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b/site/content/pages/datasets/duke_mtmc/assets/index.jpg Binary files differnew file mode 100644 index 00000000..6651c15c --- /dev/null +++ b/site/content/pages/datasets/duke_mtmc/assets/index.jpg diff --git a/site/content/pages/datasets/facebook/index.md b/site/content/pages/datasets/facebook/index.md new file mode 100644 index 00000000..6e3857fd --- /dev/null +++ b/site/content/pages/datasets/facebook/index.md @@ -0,0 +1,32 @@ +------------ + +status: published +title: Facebook +desc: TBD +subdesc: TBD +image: assets/background.jpg +caption: TBD +slug: facebook +published: 2019-2-23 +updated: 2019-2-23 +color: #aaaaff +authors: Adam Harvey + +------------ + +### Statistics + ++ Years: 2002-2004 ++ Images: 13,233 ++ Identities: 5,749 ++ Origin: Yahoo News Images ++ Funding: (Possibly, partially CIA) + +---- + +Ignore content below these lines + +--- + + +- Tool to create face datasets from Facebook <https://github.com/ankitaggarwal011/FaceGrab> diff --git a/site/content/pages/datasets/helen/assets/index.jpg b/site/content/pages/datasets/helen/assets/index.jpg Binary files differnew file mode 100644 index 00000000..37cb5882 --- /dev/null +++ b/site/content/pages/datasets/helen/assets/index.jpg diff --git a/site/content/pages/datasets/hrt_transgender/assets/index.jpg b/site/content/pages/datasets/hrt_transgender/assets/index.jpg Binary files differnew file mode 100644 index 00000000..4f3307c4 --- /dev/null +++ b/site/content/pages/datasets/hrt_transgender/assets/index.jpg diff --git a/site/content/pages/datasets/lfw/assets/index.jpg b/site/content/pages/datasets/lfw/assets/index.jpg Binary files differnew file mode 100644 index 00000000..1df0c4f6 --- /dev/null +++ b/site/content/pages/datasets/lfw/assets/index.jpg diff --git a/site/content/pages/datasets/lfw/assets/lfw_index.gif b/site/content/pages/datasets/lfw/assets/lfw_index.gif Binary files differdeleted file mode 100644 index 63a5c423..00000000 --- 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differnew file mode 100644 index 00000000..be14f27c --- /dev/null +++ b/site/content/pages/datasets/pubfig/assets/index.jpg diff --git a/site/content/pages/datasets/ytmu/assets/index.jpg b/site/content/pages/datasets/ytmu/assets/index.jpg Binary files differnew file mode 100644 index 00000000..6df15db5 --- /dev/null +++ b/site/content/pages/datasets/ytmu/assets/index.jpg diff --git a/site/content/pages/datasets/ytmu/assets/index_02.jpg b/site/content/pages/datasets/ytmu/assets/index_02.jpg Binary files differnew file mode 100644 index 00000000..30c863f6 --- /dev/null +++ b/site/content/pages/datasets/ytmu/assets/index_02.jpg diff --git a/site/content/pages/datasets/ytmu/assets/index_03.jpg b/site/content/pages/datasets/ytmu/assets/index_03.jpg Binary files differnew file mode 100644 index 00000000..20ccae90 --- /dev/null +++ b/site/content/pages/datasets/ytmu/assets/index_03.jpg diff --git a/site/content/pages/research/01_from_1_to_100_pixels/index.md b/site/content/pages/research/01_from_1_to_100_pixels/index.md index 0123fffe..29204168 100644 --- a/site/content/pages/research/01_from_1_to_100_pixels/index.md +++ b/site/content/pages/research/01_from_1_to_100_pixels/index.md @@ -45,4 +45,6 @@ Find specific cases of facial resolution being used in legal cases, forensic inv - NIST report on sres states several resolutions - "Results show that the tested face recognition systems yielded similar performance for query sets with eye-to-eye distance from 60 pixels to 30 pixels" [^nist_sres] -[^nist_sres]: NIST 906932. Performance Assessment of Face Recognition Using Super-Resolution. Shuowen Hu, Robert Maschal, S. Susan Young, Tsai Hong Hong, Jonathon P. Phillips
