update

3 files changed, 13 insertions, 13 deletions

diff --git a/scraper/reports/doi_institutions_geocoded.csv b/scraper/reports/doi_institutions_geocoded.csv
index 40302ee1..a3ea6a5e 100644
--- a/scraper/reports/doi_institutions_geocoded.csv
+++ b/scraper/reports/doi_institutions_geocoded.csv
@@ -66,7 +66,7 @@ b6d5b71f78226c8696d7502e4514feb575088979,Selecting and evaluating data for train
 b6049a15e204327e44b6d1ee3a1148bd7e21c635,Single view image based — 3D human pose reconstruction,Bournemouth University,Bournemouth University,"Bournemouth University, BU footpaths, Poole, South West England, England, BH10 4HX, UK",50.74223495,-1.89433739,edu,,United Kingdom

 b6bb883dd14f2737d0d6225cf4acbf050d307634,“A Leopard Cannot Change Its Spots”: Improving Face Recognition Using 3D-Based Caricatures,University of Beira Interior,University of Beira Interior,"Universidade da Beira Interior, R. Marquês de Ávila e Bolama, 6201-001 Covilhã, Portugal",40.27785900,-7.50898300,edu,,Portugal

 b6919d278aa46648ecf6f64218efbd6b0fbc53b8,A joint optimization framework of low-dimensional projection and collaborative representation for discriminative classification,Hong Kong Polytechnic University,Hong Kong Polytechnic University,"hong kong, 11, 育才道 Yuk Choi Road, 尖沙咀 Tsim Sha Tsui, 油尖旺區 Yau Tsim Mong District, 九龍 Kowloon, HK, 00000, 中国",22.30457200,114.17976285,edu,,China

-b64a94c42df080196acb74cd06847297e361ff62,Pedestrian detection based on DCT of multi-channel feature,PLA University,"College of Communications Engineering, PLA University, of Science and Technology, Nanjing, China","China, Jiangsu, Nanjing, Xuanwu, 中山门外大街",32.03522500,118.85531700,edu,,China

+b64a94c42df080196acb74cd06847297e361ff62,Pedestrian detection based on DCT of multi-channel feature,PLA University of Science and Technology,"College of Communications Engineering, PLA University, of Science and Technology, Nanjing, China","China, Jiangsu, Nanjing, Xuanwu, 中山门外大街",32.03522500,118.85531700,mil,,China

 b6685941588febbf66f9bf6a074cd548bc8a567f,Proficient facial expression recognition using HLACLF extraction with a bank of Bayesian classifiers for recognition,Noorul Islam University,"Information Technology, NICHE, Noorul Islam University, Kumaracoil, Thuckulay, India","Thuckalay, Kumaracoil, Tamil Nadu 629180, India",8.25117820,77.35115690,edu,,India

 b664dc4902902b3753ea862378f878487b13cc22,Employing vector quantization algorithm in a transform domain for facial recognition,University of Central Florida,University of Central Florida,"University of Central Florida, Libra Drive, University Park, Orange County, Florida, 32816, USA",28.59899755,-81.19712501,edu,,United States

 b69752efd9c76ba1b902ce49d0ce741d50ae78cf,Improving video event retrieval by user feedback,Leiden University,"Leiden University, Leiden, The Netherlands","Rapenburg 70, 2311 EZ Leiden, Netherlands",52.15714850,4.48520900,edu,,Netherlands

@@ -1140,7 +1140,7 @@ d14badb33740b42833ccd620b344fd75c35df2b7,Group happiness assessment using geomet
 d116bac3b6ad77084c12bea557d42ed4c9d78433,Recognition of occluded facial expressions based on CENTRIST features,University of Campinas,University of Campinas,"USJ, 97, Rua Sílvia Maria Fabro, Kobrasol, Campinas, São José, Microrregião de Florianópolis, Mesorregião da Grande Florianópolis, SC, Região Sul, 88102-130, Brasil",-27.59539950,-48.61542180,edu,,Brazil

 d1ff495047a47ea686cde7806cc04b377ef9984f,Augmented songbook: an augmented reality educational application for raising music awareness,"EPITA Research and Development Laboratory (LRDE), Le Kremlin-Bicêtre, France","EPITA Research and Development Laboratory (LRDE), Le Kremlin-Bicêtre, France","14-16 Rue Voltaire, 94270 Le Kremlin-Bicêtre, France",48.81552200,2.36297300,edu,,France

 d1a8a46e1eb3769ed12f44075d63b49ccfe8c137,Feature specific analysis of a deep convolutional neural network for ageing classification,Newcastle University,Newcastle University,"Newcastle University, Claremont Walk, Haymarket, Newcastle upon Tyne, Tyne and Wear, North East England, England, NE1 7RU, UK",54.98023235,-1.61452627,edu,,United Kingdom

-d1d14401d5c2ffcbba10a867905df47a99445b97,Projective Weight-Based Unsupervised Laplacian Graph Learning for Person Re-Identification,PLA Army Engineering University,"PLA Army Engineering University, China","China, 双龙街60号",31.97090700,118.81289890,edu,,China

+d1d14401d5c2ffcbba10a867905df47a99445b97,Projective Weight-Based Unsupervised Laplacian Graph Learning for Person Re-Identification,PLA Army Engineering University,"PLA Army Engineering University, China","China, Jiangsu, Nanjing, Xuanwu, 中山门外大街",32.03522500,118.85531700,mil,,China

 d1e1c489ffe0331313e9c626a971bbaacc633774,Cheating behavior detection based-on pictorial structure model,"USTECH (Beijing) Technology Co. Ltd., China","USTECH(Beijing) Technology Co. Ltd., China","Beijing, China",39.90419990,116.40739630,company,,China

 d15e93d223ab255a5d3ed6918004784a4a636fd7,Co-Referenced Subspace Clustering,Tianjin University,Tianjin University,"泰山航空港/天津大厦, 枣行路, 枣行 高王寺, 长城路, 大河, 岱岳区 (Daiyue), 泰安市, 山东省, 271000, 中国",36.20304395,117.05842113,edu,,China

 d10cfcf206b0991e3bc20ac28df1f61c63516f30,Smile or smirk? Automatic detection of spontaneous asymmetric smiles to understand viewer experience,"Affectiva, Inc.","Affectiva Inc., Waltham, MA, USA","294 Washington St, Boston, MA 02108, USA",42.35730460,-71.05824150,company,MIT spinoff,United States

@@ -1226,7 +1226,7 @@ d12a53e797f799108415ee925c6cb6e626ce6d89,Double Phase Pedestrian Detection with
 576a15c6a2f83b0b22f692809e4d62ccc114a55e,Face Verification Using GaborWavelets and AdaBoost,University of Reading,"University of Reading, Reading, United Kingdom, RG6 6AY","Reading, UK",51.44142050,-0.94181570,edu,,United Kingdom

 57558bfe896ca98777f6345a07e65752585496cb,Discriminative feature fusion for image classification,"CNRS, Laboratoire Hubert Curien, Saint-Étienne, France","CNRS, UMR 5516, Laboratoire Hubert Curien, F-42000, Saint-Étienne, France","Bâtiment F, 18 Rue Professeur Benoît Lauras, 42000 Saint-Étienne, France",45.45319200,4.38612530,edu,,France

 57f06c0bab366438ebea9ea047678971653923ff,Expression invariant 3D face recognition based on GMDS,Guangdong University of Technology,Guangdong University of Technology,"广东工业大学, 东风东路, 黄花岗街道, 越秀区 (Yuexiu), 广州市, 广东省, 510080, 中国",23.13538360,113.29470496,edu,,China

-579fabe2a068228d4020050da3d68e9314148db5,Person Re-Identification Based on Spatiogram Descriptor and Collaborative Representation,PLA University of Science and Technology,"PLA University of Science and Technology, China","China, 双龙街60号",31.97090700,118.81289890,edu,,China

+579fabe2a068228d4020050da3d68e9314148db5,Person Re-Identification Based on Spatiogram Descriptor and Collaborative Representation,PLA University of Science and Technology,"College of Communications Engineering, PLA University, of Science and Technology, Nanjing, China","China, Jiangsu, Nanjing, Xuanwu, 中山门外大街",32.03522500,118.85531700,mil,,China

