diff options
| author | jules@lens <julescarbon@gmail.com> | 2019-02-18 13:53:18 +0100 |
|---|---|---|
| committer | jules@lens <julescarbon@gmail.com> | 2019-02-18 13:53:18 +0100 |
| commit | 3fc5bb42b0dd94b56d0f11b1568d30a1ff835629 (patch) | |
| tree | 0a56e7f9cb84bda15a6e3074d1eba8312cc058f5 /site/datasets/final/megaface.json | |
| parent | b28f65ad5016ba3c3c9f973bd2a64ea3c8a3f84c (diff) | |
rebuild everything
Diffstat (limited to 'site/datasets/final/megaface.json')
| -rw-r--r-- | site/datasets/final/megaface.json | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/site/datasets/final/megaface.json b/site/datasets/final/megaface.json index c2cb2154..b7c466d6 100644 --- a/site/datasets/final/megaface.json +++ b/site/datasets/final/megaface.json @@ -1 +1 @@ -{"id": "28d4e027c7e90b51b7d8908fce68128d1964668a", "dataset": {"key": "megaface", "name_short": "MegaFace", "using": "Y", "ft_share": "1", "subset_of": "", "superset_of": "", "name_full": "MegaFace Dataset", "url": "http://megaface.cs.washington.edu/", "added_on": "", "faces": "", "pdf_paper": "Y", "comments": "", "": "", "relevance": "10"}, "statistics": {"key": "megaface", "name": "MegaFace", "berit": "Y", "charlie": "", "adam": "", "priority": "N", "wild": "Y", "indoor": "", "outdoor": "", "cyberspace": "Y", "names": "", "downloaded": "Y", "year_start": "", "year_end": "", "year_published": "2016", "ongoing": "", "images": "4,753,520 ", "videos": "", "faces_unique": "672,057 ", "total_faces": "", "img_per_person": "", "num_cameras": "", "faces_persons": "", "female": "", "male": "", "landmarks": "", "width": "", "height": "", "color": "", "gray": "", "derivative_of": "", "tags": "fr", "source": "flickr", "purpose_short": "million scale face recognition", "size_gb": "", "agreement": "", "agree_requied": "", "agreement_signed": "", "comment": "", "comment 2": "", "comment 3": "", "": ""}, "paper": {"paper_id": "28d4e027c7e90b51b7d8908fce68128d1964668a", "key": "megaface", "title": "Level Playing Field for Million Scale Face Recognition", "year": "2017", "pdf": ["https://arxiv.org/pdf/1705.00393.pdf"], "address": "", "name": "MegaFace", "doi": []}, "address": null, "additional_papers": [{"paper_id": "96e0cfcd81cdeb8282e29ef9ec9962b125f379b0", "key": "megaface", "title": "The MegaFace Benchmark: 1 Million Faces for Recognition at Scale", "year": "2016", "pdf": ["https://arxiv.org/pdf/1512.00596.pdf"], "address": "", "name": "MegaFace", "doi": []}], "citations": [{"id": "1345fb7700389f9d02f203b3cb25ac3594855054", "title": "Hierarchical Training for Large Scale Face Recognition with Few Samples Per Subject", "addresses": [{"address": "Chinese Academy of Sciences", "lat": "40.00447950", "lng": "116.37023800", "type": "edu"}], "year": "2018", "pdf": []}, {"id": "b59cee1f647737ec3296ccb3daa25c890359c307", "title": "Continuously Reproducing Toolchains in Pattern Recognition and Machine Learning Experiments", "addresses": [{"address": "IDIAP Research Institute", "lat": "46.10923700", "lng": "7.08453549", "type": "edu"}], "year": "2017", "pdf": ["https://pdfs.semanticscholar.org/b59c/ee1f647737ec3296ccb3daa25c890359c307.pdf"]}, {"id": "ebb3d5c70bedf2287f9b26ac0031004f8f617b97", "title": "Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans", "addresses": [{"address": "Electrical and Computer Engineering", "lat": "33.58667840", "lng": "-101.87539204", "type": "edu"}, {"address": "University of Maryland", "lat": "39.28996850", "lng": "-76.62196103", "type": "edu"}], "year": "2018", "pdf": []}, {"id": "f3a59d85b7458394e3c043d8277aa1ffe3cdac91", "title": "Query-Free Attacks on Industry-Grade Face Recognition Systems under Resource Constraints", "addresses": [{"address": "Indiana University", "lat": "39.86948105", "lng": "-84.87956905", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1802.09900.pdf"]}, {"id": "cd6aaa37fffd0b5c2320f386be322b8adaa1cc68", "title": "Deep Face Recognition: A Survey", "addresses": [{"address": "Beijing University of Posts and Telecommunications", "lat": "39.96014880", "lng": "116.35193921", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1804.06655.pdf"]}, {"id": "4d46fd59364ed5ec8f50abe68cd7886379bfd80a", "title": "Learning from Millions of 3D Scans for Large-Scale 3D Face Recognition", "addresses": [{"address": "University of Western Australia", "lat": "-31.95040445", "lng": "115.79790037", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1711.05942.pdf"]}, {"id": "9f65319b8a33c8ec11da2f034731d928bf92e29d", "title": "TAKING ROLL : A PIPELINE FOR FACE RECOGNITION", "addresses": [{"address": "Louisiana State University", "lat": "30.40550035", "lng": "-91.18620474", "type": "edu"}], "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/9f65/319b8a33c8ec11da2f034731d928bf92e29d.pdf"]}, {"id": "9e31e77f9543ab42474ba4e9330676e18c242e72", "title": "The Devil of Face Recognition is in the Noise", "addresses": [{"address": "University of California, San Diego", "lat": "32.87935255", "lng": "-117.23110049", "type": "edu"}, {"address": "Nanyang Technological University", "lat": "1.34841040", "lng": "103.68297965", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1807.11649.pdf"]}, {"id": "841855205818d3a6d6f85ec17a22515f4f062882", "title": "Low Resolution Face Recognition in the Wild", "addresses": [{"address": "University of Notre Dame", "lat": "41.70456775", "lng": "-86.23822026", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1805.11529.pdf"]}, {"id": "8efda5708bbcf658d4f567e3866e3549fe045bbb", "title": "Pre-trained Deep Convolutional Neural Networks for Face Recognition", "addresses": [{"address": "University of Groningen", "lat": "53.21967825", "lng": "6.56251482", "type": "edu"}], "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/8efd/a5708bbcf658d4f567e3866e3549fe045bbb.pdf"]}, {"id": "9865fe20df8fe11717d92b5ea63469f59cf1635a", "title": "Wildest Faces: Face Detection and Recognition in Violent Settings", "addresses": [{"address": "Hacettepe University", "lat": "39.86742125", "lng": "32.73519072", "type": "edu"}, {"address": "Middle East Technical University", "lat": "39.87549675", "lng": "32.78553506", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1805.07566.pdf"]}, {"id": "e64c166dc5bb33bc61462a8b5ac92edb24d905a1", "title": "Fast Face Image Synthesis with Minimal Training.", "addresses": [{"address": "University of Notre Dame", "lat": "41.70456775", "lng": "-86.23822026", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1811.01474.pdf"]}, {"id": "3c5ba48d25fbe24691ed060fa8f2099cc9eba14f", "title": "Racial Faces in-the-Wild: Reducing Racial Bias by Deep Unsupervised Domain Adaptation", "addresses": [{"address": "Beijing University of Posts and Telecommunications", "lat": "39.96014880", "lng": "116.35193921", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1812.00194.pdf"]}, {"id": "7a7fddb3020e0c2dd4e3fe275329eb10f1cfbb8a", "title": "Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition", "addresses": [{"address": "Tencent", "lat": "22.54471540", "lng": "113.93571640", "type": "company"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1810.07599.pdf"]}, {"id": "a32878e85941b5392d58d28e5248f94e16e25d78", "title": "Quality Classified Image Analysis with Application to Face Detection and Recognition", "addresses": [{"address": "Shenzhen University", "lat": "22.53521465", "lng": "113.93159110", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1801.06445.pdf"]}, {"id": "73ea06787925157df519a15ee01cc3dc1982a7e0", "title": "Fast