\ No newline at end of file +[^nist_sres]: NIST 906932. Performance Assessment of Face Recognition Using Super-Resolution. Shuowen Hu, Robert Maschal, S. Susan Young, Tsai Hong Hong, Jonathon P. Phillips + +- "Note that we only keep the images with a minimal side length of 80 pixels." and "a face will be labeled as “Ignore” if it is very difficult to be detected due to blurring, severe deformation and unrecognizable eyes, or the side length of its bounding box is less than 32 pixels." Ge_Detecting_Masked_Faces_CVPR_2017_paper.pdf
\ No newline at end of file diff --git a/site/datasets/final/ijb_c_sample.csv b/site/datasets/final/ijb_c_sample.csv new file mode 100644 index 00000000..15bfccab --- /dev/null +++ b/site/datasets/final/ijb_c_sample.csv @@ -0,0 +1,141 @@ +index,dataset_name,key,lat,lng,loc,loc_type,paper_id,paper_type,paper_url,title,year +0,IJB-A,ijb_c,0.0,0.0,,,140c95e53c619eac594d70f6369f518adfea12ef,main,http://www.cv-foundation.org/openaccess/content_cvpr_2015/app/1B_089_ext.pdf,Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A,2015 +1,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,872dfdeccf99bbbed7c8f1ea08afb2d713ebe085,citation,https://arxiv.org/pdf/1703.09507.pdf,L2-constrained Softmax Loss for Discriminative Face Verification,2017 +2,IJB-A,ijb_c,38.8920756,-104.79716389,"University of Colorado, Colorado Springs",edu,146a7ecc7e34b85276dd0275c337eff6ba6ef8c0,citation,https://arxiv.org/pdf/1611.06158v1.pdf,AFFACT: Alignment-free facial attribute classification technique,2017 +3,IJB-A,ijb_c,51.7534538,-1.25400997,University of Oxford,edu,313d5eba97fe064bdc1f00b7587a4b3543ef712a,citation,https://pdfs.semanticscholar.org/cb7f/93467b0ec1afd43d995e511f5d7bf052a5af.pdf,Compact Deep Aggregation for Set Retrieval,2018 +4,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,5865b6d83ba6dbbf9167f1481e9339c2ef1d1f6b,citation,https://doi.org/10.1109/ICPR.2016.7900278,Regularized metric adaptation for unconstrained face verification,2016 +5,IJB-A,ijb_c,37.4102193,-122.05965487,Carnegie Mellon University,edu,48a9241edda07252c1aadca09875fabcfee32871,citation,https://arxiv.org/pdf/1611.08657v5.pdf,Convolutional Experts Constrained Local Model for Facial Landmark Detection,2017 +6,IJB-A,ijb_c,42.718568,-84.47791571,Michigan State University,edu,86204fc037936754813b91898377e8831396551a,citation,https://arxiv.org/pdf/1709.01442.pdf,Dense Face Alignment,2017 +7,IJB-A,ijb_c,22.57423855,88.4337303,"Institute of Engineering and Management, Kolkata, India",edu,b2cb335ded99b10f37002d09753bd5a6ea522ef1,citation,https://doi.org/10.1109/ISBA.2017.7947679,Analysis of adaptability of deep features for verifying blurred and cross-resolution images,2017 +8,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,b2cb335ded99b10f37002d09753bd5a6ea522ef1,citation,https://doi.org/10.1109/ISBA.2017.7947679,Analysis of adaptability of deep features for verifying blurred and cross-resolution images,2017 +9,IJB-A,ijb_c,45.7835966,4.7678948,École Centrale de Lyon,edu,486840f4f524e97f692a7f6b42cd19019ee71533,citation,https://arxiv.org/pdf/1703.08388v2.pdf,DeepVisage: Making Face Recognition Simple Yet With Powerful Generalization Skills,2017 +10,IJB-A,ijb_c,48.832493,2.267474,Safran Identity and Security,company,486840f4f524e97f692a7f6b42cd19019ee71533,citation,https://arxiv.org/pdf/1703.08388v2.pdf,DeepVisage: Making Face Recognition Simple Yet With Powerful Generalization Skills,2017 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Results,2018 +15,IJB-A,ijb_c,40.47913175,-74.43168868,Rutgers University,edu,377f2b65e6a9300448bdccf678cde59449ecd337,citation,https://arxiv.org/pdf/1804.10275.pdf,Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results,2018 +16,IJB-A,ijb_c,42.718568,-84.47791571,Michigan State University,edu,cd55fb30737625e86454a2861302b96833ed549d,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7139094,Annotating Unconstrained Face Imagery: A scalable approach,2015 +17,IJB-A,ijb_c,38.95187,-77.363259,"Noblis, Falls Church, VA, U.S.A.",company,cd55fb30737625e86454a2861302b96833ed549d,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7139094,Annotating Unconstrained Face Imagery: A scalable approach,2015 +18,IJB-A,ijb_c,46.0501558,14.46907327,University of Ljubljana,edu,5226296884b3e151ce317a37f94827dbda0b9d16,citation,https://doi.org/10.1109/IWBF.2016.7449690,Deep pair-wise similarity learning for face recognition,2016 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Representation Learning for Unconstrained Face Identification,2017 +34,IJB-A,ijb_c,-35.23656905,149.08446994,University of Canberra,edu,0c1d85a197a1f5b7376652a485523e616a406273,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2017.169,Joint Registration and Representation Learning for Unconstrained Face Identification,2017 +35,IJB-A,ijb_c,32.77824165,34.99565673,Open University of Israel,edu,c75e6ce54caf17b2780b4b53f8d29086b391e839,citation,https://arxiv.org/pdf/1802.00542.pdf,"ExpNet: Landmark-Free, Deep, 3D Facial Expressions",2018 +36,IJB-A,ijb_c,42.718568,-84.47791571,Michigan State University,edu,450c6a57f19f5aa45626bb08d7d5d6acdb863b4b,citation,https://arxiv.org/pdf/1805.00611.pdf,Towards Interpretable Face Recognition,2018 +37,IJB-A,ijb_c,51.7534538,-1.25400997,University of Oxford,edu,30180f66d5b4b7c0367e4b43e2b55367b72d6d2a,citation,http://www.robots.ox.ac.uk/~vgg/publications/2017/Crosswhite17/crosswhite17.pdf,Template Adaptation for Face Verification and 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First Manually Collected, In-the-Wild Age Database",2017 +50,IJB-A,ijb_c,50.8142701,8.771435,Philipps-Universität Marburg,edu,5981c309bd0ffd849c51b1d8a2ccc481a8ec2f5c,citation,https://doi.org/10.1109/ICT.2017.7998256,SmartFace: Efficient face detection on smartphones for wireless on-demand emergency networks,2017 +51,IJB-A,ijb_c,42.718568,-84.47791571,Michigan State University,edu,a2b4a6c6b32900a066d0257ae6d4526db872afe2,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8272466,Learning Face Image Quality From Human Assessments,2018 +52,IJB-A,ijb_c,39.9601488,116.35193921,Beijing University of Posts and Telecommunications,edu,3dfb822e16328e0f98a47209d7ecd242e4211f82,citation,https://arxiv.org/pdf/1708.08197.pdf,Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments,2017 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+57,IJB-A,ijb_c,28.5456282,77.2731505,"IIIT Delhi, India",edu,3cf1f89d73ca4b25399c237ed3e664a55cd273a2,citation,https://arxiv.org/pdf/1710.02914.pdf,Face