 57f3a6ce22c050ca78146b6cc5475c46789647e3,Virtual Sensors Determined Through Machine Learning,University of Oslo,University of Oslo,"UiO, Moltke Moes vei, Blindern, Nordre Aker, Oslo, 0851, Norge",59.93891665,10.72170765,edu,,Norway

 575134b3d99ffde52f238b82b74d6ca3fc5ba08d,Detector with focus: Normalizing gradient in image pyramid,POSTECH,"POSTECH, Pohang 790-784, Republic of Korea","77 Cheongam-ro, Hyogok-dong, Nam-gu, Pohang, Gyeongsangbuk-do, South Korea",36.01388570,129.32318360,edu,,South Korea

 576d8fb95655b67f50a7aafda2bb4e220eba19f4,"Colour Object recognition combining Motion Descriptors, Zernike Moments and Support Vector Machine","University of Sfax, Tunisia","REGIM-Labo: REsearch Groups in Intelligent Machines, University of Sfax, ENIS, BP 1173, Sfax, 3038, Tunisia","Université de Route de l'Aéroport Km 0.5 BP 1169 .3029 Sfax, Sfax, Tunisia",34.73610660,10.74272750,edu,"University of Sfax, Tunisia",Tunisia

@@ -2368,11 +2368,11 @@ f9e9e352862938ce6374381fa4a233ad01ddd3bb,Crowd motion analysis for group detecti
 26a9447fd836c98077d512b224c2c8771b7806d9,Generalized ring averaging: a new method for left and right directional illumination invariant face recognition for frontal poses and their small variants,"D.Y. Patil college of engg., Akurdi, Pune","D.Y. Patil college of engg., Akurdi, Pune","Sector 29, Near Akurdi Railway Station, Nigdi, Pradhikaran, Pimpri-Chinchwad, Pune, Maharashtra 411044, India",18.64484330,73.75818650,edu,,India

 269e098a1297ddb1e3fcd85524dc8272ef2295e9,Real-time identification of pedestrian meeting and split events from surveillance videos using motion similarity and its applications,Electrical and Computer Engineering,Electrical and Computer Engineering,"Electrical and Computer Engineering, Boston Avenue, South Overton, Lubbock, Lubbock County, Texas, 79409, USA",33.58667840,-101.87539204,edu,,United States

 267b78d000fbac96f8187f6baa5ec9d9cdc23536,A Deep Learning Approach for Next Location Prediction,Lanzhou University,Lanzhou University,"222 Tianshui S Rd, Chengguan Qu, Lanzhou Shi, Gansu Sheng, China",36.04776990,103.85856240,edu,,China

-262cdbc57ecf5c18756046c0d8b9aa7eb10e3b19,Local Similarity based Linear Graph Embedding: A Robust Face Recognition Framework for SSPP problem,PLA University of Science and Technology,"PLA University of Science and Technology, China","China, 双龙街60号",31.97090700,118.81289890,edu,,China

+262cdbc57ecf5c18756046c0d8b9aa7eb10e3b19,Local Similarity based Linear Graph Embedding: A Robust Face Recognition Framework for SSPP problem,PLA University of Science and Technology,"College of Communications Engineering, PLA University, of Science and Technology, Nanjing, China","China, Jiangsu, Nanjing, Xuanwu, 中山门外大街",32.03522500,118.85531700,mil,,China

 26be80fcd89edd7d4a0c2aeec463d1aa74f5ff47,Boosting facial expression recognition using LDGP ¿ Local Distinctive Gradient Pattern,Green University of Bangladesh,"Green University of Bangladesh, Dhaka, Bangladesh","220/D, Begum Rokeya Sarani, ( 300 meter North Side of IDB Bhaban), Dhaka 1207, Bangladesh",23.78696130,90.37753390,edu,,Bangladesh

 26e118084d09771bfb996628a7569200100bff1a,Predicting Occupation from Images by Combining Face and Body Context Information,National Chung Cheng University,National Chung Cheng University,"國立中正大學, 168, 鳳凰大道, 民雄鄉, 嘉義縣, 62102, 臺灣",23.56306355,120.47510531,edu,,Taiwan

 26e4d80c4ef601b739904c5acc1a682dbc049af0,"Energy efficient face recognition using row, column feature vectors of Slant Transform and performance comparison with PCA",SVKM's NMIMS University,"Research Scholar, MPSTME, SVKM's NMIMS University, SFIT Borivalli(W) ,Mumbai 400 103, India","Bhakthi Vendanth Swami Marg, Near Cooper Hospital, JVPD Scheme, Vile Parle West, Mumbai, Maharashtra 400056, India",19.10988330,72.83754300,edu,,India

-264a344e4d65cbc14d52f0c875f4534217c37bdd,Patch-based scale calculation for visual tracking,PLA University of Science and Technology (PLAUST),"College of Command Information Systems, PLA University of Science and Technology (PLAUST), Nanjing, China","China, 双龙街60号",31.97090700,118.81289890,edu,,China

+264a344e4d65cbc14d52f0c875f4534217c37bdd,Patch-based scale calculation for visual tracking,PLA University of Science and Technology,"College of Communications Engineering, PLA University, of Science and Technology, Nanjing, China","China, Jiangsu, Nanjing, Xuanwu, 中山门外大街",32.03522500,118.85531700,mil,,China

 26b606ac6beb2977a7853b032416c23c7b36cb8a,Multiscale binarised statistical image features for symmetric unconstrained face matching,Faculty of Electrical and Computer Engineering Urmia University,Faculty of Electrical and Computer Engineering Urmia University,"Urmia, West Azerbaijan Province, Iran",37.65757100,44.97562440,edu,,Iran

 26ec75b8ad066b36f814379a79ad57089c82c079,Learning from ambiguously labeled images,University of Pennsylvania,University of Pennsylvania,"Penn Museum, 3260, South Street, University City, Philadelphia, Philadelphia County, Pennsylvania, 19104, USA",39.94923440,-75.19198985,edu,,United States

 2604aa10fee1900451fe0ddd115c413b0204d1df,An Effective Tread Pattern Image Classification Algorithm based on Transfer Learning,Xi'an University of Posts and Telecommunications,"Xi'an University of Posts and Telecommunications, Xi'an, China","W Chang'an Ave, ChangAnQu XiBu DaXueCheng ShangQuan, Changan Qu, Xian Shi, Shaanxi Sheng, China",34.15246200,108.90348400,edu,,China

@@ -3162,7 +3162,7 @@ f0dce694bd71e46e5b84512f06de046f83a8096e,Learning With $\ell ^{1}$-Graph for Ima
 50e9e9d27132654c85427e4940dd1d40e8652434,Face Recognition Using Most Discriminative Local and Global Features,Chinese Academy of Sciences,Chinese Academy of Sciences,"中国科学院心理研究所, 16, 林萃路, 朝阳区 / Chaoyang, 北京市, 100101, 中国",40.00447950,116.37023800,edu,,China

 50333790dd98c052dfafe1f9bf7bf8b4fc9530ba,Sparse concept discriminant matrix factorization for image representation,Dalian University of Technology,Dalian University of Technology,"大连理工大学, 红凌路, 甘井子区, 凌水镇, 甘井子区 / Ganjingzi, 大连市 / Dalian, 辽宁省, 116023, 中国",38.88140235,121.52281098,edu,,China

 50f1427d24b13ac374859ef851bfe1e05355b958,Detecting bids for eye contact using a wearable camera,Georgia Institute of Technology,Georgia Institute of Technology,"Georgia Tech, Atlantic Drive Northwest, Bellwood, Rockdale, Atlanta, Fulton County, Georgia, 30318, USA",33.77603300,-84.39884086,edu,,United States

-5025a2f2a2d53db48f9504be93358c3ec5261646,Dual Channel Gradient feature for person re-identification,PLA University of Science & Technology,"College of Command Information Systems, PLA University of Science & Technology, Nanjing, China","China, 双龙街60号",31.97090700,118.81289890,edu,,China

+5025a2f2a2d53db48f9504be93358c3ec5261646,Dual Channel Gradient feature for person re-identification,PLA University of Science and Technology,"College of Communications Engineering, PLA University, of Science and Technology, Nanjing, China","China, Jiangsu, Nanjing, Xuanwu, 中山门外大街",32.03522500,118.85531700,mil,,China