Face Image Synthesis with Minimal Training", "addresses": [{"address": "University of Notre Dame", "lat": "41.70456775", "lng": "-86.23822026", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1811.01474.pdf"]}, {"id": "fba95853ca3135cc52a4b2bc67089041c2a9408c", "title": "Disguised Faces in the Wild", "addresses": [{"address": "Indian Institute of Technology Delhi", "lat": "28.54632595", "lng": "77.27325504", "type": "edu"}, {"address": "University of Maryland", "lat": "39.28996850", "lng": "-76.62196103", "type": "edu"}], "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/fba9/5853ca3135cc52a4b2bc67089041c2a9408c.pdf"]}, {"id": "0334a8862634988cc684dacd4279c5c0d03704da", "title": "FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition", "addresses": [{"address": "University of Maryland", "lat": "39.28996850", "lng": "-76.62196103", "type": "edu"}], "year": "2017", "pdf": ["https://arxiv.org/pdf/1609.06591.pdf"]}, {"id": "11a47a91471f40af5cf00449954474fd6e9f7694", "title": "NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification", "addresses": [{"address": "Southwest University", "lat": "29.82366295", "lng": "106.42050016", "type": "edu"}], "year": "2016", "pdf": ["http://www.mdpi.com/2078-2489/7/4/61/pdf"]}, {"id": "58d0c140597aa658345230615fb34e2c750d164c", "title": "Continuous Biometric Verification for Non-Repudiation of Remote Services", "addresses": [{"address": "University of Florence", "lat": "43.77764260", "lng": "11.25976500", "type": "edu"}], "year": "2017", "pdf": []}, {"id": "c5e37630d0672e4d44f7dee83ac2c1528be41c2e", "title": "Multi-task Deep Neural Network for Joint Face Recognition and Facial Attribute Prediction", "addresses": [{"address": "Fudan University", "lat": "31.30104395", "lng": "121.50045497", "type": "edu"}], "year": "2017", "pdf": []}, {"id": "ec05078be14a11157ac0e1c6b430ac886124589b", "title": "Longitudinal Face Aging in the Wild - Recent Deep Learning Approaches", "addresses": [{"address": "Concordia University", "lat": "45.57022705", "lng": "-122.63709346", "type": "edu"}, {"address": "Carnegie Mellon University", "lat": "37.41021930", "lng": "-122.05965487", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1802.08726.pdf"]}, {"id": "7788fa76f1488b1597ee2bebc462f628e659f61e", "title": "A Privacy-Aware Architecture at the Edge for Autonomous Real-Time Identity Reidentification in Crowds", "addresses": [{"address": "University of Texas at San Antonio", "lat": "29.58333105", "lng": "-98.61944505", "type": "edu"}], "year": "2018", "pdf": []}, {"id": "a2d1818eb461564a5153c74028e53856cf0b40fd", "title": "Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition", "addresses": [{"address": "Tencent", "lat": "22.54471540", "lng": "113.93571640", "type": "company"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1810.07599.pdf"]}, {"id": "eb027969f9310e0ae941e2adee2d42cdf07d938c", "title": "VGGFace2: A Dataset for Recognising Faces across Pose and Age", "addresses": [{"address": "University of Oxford", "lat": "51.75345380", "lng": "-1.25400997", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1710.08092.pdf"]}, {"id": "b18858ad6ec88d8b443dffd3e944e653178bc28b", "title": "2017 Trojaning Attack on Neural Networks", "addresses": [{"address": "Purdue University", "lat": "40.43197220", "lng": "-86.92389368", "type": "edu"}, {"address": "Nanjing University", "lat": "32.05659570", "lng": "118.77408833", "type": "edu"}], "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/b188/58ad6ec88d8b443dffd3e944e653178bc28b.pdf"]}, {"id": "44e6ce12b857aeade03a6e5d1b7fb81202c39489", "title": "VoxCeleb2: Deep Speaker Recognition", "addresses": [{"address": "University of Oxford", "lat": "51.75345380", "lng": "-1.25400997", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1806.05622.pdf"]}, {"id": "809ea255d144cff780300440d0f22c96e98abd53", "title": "ArcFace: Additive Angular Margin Loss for Deep Face Recognition", "addresses": [{"address": "Imperial College London", "lat": "51.49887085", "lng": "-0.17560797", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1801.07698.pdf"]}, {"id": "8d9ffe9f7bf1ff3ecc320afe50a92a867a12aeb7", "title": "Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings", "addresses": [{"address": "University of Oxford", "lat": "51.75345380", "lng": "-1.25400997", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1809.02169.pdf"]}, {"id": "7cffcb4f24343a924a8317d560202ba9ed26cd0b", "title": "The unconstrained ear recognition challenge", "addresses": [{"address": "University of Ljubljana", "lat": "46.05015580", "lng": "14.46907327", "type": "edu"}, {"address": "University of Colorado, Colorado Springs", "lat": "38.89207560", "lng": "-104.79716389", "type": "edu"}, {"address": "Islamic Azad University", "lat": "34.84529990", "lng": "48.55962120", "type": "edu"}, {"address": "Imperial College London", "lat": "51.49887085", "lng": "-0.17560797", "type": "edu"}], "year": "2017", "pdf": ["https://arxiv.org/pdf/1708.06997.pdf"]}, {"id": "c88ce5ef33d5e544224ab50162d9883ff6429aa3", "title": "Face Match for Family Reunification: Real-World Face Image Retrieval", "addresses": [{"address": "Central Washington University", "lat": "47.00646895", "lng": "-120.53673040", "type": "edu"}], "year": "2017", "pdf": ["https://pdfs.semanticscholar.org/c88c/e5ef33d5e544224ab50162d9883ff6429aa3.pdf"]}, {"id": "eb8519cec0d7a781923f68fdca0891713cb81163", "title": "Temporal Non-volume Preserving Approach to Facial Age-Progression and Age-Invariant Face Recognition", "addresses": [{"address": "Concordia University", "lat": "45.57022705", "lng": "-122.63709346", "type": "edu"}, {"address": "Carnegie Mellon University", "lat": "37.41021930", "lng": "-122.05965487", "type": "edu"}], "year": "2017", "pdf": ["https://arxiv.org/pdf/1703.08617.pdf"]}, {"id": "628a3f027b7646f398c68a680add48c7969ab1d9", "title": "Plan for Final Year Project : HKU-Face : A Large Scale Dataset for Deep Face Recognition", "addresses": [{"address": "Facebook", "lat": "37.39367170", "lng": "-122.08072620", "type": "company"}], "year": "2017", "pdf": ["https://pdfs.semanticscholar.org/628a/3f027b7646f398c68a680add48c7969ab1d9.pdf"]}, {"id": "a52a69bf304d49fba6eac6a73c5169834c77042d", "title": "Margin Loss: Making Faces More Separable", "addresses": [{"address": "Tsinghua University", "lat": "40.00229045", "lng": "116.32098908", "type": "edu"}], "year": "2018", "pdf": []}, {"id": "54bb25a213944b08298e4e2de54f2ddea890954a", "title": "AgeDB: The First Manually Collected, In-the-Wild Age Database", "addresses": [{"address": "Imperial College London", "lat": "51.49887085", "lng": "-0.17560797", "type": "edu"}, {"address": "Middlesex University", "lat": "51.59029705", "lng": "-0.22963221", "type": "edu"}], "year": "2017", "pdf": ["http://eprints.mdx.ac.uk/22044/1/agedb_kotsia.pdf", "http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf", "https://ibug.doc.ic.ac.uk/media/uploads/documents/agedb.pdf"]}, {"id": "831b4d8b0c0173b0bac0e328e844a0fbafae6639", "title": "Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition", "addresses": [{"address": "Chinese University of Hong Kong", "lat": "22.42031295", "lng": "114.20788644", "type": "edu"}, {"address": "SenseTime", "lat": "39.99300800", "lng": "116.32988200", "type": "company"}, {"address": "Nanyang Technological University", "lat": "1.34841040", "lng": "103.68297965", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1809.01407.pdf"]}, {"id": "b7b421be7c1dcbb8d41edb11180ba6ec87511976", "title": "A