Sketch Matching via Coupled Deep Transform Learning,2017 +58,IJB-A,ijb_c,-27.49741805,153.01316956,University of Queensland,edu,f27fd2a1bc229c773238f1912db94991b8bf389a,citation,https://doi.org/10.1109/IVCNZ.2016.7804414,How do you develop a face detector for the unconstrained environment?,2016 +59,IJB-A,ijb_c,39.86742125,32.73519072,Hacettepe University,edu,9865fe20df8fe11717d92b5ea63469f59cf1635a,citation,https://arxiv.org/pdf/1805.07566.pdf,Wildest Faces: Face Detection and Recognition in Violent Settings,2018 +60,IJB-A,ijb_c,39.87549675,32.78553506,Middle East Technical University,edu,9865fe20df8fe11717d92b5ea63469f59cf1635a,citation,https://arxiv.org/pdf/1805.07566.pdf,Wildest Faces: Face Detection and Recognition in Violent Settings,2018 +61,IJB-A,ijb_c,28.2290209,112.99483204,"National University of Defense Technology, China",edu,c1cc2a2a1ab66f6c9c6fabe28be45d1440a57c3d,citation,https://pdfs.semanticscholar.org/aae7/a5182e59f44b7bb49f61999181ce011f800b.pdf,Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis,2017 +62,IJB-A,ijb_c,1.2962018,103.77689944,National University of Singapore,edu,c1cc2a2a1ab66f6c9c6fabe28be45d1440a57c3d,citation,https://pdfs.semanticscholar.org/aae7/a5182e59f44b7bb49f61999181ce011f800b.pdf,Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis,2017 +63,IJB-A,ijb_c,17.4454957,78.34854698,International Institute of Information Technology,edu,f5eb411217f729ad7ae84bfd4aeb3dedb850206a,citation,https://pdfs.semanticscholar.org/f5eb/411217f729ad7ae84bfd4aeb3dedb850206a.pdf,Tackling Low Resolution for Better Scene Understanding,2018 +64,IJB-A,ijb_c,40.51865195,-74.44099801,State University of New Jersey,edu,96e731e82b817c95d4ce48b9e6b08d2394937cf8,citation,http://arxiv.org/pdf/1508.01722v2.pdf,Unconstrained face verification using deep CNN features,2016 +65,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,96e731e82b817c95d4ce48b9e6b08d2394937cf8,citation,http://arxiv.org/pdf/1508.01722v2.pdf,Unconstrained face verification using deep CNN features,2016 +66,IJB-A,ijb_c,32.77824165,34.99565673,Open University of Israel,edu,870433ba89d8cab1656e57ac78f1c26f4998edfb,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2017.163,Regressing Robust and Discriminative 3D Morphable Models with a Very Deep Neural Network,2017 +67,IJB-A,ijb_c,55.6801502,12.572327,University of Copenhagen,edu,3dfd94d3fad7e17f52a8ae815eb9cc5471172bc0,citation,http://pdfs.semanticscholar.org/3dfd/94d3fad7e17f52a8ae815eb9cc5471172bc0.pdf,Face2Text: Collecting an Annotated Image Description Corpus for the Generation of Rich Face Descriptions,2018 +68,IJB-A,ijb_c,35.9023226,14.4834189,University of Malta,edu,3dfd94d3fad7e17f52a8ae815eb9cc5471172bc0,citation,http://pdfs.semanticscholar.org/3dfd/94d3fad7e17f52a8ae815eb9cc5471172bc0.pdf,Face2Text: Collecting an Annotated Image Description Corpus for the Generation of Rich Face Descriptions,2018 +69,IJB-A,ijb_c,34.0224149,-118.28634407,University of Southern California,edu,6341274aca0c2977c3e1575378f4f2126aa9b050,citation,http://arxiv.org/pdf/1609.03536v1.pdf,A multi-scale cascade fully convolutional network face detector,2016 +70,IJB-A,ijb_c,41.70456775,-86.23822026,University of Notre Dame,edu,17479e015a2dcf15d40190e06419a135b66da4e0,citation,https://arxiv.org/pdf/1610.08119.pdf,Predicting First Impressions With Deep Learning,2017 +71,IJB-A,ijb_c,37.4102193,-122.05965487,Carnegie