 50b290b0b2d14a3f0f9cfd17b15d87b91d76bff1,Gender classification using spatial and temporal features,Computer Science & Engineering Bengal Engineering and Science University,"Computer Science & Engineering Bengal Engineering and Science University, Shibpur Howrah, India","P.O. - Botanic Garden, Howrah, West Bengal 711103, India",22.55518080,88.30713790,edu,,India

 50c919fe195775af1982d2fa8fd4f9d8c2f5cfe8,Supervised Feature Learning Network Based on the Improved LLE for face recognition,University of Mossouri,"University of Mossouri, 65211, USA","E2509 Lafferre Hall, Columbia, MO 65211, USA",38.93313910,-92.37380370,edu,,United States

 504bf308431785153402b7058102f6920a0d8ab3,Pedestrian detection based on improved Random Forest in natural images,Zhejiang Sci-Tech University,"College of Informatics and Electronics, Zhejiang Sci-Tech University, Hangzhou, China","Hangzhou, Zhejiang, China",30.27408400,120.15507000,edu,,China

@@ -4708,7 +4708,7 @@ ddc22765a8fa82d5e76cd7e1d6adf390fcc5b044,Histograms of Oriented Gradients for La
 34f5acebcb39d844d3bef5cf4a840fb505bda5e1,In-depth Exploration of Geotagging Performance using Sampling Strategies on YFCC100M,"CERTH, Thermi-Thessaloniki, Greece","CERTH, Thermi-Thessaloniki, Greece","Thermi 570 01, Greece",40.56676110,22.99801470,edu,,Greece

 346a88bebb07c0ea9ce98b5308df5cb3f8524f2e,Automatic nesting seabird detection based on boosted HOG-LBP descriptors,University of Lincoln,University of Lincoln,"University of Lincoln, Brayford Way, Whitton Park, New Boultham, Lincoln, Lincolnshire, East Midlands, England, LN6 7TS, UK",53.22853665,-0.54873472,edu,,United Kingdom

 344c0917c8d9e13c6b3546da8695332f86b57bd3,Semi-supervised multi-output image manifold regression,Temple University,Temple University,"Temple University School of Podiatric Medicine, Race Street, Chinatown, Philadelphia, Philadelphia County, Pennsylvania, 19103, USA",39.95472495,-75.15346905,edu,,United States

-34ca83537d5d5128dd80cd3c4cae3c45fa5f2263,Deep Supervised Hashing with Pairwise Bit Loss,PLA University of Science & Technology,"College of Command Information Systems, PLA University of Science & Technology, Nanjing, China","China, 双龙街60号",31.97090700,118.81289890,edu,,China

+34ca83537d5d5128dd80cd3c4cae3c45fa5f2263,Deep Supervised Hashing with Pairwise Bit Loss,PLA University of Science and Technology,"College of Communications Engineering, PLA University, of Science and Technology, Nanjing, China","China, Jiangsu, Nanjing, Xuanwu, 中山门外大街",32.03522500,118.85531700,mil,,China

 34832c7177a2201e9368d06ddfe9373aa68ca89a,Cancelable fusion using Social Network Analysis,University of Calgary,University of Calgary,"University of Calgary, Service Tunnel, University Heights, Calgary, Alberta, T2N 1N7, Canada",51.07840380,-114.12870770,edu,,Canada

 34fd227f4fdbc7fe028cc1f7d92cb59204333718,A Deep Face Recognition Method Based on Model Fine-tuning and Principal Component Analysis,Chinese Academy of Sciences,Chinese Academy of Sciences,"中国科学院心理研究所, 16, 林萃路, 朝阳区 / Chaoyang, 北京市, 100101, 中国",40.00447950,116.37023800,edu,,China

 34d14e8752f7e368b78b4e72beb851087cc336b1,EMD Based Face Gender Discrimination,Beijing 100876; Chongqing Three Gorges University,"PRIS Lab of Beijing University of Posts and Telecommunications, Beijing 100876; Chongqing Three Gorges University, Chongqing 404000. E-mail: niexf@tom.com","10 Xitucheng Rd, BeiTaiPingZhuang, Haidian Qu, Beijing Shi, China, 100876",39.96279600,116.35810300,edu,,China

@@ -5008,7 +5008,7 @@ ddc22765a8fa82d5e76cd7e1d6adf390fcc5b044,Histograms of Oriented Gradients for La
 8d2c43759e221f39ab1b4bf70d6891ffd19fb8da,An Automatic Facial Expression Recognition Approach Based on Confusion-Crossed Support Vector Machine Tree,Southeast University,Southeast University,"SEU, 体育馆路, 新街口, 月季园, 玄武区, 南京市, 江苏省, 210008, 中国",32.05752790,118.78682252,edu,,China

 8d203fba8a43e58703618cac3b5147c92f5919d7,Real-Time vehicle detection and tracking using deep neural networks,"State Grid Inner Shizuishan Power Supply Company, Shizuishan, China","State Grid Inner Shizuishan Power Supply Company, Shizuishan, 753000, China","Yongle N Rd, Dawukou Qu, Shizuishan Shi, Ningxia Huizuzizhiqu, China",39.02406500,106.36217600,edu,,China

 8dd62c3a6803aa1f9a102c947cb848affa18f155,A survey on mouth modeling and analysis for Sign Language recognition,Imperial College London,Imperial College London,"Imperial College London, Exhibition Road, Brompton, Royal Borough of Kensington and Chelsea, London, Greater London, England, SW7 2AZ, UK",51.49887085,-0.17560797,edu,,United Kingdom

-8d01119f663d667edb8cbc360340a4ee1a20043b,Scale-adaptive visual tracking with occlusion detection,PLA University of Science and Technology,"PLA University of Science and Technology, China","China, 双龙街60号",31.97090700,118.81289890,edu,,China

+8d01119f663d667edb8cbc360340a4ee1a20043b,Scale-adaptive visual tracking with occlusion detection,PLA University of Science and Technology,"College of Communications Engineering, PLA University, of Science and Technology, Nanjing, China","China, Jiangsu, Nanjing, Xuanwu, 中山门外大街",32.03522500,118.85531700,mil,,China

 8d34ab23d6770a6caebc2be79881481dbf7c64d3,Seeds-Based Part Segmentation by Seeds Propagation and Region Convexity Decomposition,Chinese University of Hong Kong,Chinese University of Hong Kong,"Hong Kong, 馬料水池旁路",22.41626320,114.21093180,edu,,China

 8d7451f5c5849cb0397c3ec8cd7e2081b5d9421d,Multi-target & multi-detector people tracker for mobile robots,"PAL Robotics, Barcelona, Catalunya, Spain","PAL Robotics, Barcelona, Catalunya, Spain","4º 4ª, Carrer de Pujades, 77, 08005 Barcelona, Spain",41.39680080,2.19395650,edu,,Spain

 8d6d50879a7633eca05c8d74a0e586f1f2892e83,TCU: Thread compaction unit for GPGPU applications on mobile graphics hardware,"Institute for Information Industry, Smart Network & System Institute, Taipei, Taiwan","Institute for Information Industry, Smart Network & System Institute 7F, No. 133, Sec. 4, Minsheng E. Rd., Taipei 105, Taiwan, R.O.C.","No. 4, Minsheng East Street, Sanchong District, New Taipei City, Taiwan 241",25.06834470,121.49257330,edu,,Taiwan

@@ -8116,7 +8116,7 @@ a8e7561ada380f2f50211c67fc45c3b3dea96bdb,Illumination Invariant Face Recognition
 37179032085e710d1d62a1ba2e9c1f63bb4dde91,Soft Biometrics and Their Application in Person Recognition at a Distance,Universidad Autonoma de Madrid,Universidad Autonoma de Madrid,"Facultad de Medicina de la Universidad Autónoma de Madrid, Calle de Arturo Duperier, Fuencarral, Fuencarral-El Pardo, Madrid, Área metropolitana de Madrid y Corredor del Henares, Comunidad de Madrid, 28001, España",40.48256135,-3.69060790,edu,,Spain

 37d53c1addb437a1413004424395b43ab09a6e69,An end-to-end generative adversarial network for crowd counting under complicated scenes,Shanghai Jiao Tong University,Shanghai Jiao Tong University,"上海交通大学（徐汇校区）, 淮海西路, 番禺小区, 平阴桥, 徐汇区, 上海市, 200052, 中国",31.20081505,121.42840681,edu,,China