Deep Face Identification Network Enhanced by Facial Attributes Prediction", "addresses": [{"address": "West Virginia University", "lat": "39.65404635", "lng": "-79.96475355", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1805.00324.pdf"]}, {"id": "cbbd13c29d042743f0139f1e044b6bca731886d0", "title": "Not-So-CLEVR: learning same-different relations strains feedforward neural networks.", "addresses": [{"address": "Brown University", "lat": "41.82686820", "lng": "-71.40123146", "type": "edu"}], "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/cbbd/13c29d042743f0139f1e044b6bca731886d0.pdf"]}, {"id": "3cf1f89d73ca4b25399c237ed3e664a55cd273a2", "title": "Face Sketch Matching via Coupled Deep Transform Learning", "addresses": [{"address": "IIIT Delhi, India", "lat": "28.54562820", "lng": "77.27315050", "type": "edu"}], "year": "2017", "pdf": ["https://arxiv.org/pdf/1710.02914.pdf"]}, {"id": "9b07084c074ba3710fee59ed749c001ae70aa408", "title": "Computational Models of Face Perception.", "addresses": [{"address": "Ohio State University", "lat": "40.00471095", "lng": "-83.02859368", "type": "edu"}], "year": "2017", "pdf": ["https://pdfs.semanticscholar.org/9b07/084c074ba3710fee59ed749c001ae70aa408.pdf"]}, {"id": "6d07e176c754ac42773690d4b4919a39df85d7ec", "title": "Face Attribute Prediction Using Off-The-Shelf Deep Learning Networks", "addresses": [{"address": "KTH Royal Institute of Technology, Stockholm", "lat": "59.34986645", "lng": "18.07063213", "type": "edu"}], "year": "2016", "pdf": ["https://pdfs.semanticscholar.org/6d07/e176c754ac42773690d4b4919a39df85d7ec.pdf"]}, {"id": "47190d213caef85e8b9dd0d271dbadc29ed0a953", "title": "The Devil of Face Recognition is in the Noise", "addresses": [{"address": "University of California, San Diego", "lat": "32.87935255", "lng": "-117.23110049", "type": "edu"}, {"address": "Nanyang Technological University", "lat": "1.34841040", "lng": "103.68297965", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1807.11649.pdf"]}, {"id": "291265db88023e92bb8c8e6390438e5da148e8f5", "title": "MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition", "addresses": [{"address": "Microsoft", "lat": "47.64233180", "lng": "-122.13693020", "type": "company"}], "year": "2016", "pdf": ["https://arxiv.org/pdf/1607.08221.pdf"]}, {"id": "818ecc8c8d4dc398b01a852df90cb8d972530fa5", "title": "Unsupervised Training for 3D Morphable Model Regression", "addresses": [{"address": "Princeton University", "lat": "40.34829285", "lng": "-74.66308325", "type": "edu"}, {"address": "MIT CSAIL", "lat": "42.36194070", "lng": "-71.09043780", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1806.06098.pdf"]}, {"id": "323f9ae6bdd2a4e4dce4168f7f7e19c70585c9b5", "title": "Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systems", "addresses": [{"address": "University of Basel", "lat": "47.56126510", "lng": "7.57529610", "type": "edu"}], "year": "2017", "pdf": ["https://arxiv.org/pdf/1712.01619.pdf"]}, {"id": "d949fadc9b6c5c8b067fa42265ad30945f9caa99", "title": "Rethinking Feature Discrimination and Polymerization for Large-scale Recognition", "addresses": [{"address": "Chinese University of Hong Kong", "lat": "22.42031295", "lng": "114.20788644", "type": "edu"}], "year": "2017", "pdf": ["https://arxiv.org/pdf/1710.00870.pdf"]}, {"id": "4cdb6144d56098b819076a8572a664a2c2d27f72", "title": "Face Synthesis for Eyeglass-Robust Face Recognition", "addresses": [{"address": "Chinese Academy of Sciences", "lat": "40.00447950", "lng": "116.37023800", "type": "edu"}, {"address": "University of Chinese Academy of Sciences", "lat": "39.90828040", "lng": "116.24585270", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1806.01196.pdf"]}, {"id": "672fae3da801b2a0d2bad65afdbbbf1b2320623e", "title": "Pose-Selective Max Pooling for Measuring Similarity", "addresses": [{"address": "Johns Hopkins University", "lat": "39.32905300", "lng": "-76.61942500", "type": "edu"}], "year": "2016", "pdf": ["https://arxiv.org/pdf/1609.07042.pdf"]}, {"id": "0f9fe80fff218573a4805437ba7010fa823ca0e6", "title": "DIY Human Action Data Set Generation", "addresses": [{"address": "Simon Fraser University", "lat": "49.27674540", "lng": "-122.91777375", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1803.11264.pdf"]}, {"id": "2b869d5551b10f13bf6fcdb8d13f0aa4d1f59fc4", "title": "Ring loss: Convex Feature Normalization for Face Recognition", "addresses": [{"address": "Carnegie Mellon University", "lat": "37.41021930", "lng": "-122.05965487", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1803.00130.pdf"]}, {"id": "6193c833ad25ac27abbde1a31c1cabe56ce1515b", "title": "Trojaning Attack on Neural Networks", "addresses": [{"address": "Purdue University", "lat": "40.43197220", "lng": "-86.92389368", "type": "edu"}], "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/5f25/7ca18a92c3595db3bda3224927ec494003a5.pdf"]}, {"id": "d4f1eb008eb80595bcfdac368e23ae9754e1e745", "title": "Unconstrained Face Detection and Open-Set Face Recognition Challenge", "addresses": [{"address": "University of Colorado, Colorado Springs", "lat": "38.89207560", "lng": "-104.79716389", "type": "edu"}], "year": "2017", "pdf": ["https://arxiv.org/pdf/1708.02337.pdf"]}, {"id": "1648cf24c042122af2f429641ba9599a2187d605", "title": "Boosting cross-age face verification via generative age normalization", "addresses": [{"address": "EURECOM", "lat": "43.61438600", "lng": "7.07112500", "type": "edu"}], "year": "2017", "pdf": ["http://www.eurecom.fr/en/publication/5333/download/sec-publi-5333.pdf"]}, {"id": "228ea13041910c41b50d0052bdce924037c3bc6a", "title": "A Review Paper Between Open Source and Commercial SDK and Performance Comparisons of Face Matchers", "addresses": [{"address": "National Science and Technology Development Agency, Thailand", "lat": "14.09502500", "lng": "100.66471010", "type": "gov"}], "year": "2018", "pdf": []}, {"id": "5a259f2f5337435f841d39dada832ab24e7b3325", "title": "Face Recognition via Active Annotation and Learning", "addresses": [{"address": "Fudan University", "lat": "31.30104395", "lng": "121.50045497", "type": "edu"}, {"address": "Chinese Academy of Sciences", "lat": "40.00447950", "lng": "116.37023800", "type": "edu"}], "year": "2016", "pdf": []}, {"id": "746c0205fdf191a737df7af000eaec9409ede73f", "title": "Investigating Nuisances in DCNN-Based Face Recognition", "addresses": [{"address": "University of Florence", "lat": "43.77764260", "lng": "11.25976500", "type": "edu"}], "year": "2018", "pdf": []}, {"id": "626913b8fcbbaee8932997d6c4a78fe1ce646127", "title": "Learning from Millions of 3D Scans for Large-scale 3D Face Recognition", "addresses": [{"address": "University of Western Australia", "lat": "-31.95040445", "lng": "115.79790037", "type": "edu"}], "year": "2017", "pdf": ["https://arxiv.org/pdf/1711.05942.pdf"]}, {"id": "d80a3d1f3a438e02a6685e66ee908446766fefa9", "title": "Quantifying Facial Age by Posterior of Age Comparisons", "addresses": [{"address": "SenseTime", "lat": "39.99300800", "lng": "116.32988200", "type": "company"}, {"address": "Chinese University of Hong Kong", "lat": "22.42031295", "lng": "114.20788644", "type": "edu"}], "year": "2017", "pdf": ["https://arxiv.org/pdf/1708.09687.pdf"]}, {"id": "270e5266a1f6e76954dedbc2caf6ff61a5fbf8d0", "title": "EmotioNet Challenge: Recognition of facial expressions of emotion in the