Mellon University,edu,a0b1990dd2b4cd87e4fd60912cc1552c34792770,citation,https://pdfs.semanticscholar.org/a0b1/990dd2b4cd87e4fd60912cc1552c34792770.pdf,Deep Constrained Local Models for Facial Landmark Detection,2016 +72,IJB-A,ijb_c,30.642769,104.06751175,"Sichuan University, Chengdu",edu,772474b5b0c90629f4d9c223fd9c1ef45e1b1e66,citation,https://doi.org/10.1109/BTAS.2017.8272716,Multi-dim: A multi-dimensional face database towards the application of 3D technology in real-world scenarios,2017 +73,IJB-A,ijb_c,38.8920756,-104.79716389,"University of Colorado, Colorado Springs",edu,4b3f425274b0c2297d136f8833a31866db2f2aec,citation,http://doi.ieeecomputersociety.org/10.1109/CVPRW.2017.85,Toward Open-Set Face Recognition,2017 +74,IJB-A,ijb_c,56.46255985,84.95565495,Tomsk Polytechnic University,edu,17ded725602b4329b1c494bfa41527482bf83a6f,citation,http://pdfs.semanticscholar.org/cb10/434a5d68ffbe9ed0498771192564ecae8894.pdf,Compact Convolutional Neural Network Cascade for Face Detection,2015 +75,IJB-A,ijb_c,37.3351908,-121.88126008,San Jose State University,edu,14b016c7a87d142f4b9a0e6dc470dcfc073af517,citation,http://ws680.nist.gov/publication/get_pdf.cfm?pub_id=918912,Modest proposals for improving biometric recognition papers,2015 +76,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,93420d9212dd15b3ef37f566e4d57e76bb2fab2f,citation,https://arxiv.org/pdf/1611.00851.pdf,An All-In-One Convolutional Neural Network for Face Analysis,2017 +77,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,def2983576001bac7d6461d78451159800938112,citation,https://arxiv.org/pdf/1705.07426.pdf,The Do’s and Don’ts for CNN-Based Face Verification,2017 +78,IJB-A,ijb_c,42.718568,-84.47791571,Michigan State University,edu,4b605e6a9362485bfe69950432fa1f896e7d19bf,citation,http://biometrics.cse.msu.edu/Publications/Face/BlantonAllenMillerKalkaJain_CVPRWB2016_HID.pdf,A Comparison of Human and Automated Face Verification Accuracy on Unconstrained Image Sets,2016 +79,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,8d3e95c31c93548b8c71dbeee2e9f7180067a888,citation,https://doi.org/10.1109/ICPR.2016.7899841,Template regularized sparse coding for face verification,2016 +80,IJB-A,ijb_c,42.8271556,-73.8780481,GE Global Research,company,8d3e95c31c93548b8c71dbeee2e9f7180067a888,citation,https://doi.org/10.1109/ICPR.2016.7899841,Template regularized sparse coding for face verification,2016 +81,IJB-A,ijb_c,25.0410728,121.6147562,Institute of Information Science,edu,337dd4aaca2c5f9b5d2de8e0e2401b5a8feb9958,citation,https://arxiv.org/pdf/1810.11160.pdf,Data-specific Adaptive Threshold for Face Recognition and Authentication,2018 +82,IJB-A,ijb_c,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,0aeb5020003e0c89219031b51bd30ff1bceea363,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.525,Sparsifying Neural Network Connections for Face Recognition,2016 +83,IJB-A,ijb_c,22.42031295,114.20788644,Chinese University of Hong Kong,edu,0aeb5020003e0c89219031b51bd30ff1bceea363,citation,http://doi.ieeecomputersociety.org/10.1109/CVPR.2016.525,Sparsifying Neural Network Connections for Face Recognition,2016 +84,IJB-A,ijb_c,42.718568,-84.47791571,Michigan State University,edu,99daa2839213f904e279aec7cef26c1dfb768c43,citation,https://arxiv.org/pdf/1805.02283.pdf,DocFace: Matching ID Document Photos to Selfies,2018 +85,IJB-A,ijb_c,43.7776426,11.259765,University of