 37de6b09aec4e55e429c47434f1bd29aa02a7457,Detection of unusual human activity based on sequence of actions with MHI and CDP,Meijo University,"Meijo University, Nagoya-shi, Japan","1 Chome-５０１ Shiogamaguchi, Tenpaku Ward, Nagoya, Aichi Prefecture 468-0073, Japan",35.13542910,136.97509830,edu,,Japan

-37191adce691000583f4ec7d805323514268f8dc,An autopilot system based on ROS distributed architecture and deep learning,PLA University of Science and Technology,"PLA University of Science and Technology, China","China, 双龙街60号",31.97090700,118.81289890,edu,,China

+37191adce691000583f4ec7d805323514268f8dc,An autopilot system based on ROS distributed architecture and deep learning,PLA University of Science and Technology,"College of Communications Engineering, PLA University, of Science and Technology, Nanjing, China","China, Jiangsu, Nanjing, Xuanwu, 中山门外大街",32.03522500,118.85531700,mil,,China

 ecbe158c795b2bdbad9a16ac40a12a09c6bf11f1,Deep Temporal Feature Encoding for Action Recognition,"CASIA, National Laboratory of Pattern Recognition","CASIA, National Laboratory of Pattern Recognition","China, Beijing, Haidian, 中关村东路95号",39.97920300,116.33287000,edu,,China

 ecd10c99c033ab01d23447ec8026ac758fd99d0a,Discriminative Block-Diagonal Representation Learning for Image Recognition,Nanjing University,Nanjing University,"NJU, 三江路, 鼓楼区, 南京市, 江苏省, 210093, 中国",32.05659570,118.77408833,edu,,China

 ec06832fe5b4c0629648824414c9de1555e976d4,Incremental learning for bootstrapping object classifier models,UIIS Lab University of Paris-Saclay,"ENSTA ParisTech, UIIS Lab University of Paris-Saclay, 91762 Palaiseau, France","828 Boulevard des Maréchaux, 91120 Palaiseau, France",48.71073390,2.21805010,edu,,France

@@ -8958,7 +8958,7 @@ d76f9f0e8f3f39553ecafd311d55bb4964fe13d8,Plain objects detection in image based
 eaa42e78fd94447534a2e1114fe5bf64d4604169,Classification of tumor histopathology via sparse feature learning,Oregon Health Sciences University,"Center for Spatial Systems Biomedicine, Oregon Health Sciences University, Portland, U.S.A.","Portland, OR, USA",45.51223080,-122.65871850,edu,,United States

 ea20ebb611690e4ed191a6f497c79f6aa2864d90,Curved Gabor Projection Entropy for Face Recognition,Federal University of Piauí,"Federal University of Piauí, Teresina, Brazil","Campus Universitário Ministro Petrônio Portella, s/n - Ininga, Teresina - PI, 64049-550, Brazil",-5.05860470,-42.79852120,edu,,Brazil

 eae04b2343fb2852b67c69b43dcae87c6cf313f3,Two-view face recognition using Bayesian fusion,National Central University Jhongli City,"National Central University Jhongli City, Taoyuan County 32001, Taiwan","320, Taiwan, Taoyuan City, Zhongli District, Zhongda Road, 300號Basement Level, No. 300 Zhongda rd., Zhongli district, Taoyuan, 32001, Taiwan (ROC), Zhongli District, Taoyuan City, 32001",24.96563600,121.19550300,edu,,Taiwan

-ea5a020e64107c75f2f723967af5ad3b97fb2ec3,Multi-scale correlation tracking with convolutional features,PLA University of Science and Technology (PLAUST),"College of Command Information Systems, PLA University of Science and Technology (PLAUST), Nanjing, China","China, 双龙街60号",31.97090700,118.81289890,edu,,China

+ea5a020e64107c75f2f723967af5ad3b97fb2ec3,Multi-scale correlation tracking with convolutional features,PLA University of Science and Technology,"College of Communications Engineering, PLA University, of Science and Technology, Nanjing, China","China, Jiangsu, Nanjing, Xuanwu, 中山门外大街",32.03522500,118.85531700,mil,,China

 eab53c9e3e8442050aa6ad97003f2356a365adaa,Extreme learning machine autoencoder for data augmentation,Universität Hamburg,Universität Hamburg,"Informatikum, 30, Vogt-Kölln-Straße, Stellingen, Eimsbüttel, Hamburg, 22527, Deutschland",53.59948200,9.93353436,edu,,Germany

 ea3fa5e6004c0504feaa31e01b2ea19f138e9a78,Facial expression recognition in video sequences,Jilin University,Jilin University,"吉林大学珠海校区, 丹桂路, 圣堂村, 金湾区, 珠海市, 广东省, 中国",22.05356500,113.39913285,edu,,China

 ea9e5fcdac51328a99e88f14b194abb933bd81b3,Video Classification With CNNs: Using the Codec as a Spatio-Temporal Activity Sensor,University College London,University College London,"UCL Institute of Education, 20, Bedford Way, Holborn, Bloomsbury, London Borough of Camden, London, Greater London, England, WC1H 0AL, UK",51.52316070,-0.12820370,edu,,United Kingdom

@@ -9191,7 +9191,7 @@ a628b28c30cc9e292e6fc9b3a46f9972501c9c67,Sparse Approximation to the Eigensubspa
 a693aa8f2bde89158bbe9aa8abec8428518c8d45,Effectiveness and Users' Experience of Obfuscation as a Privacy-Enhancing Technology for Sharing Photos,Clemson University,Clemson University,"Clemson University, Old Stadium Road, Clemson Heights, Pickens County, South Carolina, 29631, USA",34.66869155,-82.83743476,edu,,United States

 a65301ec723dfac73c1e884d26dedeb4de309429,Incremental generalized low rank approximation of matrices for visual learning and recognition,Dhofar University,"Dhofar University, Oman","Salalah, Oman",17.08962770,54.14584760,edu,,Oman

 a654fdf590a063d15344caccecff76d971635993,A three-dimensional self-organizing neural network architecture for three-dimensional object extraction from a noisy perspective,Visva-Bharati University,"Visva-Bharati University, Santiniketan - 731 235, India","Bolpur, West Bengal 731235, India",23.68245720,87.69048510,edu,,India

-a638c6db2c0629bab6dc2f75f297fe0ae4024de7,Pedestrian detection by PCA-based mixed HOG-LBP features,PLA University of Science and Technology Nan Jing,"PLA University of Science and Technology Nan Jing, China","China, 双龙街60号",31.97090700,118.81289890,edu,,China

+a638c6db2c0629bab6dc2f75f297fe0ae4024de7,Pedestrian detection by PCA-based mixed HOG-LBP features,PLA University of Science and Technology,"College of Communications Engineering, PLA University, of Science and Technology, Nanjing, China","China, Jiangsu, Nanjing, Xuanwu, 中山门外大街",32.03522500,118.85531700,mil,,China

 a69f85258816ed1386a9f64870121f74ef517064,Extending the detection range of vision-based driver assistance systems application to Pedestrian Protection System,University of Ottawa,University of Ottawa,"University of Ottawa, 1, Stewart Street, Byward Market, Lowertown, Rideau-Vanier, Ottawa, Ontario, K1N 6N5, Canada",45.42580475,-75.68740118,edu,,Canada

 a6f79e6b63ff8f6a2e472625d9311e8c60ac36f2,Smart space construction: Integration of robots in a visual sensor network,Indian Institute of Technology Delhi,"IIIT-Delhi, India","IIIT-Delhi, Mathura Road, Friends Colony, South East Delhi, Delhi, 110020, India",28.54632595,77.27325504,edu,,India

 a61a3f7870f7cb0ff73fc9470b28ed94692efe74,Thermal Face Recognition Under Temporal Variation Conditions,"Escuela de Ingeniería Eléctrica, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile","Escuela de Ingeniería Eléctrica, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile","Brasil 2147, Valparaíso, Región de Valparaíso, Chile",-33.04464980,-71.61378180,edu,,Chile