wild", "addresses": [{"address": "Ohio State University", "lat": "40.00471095", "lng": "-83.02859368", "type": "edu"}], "year": "2017", "pdf": ["https://arxiv.org/pdf/1703.01210.pdf"]}, {"id": "0081e2188c8f34fcea3e23c49fb3e17883b33551", "title": "Training Deep Face Recognition Systems with Synthetic Data", "addresses": [{"address": "University of Basel", "lat": "47.56126510", "lng": "7.57529610", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1802.05891.pdf"]}, {"id": "40bb090a4e303f11168dce33ed992f51afe02ff7", "title": "Marginal Loss for Deep Face Recognition", "addresses": [{"address": "Imperial College London", "lat": "51.49887085", "lng": "-0.17560797", "type": "edu"}], "year": "2017", "pdf": ["http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Deng_Marginal_Loss_for_CVPR_2017_paper.pdf"]}, {"id": "bd8f77b7d3b9d272f7a68defc1412f73e5ac3135", "title": "SphereFace: Deep Hypersphere Embedding for Face Recognition", "addresses": [{"address": "Georgia Institute of Technology", "lat": "33.77603300", "lng": "-84.39884086", "type": "edu"}, {"address": "Carnegie Mellon University", "lat": "37.41021930", "lng": "-122.05965487", "type": "edu"}, {"address": "Sun Yat-Sen University", "lat": "23.09461185", "lng": "113.28788994", "type": "edu"}], "year": "2017", "pdf": ["https://arxiv.org/pdf/1704.08063.pdf"]}, {"id": "c98983592777952d1751103b4d397d3ace00852d", "title": "Face Synthesis from Facial Identity Features", "addresses": [{"address": "University of Massachusetts", "lat": "42.38897850", "lng": "-72.52869870", "type": "edu"}], "year": "2017", "pdf": ["https://pdfs.semanticscholar.org/c989/83592777952d1751103b4d397d3ace00852d.pdf"]}, {"id": "19458454308a9f56b7de76bf7d8ff8eaa52b0173", "title": "Deep Features for Recognizing Disguised Faces in the Wild", "addresses": [{"address": "University of Maryland", "lat": "39.28996850", "lng": "-76.62196103", "type": "edu"}], "year": "", "pdf": ["https://pdfs.semanticscholar.org/1945/8454308a9f56b7de76bf7d8ff8eaa52b0173.pdf"]}, {"id": "def2983576001bac7d6461d78451159800938112", "title": "The Do\u2019s and Don\u2019ts for CNN-Based Face Verification", "addresses": [{"address": "University of Maryland", "lat": "39.28996850", "lng": "-76.62196103", "type": "edu"}], "year": "2017", "pdf": ["https://arxiv.org/pdf/1705.07426.pdf"]}, {"id": "df2c685aa9c234783ab51c1aa1bf1cb5d71a3dbb", "title": "SREFI: Synthesis of realistic example face images", "addresses": [{"address": "University of Notre Dame", "lat": "41.70456775", "lng": "-86.23822026", "type": "edu"}], "year": "2017", "pdf": ["https://arxiv.org/pdf/1704.06693.pdf"]}, {"id": "5812d8239d691e99d4108396f8c26ec0619767a6", "title": "GhostVLAD for set-based face recognition", "addresses": [{"address": "University of Oxford", "lat": "51.75345380", "lng": "-1.25400997", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1810.09951.pdf"]}, {"id": "368d59cf1733af511ed8abbcbeb4fb47afd4da1c", "title": "To Frontalize or Not To Frontalize: A Study of Face Pre-Processing Techniques and Their Impact on Recognition", "addresses": [{"address": "University of Notre Dame", "lat": "41.70456775", "lng": "-86.23822026", "type": "edu"}, {"address": "University of Ljubljana", "lat": "46.05015580", "lng": "14.46907327", "type": "edu"}], "year": "2016", "pdf": ["https://arxiv.org/pdf/1610.04823.pdf"]}, {"id": "3ac3a714042d3ebc159546c26321a1f8f4f5f80c", "title": "Clustering lightened deep representation for large scale face identification", "addresses": [{"address": "Beijing University of Posts and Telecommunications", "lat": "39.96014880", "lng": "116.35193921", "type": "edu"}], "year": "2017", "pdf": []}, {"id": "b55e70df03d9b80c91446a97957bc95772dcc45b", "title": "MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis", "addresses": [{"address": "Brno University of Technology", "lat": "49.20172000", "lng": "16.60331680", "type": "edu"}, {"address": "University of Passau", "lat": "48.56704660", "lng": "13.45178350", "type": "edu"}, {"address": "Deutsche Welle, Bonn, Germany", "lat": "50.71714970", "lng": "7.12825184", "type": "edu"}, {"address": "Expert Systems, Modena, Italy", "lat": "44.65316920", "lng": "10.85862280", "type": "company"}, {"address": "National University of Ireland Galway", "lat": "53.27639715", "lng": "-9.05829961", "type": "edu"}, {"address": "Paradigma Digital, Madrid, Spain", "lat": "40.44029950", "lng": "-3.78700760", "type": "company"}, {"address": "Siren Solutions, Dublin, Ireland", "lat": "53.34980530", "lng": "-6.26030970", "type": "company"}], "year": "2018", "pdf": []}, {"id": "e8523c4ac9d7aa21f3eb4062e09f2a3bc1eedcf7", "title": "Toward End-to-End Face Recognition Through Alignment Learning", "addresses": [{"address": "Tsinghua University", "lat": "40.00229045", "lng": "116.32098908", "type": "edu"}], "year": "2017", "pdf": ["https://arxiv.org/pdf/1701.07174.pdf"]}, {"id": "3dfd94d3fad7e17f52a8ae815eb9cc5471172bc0", "title": "Face2Text: Collecting an Annotated Image Description Corpus for the Generation of Rich Face Descriptions", "addresses": [{"address": "University of Malta", "lat": "35.90232260", "lng": "14.48341890", "type": "edu"}, {"address": "University of Copenhagen", "lat": "55.68015020", "lng": "12.57232700", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1803.03827.pdf"]}, {"id": "d7cbedbee06293e78661335c7dd9059c70143a28", "title": "MobileFaceNets: Efficient CNNs for Accurate Real-time Face Verification on Mobile Devices", "addresses": [{"address": "Beijing Jiaotong University", "lat": "39.94976005", "lng": "116.33629046", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1804.07573.pdf"]}, {"id": "93af36da08bf99e68c9b0d36e141ed8154455ac2", "title": "A Dditive M Argin S Oftmax for F Ace V Erification", "addresses": [{"address": "University of Electronic Science and Technology of China", "lat": "40.01419050", "lng": "-83.03091430", "type": "edu"}, {"address": "Georgia Institute of Technology", "lat": "33.77603300", "lng": "-84.39884086", "type": "edu"}], "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/93af/36da08bf99e68c9b0d36e141ed8154455ac2.pdf"]}, {"id": "582edc19f2b1ab2ac6883426f147196c8306685a", "title": "Do We Really Need to Collect Millions of Faces for Effective Face Recognition?", "addresses": [{"address": "Open University of Israel", "lat": "32.77824165", "lng": "34.99565673", "type": "edu"}], "year": "2016", "pdf": ["https://arxiv.org/pdf/1603.07057.pdf"]}, {"id": "2359c3f763e96e0ee62b1119c897a32ce9715a77", "title": "Neural Computing on a Raspberry Pi : Applications to Zebrafish Behavior Monitoring", "addresses": [{"address": "Brown University", "lat": "41.82686820", "lng": "-71.40123146", "type": "edu"}], "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/2359/c3f763e96e0ee62b1119c897a32ce9715a77.pdf"]}]}
