Makeup: An Investigative Study.csv
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+++ b/scraper/datasets/scholar/entries/Spoofing Faces Using Makeup: An Investigative Study.csv
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+Spoofing faces using makeup: An investigative study|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/7947686/|2017|6|8|1291042674502639294|None|http://scholar.google.com/scholar?cites=1291042674502639294&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=1291042674502639294&hl=en&as_sdt=0,5|None|Makeup can be used to alter the facial appearance of a person. Previous studies have established the potential of using makeup to obfuscate the identity of an individual with respect to an automated face matcher. In this work, we analyze the potential of using makeup for spoofing an identity, where an individual attempts to impersonate another person's facial appearance. In this regard, we first assemble a set of face images downloaded from the internet where individuals use facial cosmetics to impersonate …
diff --git a/scraper/datasets/scholar/entries/Sports Videos in the Wild (SVW): A Video Dataset for Sports Analysis.csv b/scraper/datasets/scholar/entries/Sports Videos in the Wild (SVW): A Video Dataset for Sports Analysis.csv
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+++ b/scraper/datasets/scholar/entries/Sports Videos in the Wild (SVW): A Video Dataset for Sports Analysis.csv
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+Sports videos in the wild (SVW): A video dataset for sports analysis|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/7163105/|2015|14|11|10001086963759053928|None|http://scholar.google.com/scholar?cites=10001086963759053928&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=10001086963759053928&hl=en&as_sdt=0,5|None|Considering the enormous creation rate of usergenerated videos on websites like YouTube, there is an immediate need for automatic categorization, recognition and analysis of videos. To develop algorithms for analyzing user-generated videos, unconstrained and representative datasets are of great significance. For this purpose, we collected a dataset of Sports Videos in the Wild (SVW), consisting of videos captured by users of the leading sports training mobile app (Coach's Eye) while practicing a sport or watching a game. The dataset …
diff --git a/scraper/datasets/scholar/entries/The Do's and Don'ts for CNN-based Face Verification.csv b/scraper/datasets/scholar/entries/The Do's and Don'ts for CNN-based Face Verification.csv
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+The Do's and Don'ts for CNN-based Face Verification.|http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w37/Bansal_The_Dos_and_ICCV_2017_paper.pdf|2017|21|7|16583671830808674747|http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w37/Bansal_The_Dos_and_ICCV_2017_paper.pdf|http://scholar.google.com/scholar?cites=16583671830808674747&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=16583671830808674747&hl=en&as_sdt=0,5|None|While the research community appears to have developed a consensus on the methods of acquiring annotated data, design and training of CNNs, many questions still remain to be answered. In this paper, we explore the following questions that are critical to face recognition research:(i) Can we train on still images and expect the systems to work on videos?(ii) Are deeper datasets better than wider datasets?(iii) Does adding label noise lead to improvement in performance of deep networks?(iv) Is alignment needed for face …
diff --git a/scraper/datasets/scholar/entries/The Extended Cohn-Kanade Dataset (CK+): A complete expression dataset for action unit and emotion-specified expression.csv b/scraper/datasets/scholar/entries/The Extended Cohn-Kanade Dataset (CK+): A complete expression dataset for action unit and emotion-specified expression.csv
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+++ b/scraper/datasets/scholar/entries/The Extended Cohn-Kanade Dataset (CK+): A complete expression dataset for action unit and emotion-specified expression.csv
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+The extended cohn-kanade dataset (ck+) a complete expression dataset for action unit and emotion-speified expression|None|2010|3|0|13817454793240235261|None|http://scholar.google.com/scholar?cites=13817454793240235261&as_sdt=2005&sciodt=0,5&hl=en|None|None|None
diff --git a/scraper/datasets/scholar/entries/The MegaFace Benchmark: 1 Million Faces for Recognition at Scale.csv b/scraper/datasets/scholar/entries/The MegaFace Benchmark: 1 Million Faces for Recognition at Scale.csv
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+++ b/scraper/datasets/scholar/entries/The MegaFace Benchmark: 1 Million Faces for Recognition at Scale.csv
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+The megaface benchmark: 1 million faces for recognition at scale|http://scholar.google.com/https://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Kemelmacher-Shlizerman_The_MegaFace_Benchmark_CVPR_2016_paper.html|2016|159|11|6051410257476935491|None|http://scholar.google.com/scholar?cites=6051410257476935491&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=6051410257476935491&hl=en&as_sdt=0,5|None|Recent face recognition experiments on a major benchmark LFW show stunning performance--a number of algorithms achieve near to perfect score, surpassing human recognition rates. In this paper, we advocate evaluations at the million scale (LFW includes only 13K photos of 5K people). To this end, we have assembled the MegaFace dataset and created the first MegaFace challenge. Our dataset includes One Million photos that capture more than 690K different individuals. The challenge evaluates performance of algorithms …
diff --git a/scraper/datasets/scholar/entries/UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild.csv b/scraper/datasets/scholar/entries/UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild.csv
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+++ b/scraper/datasets/scholar/entries/UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild.csv
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+UCF101: A dataset of 101 human actions classes from videos in the wild|http://scholar.google.com/https://arxiv.org/abs/1212.0402|2012|1211|11|10653986877352008041|None|http://scholar.google.com/scholar?cites=10653986877352008041&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=10653986877352008041&hl=en&as_sdt=0,5|None|We introduce UCF101 which is currently the largest dataset of human actions. It consists of 101 action classes, over 13k clips and 27 hours of video data. The database consists of realistic user uploaded videos containing camera motion and cluttered background. Additionally, we provide baseline action recognition results on this new dataset using standard bag of words approach with overall performance of 44.5%. To the best of our knowledge, UCF101 is currently the most challenging dataset of actions due to its large …
diff --git a/scraper/datasets/scholar/entries/UMDFaces: An Annotated Face Dataset for Training Deep Networks.csv b/scraper/datasets/scholar/entries/UMDFaces: An Annotated Face Dataset for Training Deep Networks.csv
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+++ b/scraper/datasets/scholar/entries/UMDFaces: An Annotated Face Dataset for Training Deep Networks.csv
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+Umdfaces: An annotated face dataset for training deep networks|http://scholar.google.com/https://ieeexplore.ieee.org/abstract/document/8272731/|2017|35|5|15417824747310072694|None|http://scholar.google.com/scholar?cites=15417824747310072694&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=15417824747310072694&hl=en&as_sdt=0,5|None|Recent progress in face detection (including keypoint detection), and recognition is mainly being driven by (i) deeper convolutional neural network architectures, and (ii) larger datasets. However, most of the large datasets are maintained by private companies and are not publicly available. The academic computer vision community needs larger and more varied datasets to make further progress. In this paper, we introduce a new face dataset, called UMDFaces, which has 367,888 annotated faces of 8,277 subjects. We also introduce …