diff --git a/scraper/reports/report_coverage.html b/scraper/reports/report_coverage.html
index 81576f4b..0eaff4ea 100644
--- a/scraper/reports/report_coverage.html
+++ b/scraper/reports/report_coverage.html
@@ -1 +1 @@
-<!doctype html><html><head><meta charset='utf-8'><title>Coverage</title><link rel='stylesheet' href='reports.css'></head><body><h2>Coverage</h2><table border='1' cellpadding='3' cellspacing='3'><th>Paper ID</th><th>Megapixels Key</th><th>Megapixels Name</th><th>Report Link</th><th>PDF Link</th><th>Journal</th><th>Type</th><th>Address</th><th>Country</th><th>Lat</th><th>Lng</th><th>Coverage</th><th>Total Citations</th><th>Geocoded Citations</th><th>Unknown Citations</th><th>Empty Citations</th><th>With PDF</th><th>With DOI</th><tr><td>0e986f51fe45b00633de9fd0c94d082d2be51406</td><td>afw</td><td>AFW</td><td><a href="papers/0e986f51fe45b00633de9fd0c94d082d2be51406.html" target="_blank">Face detection, pose estimation, and landmark localization in the wild</a></td><td><span class="gray">[pdf]</a></td><td>2012 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>999</td><td>709</td><td>290</td><td>35</td><td>576</td><td>422</td></tr><tr><td>162ea969d1929ed180cc6de9f0bf116993ff6e06</td><td>vgg_faces</td><td>VGG Face</td><td><a href="papers/162ea969d1929ed180cc6de9f0bf116993ff6e06.html" target="_blank">Deep Face Recognition</a></td><td><a href="https://pdfs.semanticscholar.org/f372/ab9b3270d4e4f6a0258c83c2736c3a5c0454.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>999</td><td>646</td><td>353</td><td>48</td><td>558</td><td>429</td></tr><tr><td>b5f2846a506fc417e7da43f6a7679146d99c5e96</td><td>ucf_101</td><td>UCF101</td><td><a href="papers/b5f2846a506fc417e7da43f6a7679146d99c5e96.html" target="_blank">UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild</a></td><td><a href="https://arxiv.org/pdf/1212.0402.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>64%</td><td>999</td><td>642</td><td>357</td><td>56</td><td>628</td><td>362</td></tr><tr><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td><td>lfw</td><td>LFW</td><td><a href="papers/370b5757a5379b15e30d619e4d3fb9e8e13f3256.html" target="_blank">Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments</a></td><td><a href="https://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>63%</td><td>999</td><td>631</td><td>368</td><td>59</td><td>598</td><td>382</td></tr><tr><td>5e0f8c355a37a5a89351c02f174e7a5ddcb98683</td><td>coco</td><td>COCO</td><td><a href="papers/5e0f8c355a37a5a89351c02f174e7a5ddcb98683.html" target="_blank">Microsoft COCO: Common Objects in Context</a></td><td><a href="https://arxiv.org/pdf/1405.0312.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>999</td><td>608</td><td>391</td><td>25</td><td>722</td><td>259</td></tr><tr><td>4d9a02d080636e9666c4d1cc438b9893391ec6c7</td><td>cohn_kanade_plus</td><td>CK+</td><td><a href="papers/4d9a02d080636e9666c4d1cc438b9893391ec6c7.html" target="_blank">The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression</a></td><td><span class="gray">[pdf]</a></td><td>2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops</td><td>edu</td><td>University of Pittsburgh</td><td>United States</td><td>40.44415295</td><td>-79.96243993</td><td>61%</td><td>999</td><td>607</td><td>392</td><td>57</td><td>470</td><td>518</td></tr><tr><td>026e3363b7f76b51cc711886597a44d5f1fd1de2</td><td>kitti</td><td>KITTI</td><td><a href="papers/026e3363b7f76b51cc711886597a44d5f1fd1de2.html" target="_blank">Vision meets robotics: The KITTI dataset</a></td><td><a href="https://pdfs.semanticscholar.org/026e/3363b7f76b51cc711886597a44d5f1fd1de2.pdf" target="_blank">[pdf]</a></td><td>I. J. Robotics Res.</td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>999</td><td>602</td><td>397</td><td>36</td><td>553</td><td>462</td></tr><tr><td>0ee1916a0cb2dc7d3add086b5f1092c3d4beb38a</td><td>voc</td><td>VOC</td><td><a href="papers/0ee1916a0cb2dc7d3add086b5f1092c3d4beb38a.html" target="_blank">The Pascal Visual Object Classes (VOC) Challenge</a></td><td><a href="http://eprints.pascal-network.org/archive/00006187/01/PascalVOC_IJCV2009.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td>company</td><td>Microsoft</td><td>United States</td><td>47.64233180</td><td>-122.13693020</td><td>60%</td><td>999</td><td>599</td><td>399</td><td>29</td><td>557</td><td>422</td></tr><tr><td>f72f6a45ee240cc99296a287ff725aaa7e7ebb35</td><td>caltech_pedestrians</td><td>Caltech Pedestrians</td><td><a href="papers/f72f6a45ee240cc99296a287ff725aaa7e7ebb35.html" target="_blank">Pedestrian Detection: An Evaluation of the State of the Art</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td>edu</td><td>California Institute of Technology</td><td>United States</td><td>34.13710185</td><td>-118.12527487</td><td>60%</td><td>999</td><td>596</td><td>403</td><td>70</td><td>527</td><td>466</td></tr><tr><td>759a3b3821d9f0e08e0b0a62c8b693230afc3f8d</td><td>pubfig</td><td>PubFig</td><td><a href="papers/759a3b3821d9f0e08e0b0a62c8b693230afc3f8d.html" target="_blank">Attribute and simile classifiers for face verification</a></td><td><span class="gray">[pdf]</a></td><td>2009 IEEE 12th International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>64%</td><td>914</td><td>583</td><td>331</td><td>47</td><td>586</td><td>316</td></tr><tr><td>6d96f946aaabc734af7fe3fc4454cf8547fcd5ed</td><td>ar_facedb</td><td>AR Face</td><td><a href="papers/6d96f946aaabc734af7fe3fc4454cf8547fcd5ed.html" target="_blank">The AR face database</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>58%</td><td>999</td><td>579</td><td>420</td><td>58</td><td>458</td><td>530</td></tr><tr><td>31b58ced31f22eab10bd3ee2d9174e7c14c27c01</td><td>tiny_images</td><td>Tiny Images</td><td><a href="papers/31b58ced31f22eab10bd3ee2d9174e7c14c27c01.html" target="_blank">80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>999</td><td>574</td><td>425</td><td>89</td><td>644</td><td>337</td></tr><tr><td>10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5</td><td>inria_person</td><td>INRIA Pedestrian</td><td><a href="papers/10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5.html" target="_blank">Histograms of oriented gradients for human detection</a></td><td><span class="gray">[pdf]</a></td><td>2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)</td><td>edu</td><td>INRIA Rhone-Alps, Montbonnot, France</td><td>France</td><td>45.21788600</td><td>5.80736900</td><td>57%</td><td>999</td><td>572</td><td>427</td><td>42</td><td>419</td><td>509</td></tr><tr><td>18ae7c9a4bbc832b8b14bc4122070d7939f5e00e</td><td>frgc</td><td>FRGC</td><td><a href="papers/18ae7c9a4bbc832b8b14bc4122070d7939f5e00e.html" target="_blank">Overview of the face recognition grand challenge</a></td><td><span class="gray">[pdf]</a></td><td>2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)</td><td>edu</td><td>NIST</td><td>United States</td><td>39.14004000</td><td>-77.21850600</td><td>57%</td><td>999</td><td>568</td><td>430</td><td>85</td><td>549</td><td>442</td></tr><tr><td>18c72175ddbb7d5956d180b65a96005c100f6014</td><td>yale_faces</td><td>YaleFaces</td><td><a href="papers/18c72175ddbb7d5956d180b65a96005c100f6014.html" target="_blank">From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose</a></td><td><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.147.1487&rep=rep1&type=pdf" target="_blank">[pdf]</a></td><td>IEEE Trans. Pattern Anal. Mach. Intell.