\ No newline at end of file +{"id": "28d4e027c7e90b51b7d8908fce68128d1964668a", "dataset": {"key": "megaface", "name_short": "MegaFace", "using": "Y", "ft_share": "1", "subset_of": "", "superset_of": "", "name_full": "MegaFace Dataset", "url": "http://megaface.cs.washington.edu/", "added_on": "", "faces": "", "pdf_paper": "Y", "comments": "", "": "", "relevance": "10"}, "statistics": {"key": "megaface", "name": "MegaFace", "berit": "Y", "charlie": "", "adam": "", "priority": "N", "wild": "Y", "indoor": "", "outdoor": "", "cyberspace": "Y", "names": "", "downloaded": "Y", "year_start": "", "year_end": "", "year_published": "2016", "ongoing": "", "images": "4,753,520 ", "videos": "", "faces_unique": "672,057 ", "total_faces": "", "img_per_person": "", "num_cameras": "", "faces_persons": "", "female": "", "male": "", "landmarks": "", "width": "", "height": "", "color": "", "gray": "", "derivative_of": "", "tags": "fr", "source": "flickr", "purpose_short": "million scale face recognition", "size_gb": "", "agreement": "", "agree_requied": "", "agreement_signed": "", "comment": "", "comment 2": "", "comment 3": "", "": ""}, "paper": {"paper_id": "28d4e027c7e90b51b7d8908fce68128d1964668a", "key": "megaface", "title": "Level Playing Field for Million Scale Face Recognition", "year": "2017", "pdf": ["https://arxiv.org/pdf/1705.00393.pdf"], "address": "", "name": "MegaFace", "doi": []}, "address": null, "additional_papers": [{"paper_id": "96e0cfcd81cdeb8282e29ef9ec9962b125f379b0", "key": "megaface", "title": "The MegaFace Benchmark: 1 Million Faces for Recognition at Scale", "year": "2016", "pdf": ["https://arxiv.org/pdf/1512.00596.pdf"], "address": "", "name": "MegaFace", "doi": []}], "citations": [{"id": "1345fb7700389f9d02f203b3cb25ac3594855054", "title": "Hierarchical Training for Large Scale Face Recognition with Few Samples Per Subject", "addresses": [{"address": "Chinese Academy of Sciences", "lat": "40.00447950", "lng": "116.37023800", "type": "edu"}], "year": "2018", "pdf": []}, {"id": "b59cee1f647737ec3296ccb3daa25c890359c307", "title": "Continuously Reproducing Toolchains in Pattern Recognition and Machine Learning Experiments", "addresses": [{"address": "IDIAP Research Institute", "lat": "46.10923700", "lng": "7.08453549", "type": "edu"}], "year": "2017", "pdf": ["https://pdfs.semanticscholar.org/b59c/ee1f647737ec3296ccb3daa25c890359c307.pdf"]}, {"id": "ebb3d5c70bedf2287f9b26ac0031004f8f617b97", "title": "Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans", "addresses": [{"address": "Electrical and Computer Engineering", "lat": "33.58667840", "lng": "-101.87539204", "type": "edu"}, {"address": "University of Maryland", "lat": "39.28996850", "lng": "-76.62196103", "type": "edu"}], "year": "2018", "pdf": []}, {"id": "f3a59d85b7458394e3c043d8277aa1ffe3cdac91", "title": "Query-Free Attacks on Industry-Grade Face Recognition Systems under Resource Constraints", "addresses": [{"address": "Indiana University", "lat": "39.86948105", "lng": "-84.87956905", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1802.09900.pdf"]}, {"id": "cd6aaa37fffd0b5c2320f386be322b8adaa1cc68", "title": "Deep Face Recognition: A Survey", "addresses": [{"address": "Beijing University of Posts and Telecommunications", "lat": "39.96014880", "lng": "116.35193921", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1804.06655.pdf"]}, {"id": "b4f3e9fc0a2b40595ae0a625d1d768a57a7c2eba", "title": "Recognizing Disguised Faces in the Wild", "addresses": [{"address": "Member", "lat": "37.05826350", "lng": "-95.67914910", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1811.08837.pdf"]}, {"id": "7323b594d3a8508f809e276aa2d224c4e7ec5a80", "title": "An Experimental Evaluation of Covariates Effects on Unconstrained Face Verification", "addresses": [{"address": "Member", "lat": "37.05826350", "lng": "-95.67914910", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1808.05508.pdf"]}, {"id": "4d46fd59364ed5ec8f50abe68cd7886379bfd80a", "title": "Learning from Millions of 3D Scans for Large-Scale 3D Face Recognition", "addresses": [{"address": "University of Western Australia", "lat": "-31.95040445", "lng": "115.79790037", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1711.05942.pdf"]}, {"id": "9f65319b8a33c8ec11da2f034731d928bf92e29d", "title": "TAKING ROLL : A PIPELINE FOR FACE RECOGNITION", "addresses": [{"address": "Louisiana State University", "lat": "30.40550035", "lng": "-91.18620474", "type": "edu"}], "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/9f65/319b8a33c8ec11da2f034731d928bf92e29d.pdf"]}, {"id": "9e31e77f9543ab42474ba4e9330676e18c242e72", "title": "The Devil of Face Recognition is in the Noise", "addresses": [{"address": "University of California, San Diego", "lat": "32.87935255", "lng": "-117.23110049", "type": "edu"}, {"address": "Nanyang Technological University", "lat": "1.34841040", "lng": "103.68297965", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1807.11649.pdf"]}, {"id": "1ffe20eb32dbc4fa85ac7844178937bba97f4bf0", "title": "Face Clustering: Representation and Pairwise Constraints", "addresses": [{"address": "Member", "lat": "37.05826350", "lng": "-95.67914910", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1706.05067.pdf"]}, {"id": "841855205818d3a6d6f85ec17a22515f4f062882", "title": "Low Resolution Face Recognition in the Wild", "addresses": [{"address": "University of Notre Dame", "lat": "41.70456775", "lng": "-86.23822026", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1805.11529.pdf"]}, {"id": "8efda5708bbcf658d4f567e3866e3549fe045bbb", "title": "Pre-trained Deep Convolutional Neural Networks for Face Recognition", "addresses": [{"address": "University of Groningen", "lat": "53.21967825", "lng": "6.56251482", "type": "edu"}], "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/8efd/a5708bbcf658d4f567e3866e3549fe045bbb.pdf"]}, {"id": "9865fe20df8fe11717d92b5ea63469f59cf1635a", "title": "Wildest Faces: Face Detection and Recognition in Violent Settings", "addresses": [{"address": "Hacettepe University", "lat": "39.86742125", "lng": "32.73519072", "type": "edu"}, {"address": "Middle East Technical University", "lat": "39.87549675", "lng": "32.78553506", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1805.07566.pdf"]}, {"id": "e64c166dc5bb33bc61462a8b5ac92edb24d905a1", "title": "Fast Face Image Synthesis with Minimal Training.", "addresses": [{"address": "University of Notre Dame", "lat": "41.70456775", "lng": "-86.23822026", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1811.01474.pdf"]}, {"id": "3c5ba48d25fbe24691ed060fa8f2099cc9eba14f", "title": "Racial Faces in-the-Wild: Reducing Racial Bias by Deep Unsupervised Domain Adaptation", "addresses": [{"address": "Beijing University of Posts and Telecommunications", "lat": "39.96014880", "lng": "116.35193921", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1812.00194.pdf"]}, {"id": "7a7fddb3020e0c2dd4e3fe275329eb10f1cfbb8a", "title": "Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition", "addresses": [{"address": "Tencent", "lat": "22.54471540", "lng": "113.93571640", "type": "company"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1810.07599.pdf"]}, {"id": "a32878e85941b5392d58d28e5248f94e16e25d78", "title": "Quality Classified Image Analysis with Application to Face Detection and Recognition", "addresses": [{"address": "Shenzhen University", "lat": "22.53521465", "lng": "113.93159110", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1801.06445.pdf"]}, {"id": "73ea06787925157df519a15ee01cc3dc1982a7e0", "title": "Fast Face Image Synthesis with Minimal Training", "addresses": [{"address": "University of Notre Dame", "lat": "41.70456775", "lng": "-86.23822026", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1811.01474.pdf"]}, {"id": "fba95853ca3135cc52a4b2bc67089041c2a9408c", "title": "Disguised Faces in the Wild", "addresses": [{"address": "Indian Institute of Technology Delhi", "lat": "28.54632595", "lng": "77.27325504", "type": "edu"}, {"address": "University of