Florence,edu,71ca8b6e84c17b3e68f980bfb8cddc837100f8bf,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7899774,Effective 3D based frontalization for unconstrained face recognition,2016 +86,IJB-A,ijb_c,51.49887085,-0.17560797,Imperial College London,edu,c43ed9b34cad1a3976bac7979808eb038d88af84,citation,https://arxiv.org/pdf/1804.03675.pdf,Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model,2018 +87,IJB-A,ijb_c,51.24303255,-0.59001382,University of Surrey,edu,c43ed9b34cad1a3976bac7979808eb038d88af84,citation,https://arxiv.org/pdf/1804.03675.pdf,Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model,2018 +88,IJB-A,ijb_c,37.3936717,-122.0807262,Facebook,company,628a3f027b7646f398c68a680add48c7969ab1d9,citation,https://pdfs.semanticscholar.org/628a/3f027b7646f398c68a680add48c7969ab1d9.pdf,Plan for Final Year Project : HKU-Face : A Large Scale Dataset for Deep Face Recognition,2017 +89,IJB-A,ijb_c,40.2773077,-7.5095801,University of Beira Interior,edu,61262450d4d814865a4f9a84299c24daa493f66e,citation,http://doi.org/10.1007/s10462-016-9474-x,Biometric recognition in surveillance scenarios: a survey,2016 +90,IJB-A,ijb_c,-31.95040445,115.79790037,University of Western Australia,edu,626913b8fcbbaee8932997d6c4a78fe1ce646127,citation,https://arxiv.org/pdf/1711.05942.pdf,Learning from Millions of 3D Scans for Large-scale 3D Face Recognition,2017 +91,IJB-A,ijb_c,35.9023226,14.4834189,University of Malta,edu,4efd58102ff46b7435c9ec6d4fc3dd21d93b15b4,citation,https://doi.org/10.1109/TIFS.2017.2788002,"Matching Software-Generated Sketches to Face Photographs With a Very Deep CNN, Morphed Faces, and Transfer Learning",2018 +92,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,b6f758be954d34817d4ebaa22b30c63a4b8ddb35,citation,http://arxiv.org/abs/1703.04835,A Proximity-Aware Hierarchical Clustering of Faces,2017 +93,IJB-A,ijb_c,32.77824165,34.99565673,Open University of Israel,edu,0a34fe39e9938ae8c813a81ae6d2d3a325600e5c,citation,https://arxiv.org/pdf/1708.07517.pdf,FacePoseNet: Making a Case for Landmark-Free Face Alignment,2017 +94,IJB-A,ijb_c,40.2773077,-7.5095801,University of Beira Interior,edu,84ae55603bffda40c225fe93029d39f04793e01f,citation,https://doi.org/10.1109/ICB.2016.7550066,ICB-RW 2016: International challenge on biometric recognition in the wild,2016 +95,IJB-A,ijb_c,41.70456775,-86.23822026,University of Notre Dame,edu,73ea06787925157df519a15ee01cc3dc1982a7e0,citation,https://arxiv.org/pdf/1811.01474.pdf,Fast Face Image Synthesis with Minimal Training,2018 +96,IJB-A,ijb_c,42.718568,-84.47791571,Michigan State University,edu,c6382de52636705be5898017f2f8ed7c70d7ae96,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7139089,Unconstrained face detection: State of the art baseline and challenges,2015 +97,IJB-A,ijb_c,38.95187,-77.363259,"Noblis, Falls Church, VA, U.S.A.",company,c6382de52636705be5898017f2f8ed7c70d7ae96,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7139089,Unconstrained face detection: State of the art baseline and challenges,2015 +98,IJB-A,ijb_c,40.47913175,-74.43168868,Rutgers University,edu,eee06d68497be8bf3a8aba4fde42a13aa090b301,citation,https://arxiv.org/pdf/1806.11191.pdf,CR-GAN: Learning Complete Representations for Multi-view Generation,2018 +99,IJB-A,ijb_c,35.3103441,-80.73261617,University of North Carolina at Charlotte,edu,eee06d68497be8bf3a8aba4fde42a13aa090b301,citation,https://arxiv.org/pdf/1806.11191.pdf,CR-GAN: Learning Complete Representations for Multi-view Generation,2018 +100,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,a3201e955d6607d383332f3a12a7befa08c5a18c,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7900276,VLAD encoded Deep Convolutional features for unconstrained face verification,2016 +101,IJB-A,ijb_c,40.47913175,-74.43168868,Rutgers University,edu,a3201e955d6607d383332f3a12a7befa08c5a18c,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7900276,VLAD encoded Deep Convolutional features for unconstrained face verification,2016 +102,IJB-A,ijb_c,22.42031295,114.20788644,Chinese University of Hong Kong,edu,52d7eb0fbc3522434c13cc247549f74bb9609c5d,citation,https://arxiv.org/pdf/1511.06523.pdf,WIDER FACE: A Face Detection Benchmark,2016 +103,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,19458454308a9f56b7de76bf7d8ff8eaa52b0173,citation,https://pdfs.semanticscholar.org/1945/8454308a9f56b7de76bf7d8ff8eaa52b0173.pdf,Deep Features for Recognizing Disguised Faces in the Wild,0 +104,IJB-A,ijb_c,43.7776426,11.259765,University of Florence,edu,746c0205fdf191a737df7af000eaec9409ede73f,citation,http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8423119,Investigating Nuisances in DCNN-Based Face Recognition,2018 +105,IJB-A,ijb_c,47.5612651,7.5752961,University of Basel,edu,0081e2188c8f34fcea3e23c49fb3e17883b33551,citation,http://pdfs.semanticscholar.org/0081/e2188c8f34fcea3e23c49fb3e17883b33551.pdf,Training Deep Face Recognition Systems with Synthetic Data,2018 +106,IJB-A,ijb_c,37.4102193,-122.05965487,Carnegie Mellon University,edu,2b869d5551b10f13bf6fcdb8d13f0aa4d1f59fc4,citation,https://arxiv.org/pdf/1803.00130.pdf,Ring loss: Convex Feature Normalization for Face Recognition,2018 +107,IJB-A,ijb_c,28.2290209,112.99483204,"National University of Defense Technology, China",edu,5f771fed91c8e4b666489ba2384d0705bcf75030,citation,https://arxiv.org/pdf/1804.03287.pdf,Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing,2018 +108,IJB-A,ijb_c,1.2962018,103.77689944,National University of Singapore,edu,5f771fed91c8e4b666489ba2384d0705bcf75030,citation,https://arxiv.org/pdf/1804.03287.pdf,Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing,2018 +109,IJB-A,ijb_c,42.3889785,-72.5286987,University of Massachusetts,edu,2241eda10b76efd84f3c05bdd836619b4a3df97e,citation,http://arxiv.org/pdf/1506.01342v5.pdf,One-to-many face recognition with bilinear CNNs,2016 +110,IJB-A,ijb_c,22.42031295,114.20788644,Chinese University of Hong Kong,edu,58d76380d194248b3bb291b8c7c5137a0a376897,citation,https://pdfs.semanticscholar.org/58d7/6380d194248b3bb291b8c7c5137a0a376897.pdf,FaceID-GAN : Learning a Symmetry Three-Player GAN for Identity-Preserving Face Synthesis,2018 +111,IJB-A,ijb_c,22.59805605,113.98533784,Shenzhen Institutes of Advanced Technology,edu,58d76380d194248b3bb291b8c7c5137a0a376897,citation,https://pdfs.semanticscholar.org/58d7/6380d194248b3bb291b8c7c5137a0a376897.pdf,FaceID-GAN : Learning a Symmetry Three-Player GAN for Identity-Preserving Face Synthesis,2018 +112,IJB-A,ijb_c,42.718568,-84.47791571,Michigan State University,edu,7fb5006b6522436ece5bedf509e79bdb7b79c9a7,citation,https://pdfs.semanticscholar.org/7fb5/006b6522436ece5bedf509e79bdb7b79c9a7.pdf,Multi-Task Convolutional Neural Network for Face Recognition,2017 +113,IJB-A,ijb_c,-27.49741805,153.01316956,University of