</td><td></td><td></td><td></td><td></td><td></td><td>56%</td><td>999</td><td>561</td><td>438</td><td>66</td><td>498</td><td>462</td></tr><tr><td>2ad0ee93d029e790ebb50574f403a09854b65b7e</td><td>yale_faces</td><td>YaleFaces</td><td><a href="papers/2ad0ee93d029e790ebb50574f403a09854b65b7e.html" target="_blank">Acquiring linear subspaces for face recognition under variable lighting</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>999</td><td>554</td><td>445</td><td>94</td><td>495</td><td>491</td></tr><tr><td>23fc83c8cfff14a16df7ca497661264fc54ed746</td><td>cohn_kanade</td><td>CK</td><td><a href="papers/23fc83c8cfff14a16df7ca497661264fc54ed746.html" target="_blank">Comprehensive Database for Facial Expression Analysis</a></td><td><a href="https://pdfs.semanticscholar.org/23fc/83c8cfff14a16df7ca497661264fc54ed746.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>999</td><td>553</td><td>446</td><td>69</td><td>540</td><td>439</td></tr><tr><td>b62628ac06bbac998a3ab825324a41a11bc3a988</td><td>m2vtsdb_extended</td><td>xm2vtsdb</td><td><a href="papers/b62628ac06bbac998a3ab825324a41a11bc3a988.html" target="_blank">XM2VTSDB : The extended M2VTS database</a></td><td><a href="https://pdfs.semanticscholar.org/b626/28ac06bbac998a3ab825324a41a11bc3a988.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>864</td><td>539</td><td>325</td><td>37</td><td>493</td><td>404</td></tr><tr><td>6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4</td><td>celeba</td><td>CelebA</td><td><a href="papers/6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4.html" target="_blank">Deep Learning Face Attributes in the Wild</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td>edu</td><td>Chinese University of Hong Kong</td><td>China</td><td>22.41626320</td><td>114.21093180</td><td>57%</td><td>919</td><td>526</td><td>392</td><td>61</td><td>694</td><td>201</td></tr><tr><td>dc8b25e35a3acb812beb499844734081722319b4</td><td>feret</td><td>FERET</td><td><a href="papers/dc8b25e35a3acb812beb499844734081722319b4.html" target="_blank">The FERET database and evaluation procedure for face-recognition algorithms</a></td><td><a href="http://biometrics.nist.gov/cs_links/face/frvt/feret/FERET_Database_evaluation_procedure.pdf" target="_blank">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>999</td><td>521</td><td>478</td><td>103</td><td>591</td><td>421</td></tr><tr><td>45c31cde87258414f33412b3b12fc5bec7cb3ba9</td><td>jaffe</td><td>JAFFE</td><td><a href="papers/45c31cde87258414f33412b3b12fc5bec7cb3ba9.html" target="_blank">Coding Facial Expressions with Gabor Wavelets</a></td><td><a href="http://physics.lbl.gov/patrecog/images/Facerecog_gabor.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>899</td><td>508</td><td>391</td><td>51</td><td>431</td><td>451</td></tr><tr><td>55206f0b5f57ce17358999145506cd01e570358c</td><td>orl</td><td>ORL</td><td><a href="papers/55206f0b5f57ce17358999145506cd01e570358c.html" target="_blank">Parameterisation of a stochastic model for human face identification</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>999</td><td>501</td><td>498</td><td>94</td><td>543</td><td>427</td></tr><tr><td>4d423acc78273b75134e2afd1777ba6d3a398973</td><td>cmu_pie</td><td>CMU PIE</td><td><a href="papers/4d423acc78273b75134e2afd1777ba6d3a398973.html" target="_blank">The CMU Pose, Illumination, and Expression (PIE) Database</a></td><td><a href="http://www.comp.nus.edu.sg/~tsim/piedb.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>59%</td><td>760</td><td>449</td><td>310</td><td>49</td><td>404</td><td>345</td></tr><tr><td>4d423acc78273b75134e2afd1777ba6d3a398973</td><td>multi_pie</td><td>MULTIPIE</td><td><a href="papers/4d423acc78273b75134e2afd1777ba6d3a398973.html" target="_blank">The CMU Pose, Illumination, and Expression (PIE) Database</a></td><td><a href="http://www.comp.nus.edu.sg/~tsim/piedb.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>59%</td><td>760</td><td>449</td><td>310</td><td>49</td><td>404</td><td>345</td></tr><tr><td>6273b3491e94ea4dd1ce42b791d77bdc96ee73a8</td><td>viper</td><td>VIPeR</td><td><a href="papers/6273b3491e94ea4dd1ce42b791d77bdc96ee73a8.html" target="_blank">Evaluating Appearance Models for Recognition, Reacquisition, and Tracking</a></td><td><a href="https://pdfs.semanticscholar.org/7847/b1fbccadb780b655e72c66d3f9e93ddb880c.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>edu</td><td>University of California, Santa Cruz</td><td>United States</td><td>36.99158470</td><td>-122.05827710</td><td>67%</td><td>624</td><td>415</td><td>209</td><td>33</td><td>342</td><td>276</td></tr><tr><td>6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3</td><td>cuhk_campus_03</td><td>CUHK03 Campus</td><td><a href="papers/6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3.html" target="_blank">DeepReID: Deep Filter Pairing Neural Network for Person Re-identification</a></td><td><span class="gray">[pdf]</a></td><td>2014 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>73%</td><td>568</td><td>413</td><td>155</td><td>19</td><td>320</td><td>235</td></tr><tr><td>2830fb5282de23d7784b4b4bc37065d27839a412</td><td>h3d</td><td>H3D</td><td><a href="papers/2830fb5282de23d7784b4b4bc37065d27839a412.html" target="_blank">Poselets: Body part detectors trained using 3D human pose annotations</a></td><td><span class="gray">[pdf]</a></td><td>2009 IEEE 12th International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>58%</td><td>716</td><td>413</td><td>303</td><td>59</td><td>492</td><td>222</td></tr><tr><td>2258e01865367018ed6f4262c880df85b94959f8</td><td>mot</td><td>MOT</td><td><a href="papers/2258e01865367018ed6f4262c880df85b94959f8.html" target="_blank">Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics</a></td><td><a href="https://cvhci.anthropomatik.kit.edu/images/stories/msmmi/papers/eurasip2008.pdf" target="_blank">[pdf]</a></td><td>EURASIP J. Image and Video Processing</td><td></td><td></td><td></td><td></td><td></td><td>58%</td><td>632</td><td>366</td><td>264</td><td>44</td><td>358</td><td>264</td></tr><tr><td>4308bd8c28e37e2ed9a3fcfe74d5436cce34b410</td><td>market_1501</td><td>Market 1501</td><td><a href="papers/4308bd8c28e37e2ed9a3fcfe74d5436cce34b410.html" target="_blank">Scalable Person Re-identification: A Benchmark</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td>company</td><td>Microsoft</td><td>United States</td><td>47.64233180</td><td>-122.13693020</td><td>77%</td><td>460</td><td>355</td><td>105</td><td>9</td><td>263</td><td>185</td></tr><tr><td>853bd61bc48a431b9b1c7cab10c603830c488e39</td><td>casia_webface</td><td>CASIA Webface</td><td><a href="papers/853bd61bc48a431b9b1c7cab10c603830c488e39.html" target="_blank">Learning Face Representation from Scratch</a></td><td><a href="https://arxiv.org/pdf/1411.7923.