Maryland", "lat": "39.28996850", "lng": "-76.62196103", "type": "edu"}], "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/fba9/5853ca3135cc52a4b2bc67089041c2a9408c.pdf"]}, {"id": "0334a8862634988cc684dacd4279c5c0d03704da", "title": "FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition", "addresses": [{"address": "University of Maryland", "lat": "39.28996850", "lng": "-76.62196103", "type": "edu"}], "year": "2017", "pdf": ["https://arxiv.org/pdf/1609.06591.pdf"]}, {"id": "11a47a91471f40af5cf00449954474fd6e9f7694", "title": "NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification", "addresses": [{"address": "Southwest University", "lat": "29.82366295", "lng": "106.42050016", "type": "edu"}], "year": "2016", "pdf": ["http://www.mdpi.com/2078-2489/7/4/61/pdf"]}, {"id": "58d0c140597aa658345230615fb34e2c750d164c", "title": "Continuous Biometric Verification for Non-Repudiation of Remote Services", "addresses": [{"address": "University of Florence", "lat": "43.77764260", "lng": "11.25976500", "type": "edu"}], "year": "2017", "pdf": []}, {"id": "c5e37630d0672e4d44f7dee83ac2c1528be41c2e", "title": "Multi-task Deep Neural Network for Joint Face Recognition and Facial Attribute Prediction", "addresses": [{"address": "Fudan University", "lat": "31.30104395", "lng": "121.50045497", "type": "edu"}], "year": "2017", "pdf": []}, {"id": "ec05078be14a11157ac0e1c6b430ac886124589b", "title": "Longitudinal Face Aging in the Wild - Recent Deep Learning Approaches", "addresses": [{"address": "Concordia University", "lat": "45.57022705", "lng": "-122.63709346", "type": "edu"}, {"address": "Carnegie Mellon University", "lat": "37.41021930", "lng": "-122.05965487", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1802.08726.pdf"]}, {"id": "7788fa76f1488b1597ee2bebc462f628e659f61e", "title": "A Privacy-Aware Architecture at the Edge for Autonomous Real-Time Identity Reidentification in Crowds", "addresses": [{"address": "University of Texas at San Antonio", "lat": "29.58333105", "lng": "-98.61944505", "type": "edu"}], "year": "2018", "pdf": []}, {"id": "a2d1818eb461564a5153c74028e53856cf0b40fd", "title": "Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition", "addresses": [{"address": "Tencent", "lat": "22.54471540", "lng": "113.93571640", "type": "company"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1810.07599.pdf"]}, {"id": "eb027969f9310e0ae941e2adee2d42cdf07d938c", "title": "VGGFace2: A Dataset for Recognising Faces across Pose and Age", "addresses": [{"address": "University of Oxford", "lat": "51.75345380", "lng": "-1.25400997", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1710.08092.pdf"]}, {"id": "b18858ad6ec88d8b443dffd3e944e653178bc28b", "title": "2017 Trojaning Attack on Neural Networks", "addresses": [{"address": "Purdue University", "lat": "40.43197220", "lng": "-86.92389368", "type": "edu"}, {"address": "Nanjing University", "lat": "32.05659570", "lng": "118.77408833", "type": "edu"}], "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/b188/58ad6ec88d8b443dffd3e944e653178bc28b.pdf"]}, {"id": "44e6ce12b857aeade03a6e5d1b7fb81202c39489", "title": "VoxCeleb2: Deep Speaker Recognition", "addresses": [{"address": "University of Oxford", "lat": "51.75345380", "lng": "-1.25400997", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1806.05622.pdf"]}, {"id": "809ea255d144cff780300440d0f22c96e98abd53", "title": "ArcFace: Additive Angular Margin Loss for Deep Face Recognition", "addresses": [{"address": "Imperial College London", "lat": "51.49887085", "lng": "-0.17560797", "type": "edu"}, {"address": "China", "lat": "35.86166000", "lng": "104.19539700", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1801.07698.pdf"]}, {"id": "8d9ffe9f7bf1ff3ecc320afe50a92a867a12aeb7", "title": "Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings", "addresses": [{"address": "University of Oxford", "lat": "51.75345380", "lng": "-1.25400997", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1809.02169.pdf"]}, {"id": "7cffcb4f24343a924a8317d560202ba9ed26cd0b", "title": "The unconstrained ear recognition challenge", "addresses": [{"address": "University of Ljubljana", "lat": "46.05015580", "lng": "14.46907327", "type": "edu"}, {"address": "India", "lat": "20.59368400", "lng": "78.96288000", "type": "edu"}, {"address": "University of Colorado, Colorado Springs", "lat": "38.89207560", "lng": "-104.79716389", "type": "edu"}, {"address": "Islamic Azad University", "lat": "34.84529990", "lng": "48.55962120", "type": "edu"}, {"address": "Imperial College London", "lat": "51.49887085", "lng": "-0.17560797", "type": "edu"}], "year": "2017", "pdf": ["https://arxiv.org/pdf/1708.06997.pdf"]}, {"id": "c88ce5ef33d5e544224ab50162d9883ff6429aa3", "title": "Face Match for Family Reunification: Real-World Face Image Retrieval", "addresses": [{"address": "Central Washington University", "lat": "47.00646895", "lng": "-120.53673040", "type": "edu"}], "year": "2017", "pdf": ["https://pdfs.semanticscholar.org/c88c/e5ef33d5e544224ab50162d9883ff6429aa3.pdf"]}, {"id": "eb8519cec0d7a781923f68fdca0891713cb81163", "title": "Temporal Non-volume Preserving Approach to Facial Age-Progression and Age-Invariant Face Recognition", "addresses": [{"address": "Concordia University", "lat": "45.57022705", "lng": "-122.63709346", "type": "edu"}, {"address": "Carnegie Mellon University", "lat": "37.41021930", "lng": "-122.05965487", "type": "edu"}], "year": "2017", "pdf": ["https://arxiv.org/pdf/1703.08617.pdf"]}, {"id": "628a3f027b7646f398c68a680add48c7969ab1d9", "title": "Plan for Final Year Project : HKU-Face : A Large Scale Dataset for Deep Face Recognition", "addresses": [{"address": "Facebook", "lat": "37.39367170", "lng": "-122.08072620", "type": "company"}, {"address": "k", "lat": "37.09024000", "lng": "-95.71289100", "type": "edu"}], "year": "2017", "pdf": ["https://pdfs.semanticscholar.org/628a/3f027b7646f398c68a680add48c7969ab1d9.pdf"]}, {"id": "54bb25a213944b08298e4e2de54f2ddea890954a", "title": "AgeDB: The First Manually Collected, In-the-Wild Age Database", "addresses": [{"address": "Imperial College London", "lat": "51.49887085", "lng": "-0.17560797", "type": "edu"}, {"address": "Middlesex University", "lat": "51.59029705", "lng": "-0.22963221", "type": "edu"}], "year": "2017", "pdf": ["http://eprints.mdx.ac.uk/22044/1/agedb_kotsia.pdf", "http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Moschoglou_AgeDB_The_First_CVPR_2017_paper.pdf", "https://ibug.doc.ic.ac.uk/media/uploads/documents/agedb.pdf"]}, {"id": "831b4d8b0c0173b0bac0e328e844a0fbafae6639", "title": "Consensus-Driven Propagation in Massive Unlabeled Data for Face Recognition", "addresses": [{"address": "Chinese University of Hong Kong", "lat": "22.42031295", "lng": "114.20788644", "type": "edu"}, {"address": "SenseTime", "lat": "39.99300800", "lng": "116.32988200", "type": "company"}, {"address": "Nanyang Technological University", "lat": "1.34841040", "lng": "103.68297965", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1809.01407.pdf"]}, {"id": "b7b421be7c1dcbb8d41edb11180ba6ec87511976", "title": "A Deep Face Identification Network Enhanced by Facial Attributes Prediction", "addresses": [{"address": "West Virginia University", "lat": "39.65404635", "lng": "-79.96475355", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1805.00324.pdf"]}, {"id": "cbbd13c29d042743f0139f1e044b6bca731886d0", "title": "Not-So-CLEVR: learning same-different relations strains feedforward neural networks.", "addresses": [{"address": "Brown