Queensland,edu,28646c6220848db46c6944967298d89a6559c700,citation,https://pdfs.semanticscholar.org/2864/6c6220848db46c6944967298d89a6559c700.pdf,It takes two to tango : Cascading off-the-shelf face detectors,2018 +114,IJB-A,ijb_c,51.7534538,-1.25400997,University of Oxford,edu,5812d8239d691e99d4108396f8c26ec0619767a6,citation,https://arxiv.org/pdf/1810.09951.pdf,GhostVLAD for set-based face recognition,2018 +115,IJB-A,ijb_c,25.01353105,121.54173736,National Taiwan University of Science and Technology,edu,e4c3587392d477b7594086c6f28a00a826abf004,citation,https://doi.org/10.1109/ICIP.2017.8296998,Face recognition by facial attribute assisted network,2017 +116,IJB-A,ijb_c,1.3484104,103.68297965,Nanyang Technological University,edu,47190d213caef85e8b9dd0d271dbadc29ed0a953,citation,https://arxiv.org/pdf/1807.11649.pdf,The Devil of Face Recognition is in the Noise,2018 +117,IJB-A,ijb_c,32.87935255,-117.23110049,"University of California, San Diego",edu,47190d213caef85e8b9dd0d271dbadc29ed0a953,citation,https://arxiv.org/pdf/1807.11649.pdf,The Devil of Face Recognition is in the Noise,2018 +118,IJB-A,ijb_c,39.2899685,-76.62196103,University of Maryland,edu,ce6d60b69eb95477596535227958109e07c61e1e,citation,http://www.rci.rutgers.edu/~vmp93/Conference_pub/BTAS_2015_FVFF_JunCheng_Chen.pdf,Unconstrained face verification using fisher vectors computed from frontalized faces,2015 +119,IJB-A,ijb_c,29.7207902,-95.34406271,University of Houston,edu,38d8ff137ff753f04689e6b76119a44588e143f3,citation,http://pdfs.semanticscholar.org/38d8/ff137ff753f04689e6b76119a44588e143f3.pdf,When 3D-Aided 2D Face Recognition Meets Deep Learning: An extended UR2D for Pose-Invariant Face Recognition,2017 +120,IJB-A,ijb_c,39.9082804,116.2458527,University of Chinese Academy of Sciences,edu,9627f28ea5f4c389350572b15968386d7ce3fe49,citation,https://arxiv.org/pdf/1802.07447.pdf,Load Balanced GANs for Multi-view Face Image Synthesis,2018 +121,IJB-A,ijb_c,34.0224149,-118.28634407,University of Southern California,edu,4e7ed13e541b8ed868480375785005d33530e06d,citation,http://doi.ieeecomputersociety.org/10.1109/WACV.2016.7477555,Face recognition using deep multi-pose representations,2016 +122,IJB-A,ijb_c,32.77824165,34.99565673,Open University of Israel,edu,582edc19f2b1ab2ac6883426f147196c8306685a,citation,http://pdfs.semanticscholar.org/be6c/db7b181e73f546d43cf2ab6bc7181d7d619b.pdf,Do We Really Need to Collect Millions of Faces for Effective Face Recognition?,2016 +123,IJB-A,ijb_c,37.4102193,-122.05965487,Carnegie Mellon University,edu,87e6cb090aecfc6f03a3b00650a5c5f475dfebe1,citation,https://pdfs.semanticscholar.org/87e6/cb090aecfc6f03a3b00650a5c5f475dfebe1.pdf,Holistically Constrained Local Model: Going Beyond Frontal Poses for Facial Landmark Detection,2016 +124,IJB-A,ijb_c,34.0224149,-118.28634407,University of Southern California,edu,87e6cb090aecfc6f03a3b00650a5c5f475dfebe1,citation,https://pdfs.semanticscholar.org/87e6/cb090aecfc6f03a3b00650a5c5f475dfebe1.pdf,Holistically Constrained Local Model: Going Beyond Frontal Poses for Facial Landmark Detection,2016 +125,IJB-A,ijb_c,39.65404635,-79.96475355,West Virginia University,edu,3b9b200e76a35178da940279d566bbb7dfebb787,citation,http://pdfs.semanticscholar.org/3b9b/200e76a35178da940279d566bbb7dfebb787.pdf,Learning Channel Inter-dependencies at Multiple Scales on Dense Networks for Face Recognition,2017 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