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td>edu</td><td>Chinese Academy of Sciences</td><td>China</td><td>40.00447950</td><td>116.37023800</td><td>71%</td><td>476</td><td>339</td><td>137</td><td>19</td><td>290</td><td>182</td></tr><tr><td>560e0e58d0059259ddf86fcec1fa7975dee6a868</td><td>youtube_faces</td><td>YouTubeFaces</td><td><a href="papers/560e0e58d0059259ddf86fcec1fa7975dee6a868.html" target="_blank">Face recognition in unconstrained videos with matched background similarity</a></td><td><span class="gray">[pdf]</a></td><td>CVPR 2011</td><td>edu</td><td>Tel Aviv University</td><td>Israel</td><td>32.11198890</td><td>34.80459702</td><td>66%</td><td>509</td><td>338</td><td>170</td><td>23</td><td>294</td><td>216</td></tr><tr><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td><td>tud_campus</td><td>TUD-Campus</td><td><a href="papers/3316521a5527c7700af8ae6aef32a79a8b83672c.html" target="_blank">People-tracking-by-detection and people-detection-by-tracking</a></td><td><span class="gray">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>59%</td><td>545</td><td>324</td><td>220</td><td>37</td><td>330</td><td>218</td></tr><tr><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td><td>tud_crossing</td><td>TUD-Crossing</td><td><a href="papers/3316521a5527c7700af8ae6aef32a79a8b83672c.html" target="_blank">People-tracking-by-detection and people-detection-by-tracking</a></td><td><span class="gray">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>59%</td><td>545</td><td>324</td><td>220</td><td>37</td><td>330</td><td>218</td></tr><tr><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td><td>tud_pedestrian</td><td>TUD-Pedestrian</td><td><a href="papers/3316521a5527c7700af8ae6aef32a79a8b83672c.html" target="_blank">People-tracking-by-detection and people-detection-by-tracking</a></td><td><span class="gray">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>59%</td><td>545</td><td>324</td><td>220</td><td>37</td><td>330</td><td>218</td></tr><tr><td>cc589c499dcf323fe4a143bbef0074c3e31f9b60</td><td>bu_3dfe</td><td>BU-3DFE</td><td><a href="papers/cc589c499dcf323fe4a143bbef0074c3e31f9b60.html" target="_blank">A 3D facial expression database for facial behavior research</a></td><td><span class="gray">[pdf]</a></td><td>7th International Conference on Automatic Face and Gesture Recognition (FGR06)</td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>588</td><td>316</td><td>271</td><td>44</td><td>306</td><td>282</td></tr><tr><td>95f12d27c3b4914e0668a268360948bce92f7db3</td><td>helen</td><td>Helen</td><td><a href="papers/95f12d27c3b4914e0668a268360948bce92f7db3.html" target="_blank">Interactive Facial Feature Localization</a></td><td><a href="https://pdfs.semanticscholar.org/95f1/2d27c3b4914e0668a268360948bce92f7db3.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>company</td><td>Adobe</td><td>United States</td><td>37.33077030</td><td>-121.89409510</td><td>85%</td><td>352</td><td>298</td><td>54</td><td>8</td><td>212</td><td>146</td></tr><tr><td>4053e3423fb70ad9140ca89351df49675197196a</td><td>bio_id</td><td>BioID Face</td><td><a href="papers/4053e3423fb70ad9140ca89351df49675197196a.html" target="_blank">Robust Face Detection Using the Hausdorff Distance</a></td><td><a href="https://pdfs.semanticscholar.org/4053/e3423fb70ad9140ca89351df49675197196a.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>511</td><td>289</td><td>222</td><td>49</td><td>329</td><td>182</td></tr><tr><td>8a3c5507237957d013a0fe0f082cab7f757af6ee</td><td>mafl</td><td>MAFL</td><td><a href="papers/8a3c5507237957d013a0fe0f082cab7f757af6ee.html" target="_blank">Facial Landmark Detection by Deep Multi-task Learning</a></td><td><a href="https://pdfs.semanticscholar.org/fcd7/1c18192928a2e0b264edd4d919ab2f8f652a.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>70%</td><td>407</td><td>283</td><td>124</td><td>16</td><td>252</td><td>153</td></tr><tr><td>8a3c5507237957d013a0fe0f082cab7f757af6ee</td><td>mtfl</td><td>MTFL</td><td><a href="papers/8a3c5507237957d013a0fe0f082cab7f757af6ee.html" target="_blank">Facial Landmark Detection by Deep Multi-task Learning</a></td><td><a href="https://pdfs.semanticscholar.org/fcd7/1c18192928a2e0b264edd4d919ab2f8f652a.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>70%</td><td>407</td><td>283</td><td>124</td><td>16</td><td>252</td><td>153</td></tr><tr><td>16c7c31a7553d99f1837fc6e88e77b5ccbb346b8</td><td>prid</td><td>PRID</td><td><a href="papers/16c7c31a7553d99f1837fc6e88e77b5ccbb346b8.html" target="_blank">Person Re-identification by Descriptive and Discriminative Classification</a></td><td><a href="https://pdfs.semanticscholar.org/4c1b/f0592be3e535faf256c95e27982db9b3d3d3.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>68%</td><td>386</td><td>263</td><td>123</td><td>23</td><td>204</td><td>180</td></tr><tr><td>9055b155cbabdce3b98e16e5ac9c0edf00f9552f</td><td>morph</td><td>MORPH Commercial</td><td><a href="papers/9055b155cbabdce3b98e16e5ac9c0edf00f9552f.html" target="_blank">MORPH: a longitudinal image database of normal adult age-progression</a></td><td><span class="gray">[pdf]</a></td><td>7th International Conference on Automatic Face and Gesture Recognition (FGR06)</td><td>edu</td><td>North Carolina University</td><td>United States</td><td>34.22398690</td><td>-77.87013250</td><td>59%</td><td>437</td><td>257</td><td>179</td><td>22</td><td>228</td><td>203</td></tr><tr><td>9055b155cbabdce3b98e16e5ac9c0edf00f9552f</td><td>morph_nc</td><td>MORPH Non-Commercial</td><td><a href="papers/9055b155cbabdce3b98e16e5ac9c0edf00f9552f.html" target="_blank">MORPH: a longitudinal image database of normal adult age-progression</a></td><td><span class="gray">[pdf]</a></td><td>7th International Conference on Automatic Face and Gesture Recognition (FGR06)</td><td>edu</td><td>North Carolina University</td><td>United States</td><td>34.22398690</td><td>-77.87013250</td><td>59%</td><td>437</td><td>257</td><td>179</td><td>22</td><td>228</td><td>203</td></tr><tr><td>044d9a8c61383312cdafbcc44b9d00d650b21c70</td><td>fiw_300</td><td>300-W</td><td><a href="papers/044d9a8c61383312cdafbcc44b9d00d650b21c70.html" target="_blank">300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision Workshops</td><td></td><td></td><td></td><td></td><td></td><td>79%</td><td>323</td><td>255</td><td>68</td><td>15</td><td>208</td><td>120</td></tr><tr><td>2485c98aa44131d1a2f7d1355b1e372f2bb148ad</td><td>cas_peal</td><td>CAS-PEAL</td><td><a href="papers/2485c98aa44131d1a2f7d1355b1e372f2bb148ad.html" target="_blank">The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans</td><td></td><td></td><td></td><td></td><td></td><td>59%</td><td>429</td><td>254</td><td>175</td><td>38</td><td>198</td><td>234</td></tr><tr><td>3325860c0c82a93b2eac654f5324dd6a776f609e</td><td>mpii_human_pose</td><td>MPII Human Pose</td><td><a href="papers/3325860c0c82a93b2eac654f5324dd6a776f609e.html" target="_blank">2D Human Pose Estimation: New Benchmark and State of the Art Analysis</a></td><td><span class="gray">[pdf]</a></td><td>2014 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>387</td><td>251</td><td>136</td><td>21</td><td>291</td><td>96</td></tr><tr><td>2a75f34663a60ab1b04a0049ed1d14335129e908</td><td>mmi_facial_expression</td><td>MMI Facial Expression Dataset</td><td><a href="papers/2a75f34663a60ab1b04a0049ed1d14335129e908.html" target="_blank">Web-based database for facial expression analysis</a></td><td><span class="gray">[pdf]</a></td><td>2005 IEEE International Conference on Multimedia and Expo</td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>464</td><td>250</td><td>214</td><td>45</td><td>282</td><td>188</td></tr><tr><td>75da1df4ed319926c544eefe17ec8d720feef8c0</td><td>fddb</td><td>FDDB</td><td><a href="papers/75da1df4ed319926c544eefe17ec8d720feef8c0.html" target="_blank">FDDB: A benchmark for face detection in unconstrained settings</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>380</td><td>247</td><td>133</td><td>16</td><td>202</td><td>164</td></tr><tr><td>3765df816dc5a061bc261e190acc8bdd9d47bec0</td><td>rafd</td><td>RaFD</td><td><a href="papers/3765df816dc5a061bc261e190acc8bdd9d47bec0.html" target="_blank">Presentation and validation of the Radboud Faces Database</a></td><td><a href="https://pdfs.semanticscholar.org/3765/df816dc5a061bc261e190acc8bdd9d47bec0.