University", "lat": "41.82686820", "lng": "-71.40123146", "type": "edu"}], "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/cbbd/13c29d042743f0139f1e044b6bca731886d0.pdf"]}, {"id": "3cf1f89d73ca4b25399c237ed3e664a55cd273a2", "title": "Face Sketch Matching via Coupled Deep Transform Learning", "addresses": [{"address": "IIIT Delhi, India", "lat": "28.54562820", "lng": "77.27315050", "type": "edu"}], "year": "2017", "pdf": ["https://arxiv.org/pdf/1710.02914.pdf"]}, {"id": "9b07084c074ba3710fee59ed749c001ae70aa408", "title": "Computational Models of Face Perception.", "addresses": [{"address": "Ohio State University", "lat": "40.00471095", "lng": "-83.02859368", "type": "edu"}], "year": "2017", "pdf": ["https://pdfs.semanticscholar.org/9b07/084c074ba3710fee59ed749c001ae70aa408.pdf"]}, {"id": "6d07e176c754ac42773690d4b4919a39df85d7ec", "title": "Face Attribute Prediction Using Off-The-Shelf Deep Learning Networks", "addresses": [{"address": "KTH Royal Institute of Technology, Stockholm", "lat": "59.34986645", "lng": "18.07063213", "type": "edu"}], "year": "2016", "pdf": ["https://pdfs.semanticscholar.org/6d07/e176c754ac42773690d4b4919a39df85d7ec.pdf"]}, {"id": "47190d213caef85e8b9dd0d271dbadc29ed0a953", "title": "The Devil of Face Recognition is in the Noise", "addresses": [{"address": "University of California, San Diego", "lat": "32.87935255", "lng": "-117.23110049", "type": "edu"}, {"address": "Nanyang Technological University", "lat": "1.34841040", "lng": "103.68297965", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1807.11649.pdf"]}, {"id": "291265db88023e92bb8c8e6390438e5da148e8f5", "title": "MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition", "addresses": [{"address": "Microsoft", "lat": "36.06303740", "lng": "-95.88099660", "type": "edu"}], "year": "2016", "pdf": ["https://arxiv.org/pdf/1607.08221.pdf"]}, {"id": "818ecc8c8d4dc398b01a852df90cb8d972530fa5", "title": "Unsupervised Training for 3D Morphable Model Regression", "addresses": [{"address": "Princeton University", "lat": "40.34829285", "lng": "-74.66308325", "type": "edu"}, {"address": "MIT CSAIL", "lat": "42.36194070", "lng": "-71.09043780", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1806.06098.pdf"]}, {"id": "323f9ae6bdd2a4e4dce4168f7f7e19c70585c9b5", "title": "Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systems", "addresses": [{"address": "University of Basel", "lat": "47.56126510", "lng": "7.57529610", "type": "edu"}], "year": "2017", "pdf": ["https://arxiv.org/pdf/1712.01619.pdf"]}, {"id": "d949fadc9b6c5c8b067fa42265ad30945f9caa99", "title": "Rethinking Feature Discrimination and Polymerization for Large-scale Recognition", "addresses": [{"address": "Chinese University of Hong Kong", "lat": "22.42031295", "lng": "114.20788644", "type": "edu"}], "year": "2017", "pdf": ["https://arxiv.org/pdf/1710.00870.pdf"]}, {"id": "4cdb6144d56098b819076a8572a664a2c2d27f72", "title": "Face Synthesis for Eyeglass-Robust Face Recognition", "addresses": [{"address": "Chinese Academy of Sciences", "lat": "40.00447950", "lng": "116.37023800", "type": "edu"}, {"address": "University of Chinese Academy of Sciences", "lat": "39.90828040", "lng": "116.24585270", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1806.01196.pdf"]}, {"id": "672fae3da801b2a0d2bad65afdbbbf1b2320623e", "title": "Pose-Selective Max Pooling for Measuring Similarity", "addresses": [{"address": "Johns Hopkins University", "lat": "39.32905300", "lng": "-76.61942500", "type": "edu"}], "year": "2016", "pdf": ["https://arxiv.org/pdf/1609.07042.pdf"]}, {"id": "0f9fe80fff218573a4805437ba7010fa823ca0e6", "title": "DIY Human Action Data Set Generation", "addresses": [{"address": "Simon Fraser University", "lat": "49.27674540", "lng": "-122.91777375", "type": "edu"}, {"address": "Microsoft", "lat": "36.06303740", "lng": "-95.88099660", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1803.11264.pdf"]}, {"id": "459e840ec58ef5ffcee60f49a94424eb503e8982", "title": "One-shot Face Recognition by Promoting Underrepresented Classes", "addresses": [{"address": "Microsoft", "lat": "36.06303740", "lng": "-95.88099660", "type": "edu"}], "year": "2017", "pdf": ["https://arxiv.org/pdf/1707.05574.pdf"]}, {"id": "2b869d5551b10f13bf6fcdb8d13f0aa4d1f59fc4", "title": "Ring loss: Convex Feature Normalization for Face Recognition", "addresses": [{"address": "Carnegie Mellon University", "lat": "37.41021930", "lng": "-122.05965487", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1803.00130.pdf"]}, {"id": "6193c833ad25ac27abbde1a31c1cabe56ce1515b", "title": "Trojaning Attack on Neural Networks", "addresses": [{"address": "Purdue University", "lat": "40.43197220", "lng": "-86.92389368", "type": "edu"}], "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/5f25/7ca18a92c3595db3bda3224927ec494003a5.pdf"]}, {"id": "d4f1eb008eb80595bcfdac368e23ae9754e1e745", "title": "Unconstrained Face Detection and Open-Set Face Recognition Challenge", "addresses": [{"address": "University of Colorado, Colorado Springs", "lat": "38.89207560", "lng": "-104.79716389", "type": "edu"}], "year": "2017", "pdf": ["https://arxiv.org/pdf/1708.02337.pdf"]}, {"id": "1648cf24c042122af2f429641ba9599a2187d605", "title": "Boosting cross-age face verification via generative age normalization", "addresses": [{"address": "EURECOM", "lat": "43.61438600", "lng": "7.07112500", "type": "edu"}], "year": "2017", "pdf": ["http://www.eurecom.fr/en/publication/5333/download/sec-publi-5333.pdf"]}, {"id": "228ea13041910c41b50d0052bdce924037c3bc6a", "title": "A Review Paper Between Open Source and Commercial SDK and Performance Comparisons of Face Matchers", "addresses": [{"address": "National Science and Technology Development Agency, Thailand", "lat": "14.09502500", "lng": "100.66471010", "type": "gov"}], "year": "2018", "pdf": []}, {"id": "5a259f2f5337435f841d39dada832ab24e7b3325", "title": "Face Recognition via Active Annotation and Learning", "addresses": [{"address": "Fudan University", "lat": "31.30104395", "lng": "121.50045497", "type": "edu"}, {"address": "Chinese Academy of Sciences", "lat": "40.00447950", "lng": "116.37023800", "type": "edu"}], "year": "2016", "pdf": []}, {"id": "746c0205fdf191a737df7af000eaec9409ede73f", "title": "Investigating Nuisances in DCNN-Based Face Recognition", "addresses": [{"address": "University of Florence", "lat": "43.77764260", "lng": "11.25976500", "type": "edu"}], "year": "2018", "pdf": []}, {"id": "626913b8fcbbaee8932997d6c4a78fe1ce646127", "title": "Learning from Millions of 3D Scans for Large-scale 3D Face Recognition", "addresses": [{"address": "University of Western Australia", "lat": "-31.95040445", "lng": "115.79790037", "type": "edu"}], "year": "2017", "pdf": ["https://arxiv.org/pdf/1711.05942.pdf"]}, {"id": "d80a3d1f3a438e02a6685e66ee908446766fefa9", "title": "Quantifying Facial Age by Posterior of Age Comparisons", "addresses": [{"address": "SenseTime", "lat": "39.99300800", "lng": "116.32988200", "type": "company"}, {"address": "Chinese University of Hong Kong", "lat": "22.42031295", "lng": "114.20788644", "type": "edu"}], "year": "2017", "pdf": ["https://arxiv.org/pdf/1708.09687.pdf"]}, {"id": "270e5266a1f6e76954dedbc2caf6ff61a5fbf8d0", "title": "EmotioNet Challenge: Recognition of facial expressions of emotion in the wild", "addresses": [{"address": "Ohio State University", "lat": "40.00471095", "lng": "-83.02859368", "type": "edu"}], "year": "2017", "pdf": ["https://arxiv.org/pdf/1703.01210.pdf"]}, {"id": "0081e2188c8f34fcea3e23c49fb3e17883b33551", "title": "Training Deep Face Recognition Systems with Synthetic Data", "addresses": [{"address": "University of Basel", "lat": "47.56126510", "lng": "7.57529610", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1802.05891.pdf"]}, {"id": "4b48e912a17c79ac95d6a60afed8238c9ab9e553", "title": "Minimum Margin Loss for Deep Face Recognition", "addresses": [{"address": "Member", "lat": "37.05826350", "lng": "-95.67914910", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1805.06741.pdf"]}, {"id": "40bb090a4e303f11168dce33ed992f51afe02ff7", "title": "Marginal Loss for Deep Face Recognition", "addresses": [{"address": "Imperial College London", "lat": "51.49887085", "lng": "-0.17560797", "type": "edu"}], "year": "2017", "pdf": ["http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Deng_Marginal_Loss_for_CVPR_2017_paper.pdf"]}, {"id": "bd8f77b7d3b9d272f7a68defc1412f73e5ac3135", "title": "SphereFace: Deep Hypersphere Embedding for Face Recognition", "addresses": [{"address": "Georgia Institute of Technology", "lat": "33.77603300", "lng": "-84.39884086", "type": "edu"}, {"address": "Carnegie Mellon University", "lat": "37.41021930", "lng": "-122.05965487", "type": "edu"}, {"address": "Sun Yat-Sen University", "lat": "23.09461185", "lng": "113.28788994", "type": "edu"}], "year": "2017", "pdf": ["https://arxiv.org/pdf/1704.08063.pdf"]}, {"id": "c98983592777952d1751103b4d397d3ace00852d", "title": "Face Synthesis from Facial Identity Features", "addresses": [{"address": "University of Massachusetts", "lat": "42.38897850", "lng": "-72.52869870", "type": "edu"}], "year": "2017", "pdf": ["https://pdfs.semanticscholar.org/c989/83592777952d1751103b4d397d3ace00852d.pdf"]}, {"id": "19458454308a9f56b7de76bf7d8ff8eaa52b0173", "title": "Deep Features for Recognizing Disguised Faces in the Wild", "addresses": [{"address": "University of Maryland", "lat": "39.28996850", "lng": "-76.62196103", "type": "edu"}], "year": "", "pdf": ["https://pdfs.semanticscholar.org/1945/8454308a9f56b7de76bf7d8ff8eaa52b0173.pdf"]}, {"id": "def2983576001bac7d6461d78451159800938112", "title": "The Do\u2019s and Don\u2019ts for CNN-Based Face Verification", "addresses": [{"address": "University of Maryland", "lat": "39.28996850", "lng": "-76.62196103", "type": "edu"}], "year": "2017", "pdf": ["https://arxiv.org/pdf/1705.07426.pdf"]}, {"id": "df2c685aa9c234783ab51c1aa1bf1cb5d71a3dbb", "title": "SREFI: Synthesis of realistic example face images", "addresses": [{"address": "University of Notre Dame", "lat": "41.70456775", "lng": "-86.23822026", "type": "edu"}], "year": "2017", "pdf": ["https://arxiv.org/pdf/1704.06693.pdf"]}, {"id": "5812d8239d691e99d4108396f8c26ec0619767a6", "title": "GhostVLAD for set-based face recognition", "addresses": [{"address": "University of Oxford", "lat": "51.75345380", "lng": "-1.25400997", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1810.09951.pdf"]}, {"id": "368d59cf1733af511ed8abbcbeb4fb47afd4da1c", "title": "To Frontalize or Not To Frontalize: A Study of Face Pre-Processing Techniques and Their Impact on Recognition", "addresses": [{"address": "University of Notre Dame", "lat": "41.70456775", "lng": "-86.23822026", "type": "edu"}, {"address": "University of Ljubljana", "lat": "46.05015580", "lng": "14.46907327", "type": "edu"}], "year": "2016", "pdf": ["https://arxiv.org/pdf/1610.04823.pdf"]}, {"id": "3ac3a714042d3ebc159546c26321a1f8f4f5f80c", "title": "Clustering lightened deep representation for large scale face identification", "addresses": [{"address": "Beijing University of Posts and Telecommunications", "lat": "39.96014880", "lng": "116.35193921", "type": "edu"}], "year": "2017", "pdf": []}, {"id": "03f7041515d8a6dcb9170763d4f6debd50202c2b", "title": "Clustering Millions of Faces by Identity", "addresses": [{"address": "Member", "lat": "37.05826350", "lng": "-95.67914910", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1604.00989.pdf"]}, {"id": "b55e70df03d9b80c91446a97957bc95772dcc45b", "title": "MixedEmotions: An Open-Source Toolbox for Multimodal Emotion Analysis", "addresses": [{"address": "Brno University of Technology", "lat": "49.20172000", "lng": "16.60331680", "type": "edu"}, {"address": "University of Passau", "lat": "48.56704660", "lng": "13.45178350", "type": "edu"}, {"address": "Deutsche Welle, Bonn, Germany", "lat": "50.71714970", "lng": "7.12825184", "type": "edu"}, {"address": "Expert Systems, Modena, Italy", "lat": "44.65316920", "lng": "10.85862280", "type": "company"}, {"address": "GSI Universidad Polit\u00e9cnica de Madrid, Madrid, Spain", "lat": "40.44863720", "lng": "-3.71927980", "type": "edu"}, {"address": "National University of Ireland Galway", "lat": "53.27639715", "lng": "-9.05829961", "type": "edu"}, {"address": "Paradigma Digital, Madrid, Spain", "lat": "40.44029950", "lng": "-3.78700760", "type": "company"}, {"address": "Siren Solutions, Dublin, Ireland", "lat": "53.34980530", "lng": "-6.26030970", "type": "company"}], "year": "2018", "pdf": []}, {"id": "e8523c4ac9d7aa21f3eb4062e09f2a3bc1eedcf7", "title": "Toward End-to-End Face Recognition Through Alignment Learning", "addresses": [{"address": "Tsinghua University", "lat": "40.00229045", "lng": "116.32098908", "type": "edu"}, {"address": "China", "lat": "35.86166000", "lng": "104.19539700", "type": "edu"}], "year": "2017", "pdf": ["https://arxiv.org/pdf/1701.07174.pdf"]}, {"id": "3dfd94d3fad7e17f52a8ae815eb9cc5471172bc0", "title": "Face2Text: Collecting an Annotated Image Description Corpus for the Generation of Rich Face Descriptions", "addresses": [{"address": "University of Malta", "lat": "35.90232260", "lng": "14.48341890", "type": "edu"}, {"address": "University of Copenhagen", "lat": "55.68015020", "lng": "12.57232700", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1803.03827.pdf"]}, {"id": "d7cbedbee06293e78661335c7dd9059c70143a28", "title": "MobileFaceNets: Efficient CNNs for Accurate Real-time Face Verification on Mobile Devices", "addresses": [{"address": "Beijing Jiaotong University", "lat": "39.94976005", "lng": "116.33629046", "type": "edu"}, {"address": "China", "lat": "35.86166000", "lng": "104.19539700", "type": "edu"}], "year": "2018", "pdf": ["https://arxiv.org/pdf/1804.07573.pdf"]}, {"id": "93af36da08bf99e68c9b0d36e141ed8154455ac2", "title": "A Dditive M Argin S Oftmax for F Ace V Erification", "addresses": [{"address": "University of Electronic Science and Technology of China", "lat": "40.01419050", "lng": "-83.03091430", "type": "edu"}, {"address": "Georgia Institute of Technology", "lat": "33.77603300", "lng": "-84.39884086", "type": "edu"}], "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/93af/36da08bf99e68c9b0d36e141ed8154455ac2.pdf"]}, {"id": "582edc19f2b1ab2ac6883426f147196c8306685a", "title": "Do We Really Need to Collect Millions of Faces for Effective Face Recognition?", "addresses": [{"address": "Open University of Israel", "lat": "32.77824165", "lng": "34.99565673", "type": "edu"}], "year": "2016", "pdf": ["https://arxiv.org/pdf/1603.07057.pdf"]}, {"id": "2359c3f763e96e0ee62b1119c897a32ce9715a77", "title": "Neural Computing on a Raspberry Pi : Applications to Zebrafish Behavior Monitoring", "addresses": [{"address": "Brown University", "lat": "41.82686820", "lng": "-71.40123146", "type": "edu"}], "year": "2018", "pdf": ["https://pdfs.semanticscholar.org/2359/c3f763e96e0ee62b1119c897a32ce9715a77.pdf"]}]}
\ No newline at end of file |