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>48%</td><td>487</td><td>234</td><td>253</td><td>39</td><td>342</td><td>144</td></tr><tr><td>2724ba85ec4a66de18da33925e537f3902f21249</td><td>cofw</td><td>COFW</td><td><a href="papers/2724ba85ec4a66de18da33925e537f3902f21249.html" target="_blank">Robust Face Landmark Estimation under Occlusion</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision</td><td>edu</td><td>California Institute of Technology</td><td>United States</td><td>34.13710185</td><td>-118.12527487</td><td>72%</td><td>325</td><td>233</td><td>92</td><td>12</td><td>194</td><td>133</td></tr><tr><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td><td>stickmen_buffy</td><td>Buffy Stickmen</td><td><a href="papers/6dd0597f8513dc100cd0bc1b493768cde45098a9.html" target="_blank">Learning to parse images of articulated bodies</a></td><td><a href="http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2006_899.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>369</td><td>227</td><td>141</td><td>32</td><td>237</td><td>131</td></tr><tr><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td><td>stickmen_pascal</td><td>Stickmen PASCAL</td><td><a href="papers/6dd0597f8513dc100cd0bc1b493768cde45098a9.html" target="_blank">Learning to parse images of articulated bodies</a></td><td><a href="http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2006_899.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>369</td><td>227</td><td>141</td><td>32</td><td>237</td><td>131</td></tr><tr><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td><td>stickmen_pascal</td><td>Stickmen PASCAL</td><td><a href="papers/6dd0597f8513dc100cd0bc1b493768cde45098a9.html" target="_blank">Learning to parse images of articulated bodies</a></td><td><a href="http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2006_899.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>369</td><td>227</td><td>141</td><td>32</td><td>237</td><td>131</td></tr><tr><td>9361b784e73e9238d5cefbea5ac40d35d1e3103f</td><td>oxford_town_centre</td><td>TownCentre</td><td><a href="papers/9361b784e73e9238d5cefbea5ac40d35d1e3103f.html" target="_blank">Stable multi-target tracking in real-time surveillance video</a></td><td><span class="gray">[pdf]</a></td><td>CVPR 2011</td><td>edu</td><td>University of Oxford</td><td>United Kingdom</td><td>51.75345380</td><td>-1.25400997</td><td>67%</td><td>328</td><td>221</td><td>107</td><td>13</td><td>186</td><td>140</td></tr><tr><td>a74251efa970b92925b89eeef50a5e37d9281ad0</td><td>aflw</td><td>AFLW</td><td><a href="papers/a74251efa970b92925b89eeef50a5e37d9281ad0.html" target="_blank">Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization</a></td><td><span class="gray">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td>edu</td><td>TU Graz</td><td>Austria</td><td>47.07071400</td><td>15.43950400</td><td>69%</td><td>318</td><td>218</td><td>100</td><td>27</td><td>211</td><td>107</td></tr><tr><td>13f06b08f371ba8b5d31c3e288b4deb61335b462</td><td>eth_andreas_ess</td><td>ETHZ Pedestrian</td><td><a href="papers/13f06b08f371ba8b5d31c3e288b4deb61335b462.html" target="_blank">Depth and Appearance for Mobile Scene Analysis</a></td><td><span class="gray">[pdf]</a></td><td>2007 IEEE 11th International Conference on Computer Vision</td><td>edu</td><td>ETH Zurich</td><td>Switzerland</td><td>47.37631300</td><td>8.54766990</td><td>62%</td><td>324</td><td>202</td><td>122</td><td>26</td><td>193</td><td>127</td></tr><tr><td>5981e6479c3fd4e31644db35d236bfb84ae46514</td><td>mot</td><td>MOT</td><td><a href="papers/5981e6479c3fd4e31644db35d236bfb84ae46514.html" target="_blank">Learning to associate: HybridBoosted multi-target tracker for crowded scene</a></td><td><span class="gray">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>University of Southern California</td><td>United States</td><td>34.02241490</td><td>-118.28634407</td><td>61%</td><td>326</td><td>200</td><td>125</td><td>22</td><td>190</td><td>137</td></tr><tr><td>2acf7e58f0a526b957be2099c10aab693f795973</td><td>bosphorus</td><td>The Bosphorus</td><td><a href="papers/2acf7e58f0a526b957be2099c10aab693f795973.html" target="_blank">Bosphorus Database for 3D Face Analysis</a></td><td><a href="https://pdfs.semanticscholar.org/4254/fbba3846008f50671edc9cf70b99d7304543.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>56%</td><td>352</td><td>198</td><td>154</td><td>17</td><td>162</td><td>188</td></tr><tr><td>639937b3a1b8bded3f7e9a40e85bd3770016cf3c</td><td>bfm</td><td>BFM</td><td><a href="papers/639937b3a1b8bded3f7e9a40e85bd3770016cf3c.html" target="_blank">A 3D Face Model for Pose and Illumination Invariant Face Recognition</a></td><td><span class="gray">[pdf]</a></td><td>2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>343</td><td>194</td><td>149</td><td>23</td><td>223</td><td>114</td></tr><tr><td>44484d2866f222bbb9b6b0870890f9eea1ffb2d0</td><td>cuhk_campus_03</td><td>CUHK03 Campus</td><td><a href="papers/44484d2866f222bbb9b6b0870890f9eea1ffb2d0.html" target="_blank">Human Reidentification with Transferred Metric Learning</a></td><td><a href="http://www.ee.cuhk.edu.hk/~xgwang/papers/liZWaccv12.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>69%</td><td>280</td><td>194</td><td>86</td><td>9</td><td>139</td><td>137</td></tr><tr><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td><td>leeds_sports_pose</td><td>Leeds Sports Pose</td><td><a href="papers/4b1d23d17476fcf78f4cbadf69fb130b1aa627c0.html" target="_blank">Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</a></td><td><a href="http://www.bmva.org/bmvc/2010/conference/paper12/abstract12.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>285</td><td>185</td><td>100</td><td>13</td><td>197</td><td>93</td></tr><tr><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td><td>stickmen_buffy</td><td>Buffy Stickmen</td><td><a href="papers/4b1d23d17476fcf78f4cbadf69fb130b1aa627c0.html" target="_blank">Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</a></td><td><a href="http://www.bmva.org/bmvc/2010/conference/paper12/abstract12.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>285</td><td>185</td><td>100</td><td>13</td><td>197</td><td>93</td></tr><tr><td>436f798d1a4e54e5947c1e7d7375c31b2bdb4064</td><td>tud_multiview</td><td>TUD-Multiview</td><td><a href="papers/436f798d1a4e54e5947c1e7d7375c31b2bdb4064.html" target="_blank">Monocular 3D pose estimation and tracking by detection</a></td><td><span class="gray">[pdf]</a></td><td>2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>TU Darmstadt</td><td>Germany</td><td>49.87482770</td><td>8.65632810</td><td>59%</td><td>311</td><td>185</td><td>126</td><td>34</td><td>208</td><td>105</td></tr><tr><td>436f798d1a4e54e5947c1e7d7375c31b2bdb4064</td><td>tud_stadtmitte</td><td>TUD-Stadtmitte</td><td><a href="papers/436f798d1a4e54e5947c1e7d7375c31b2bdb4064.html" target="_blank">Monocular 3D pose estimation and tracking by detection</a></td><td><span class="gray">[pdf]</a></td><td>2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>TU Darmstadt</td><td>Germany</td><td>49.87482770</td><td>8.65632810</td><td>59%</td><td>311</td><td>185</td><td>126</td><td>34</td><td>208</td><td>105</td></tr><tr><td>010f0f4929e6a6644fb01f0e43820f91d0fad292</td><td>yfcc_100m</td><td>YFCC100M</td><td><a href="papers/010f0f4929e6a6644fb01f0e43820f91d0fad292.html" target="_blank">YFCC100M: the new data in multimedia research</a></td><td><span class="gray">[pdf]</a></td><td>Commun. ACM</td><td>edu</td><td>Carnegie Mellon University</td><td>United States</td><td>40.44416190</td><td>-79.94272826</td><td>64%</td><td>274</td><td>175</td><td>99</td><td>23</td><td>172</td><td>100</td></tr><tr><td>38b55d95189c5e69cf4ab45098a48fba407609b4</td><td>cuhk_campus_03</td><td>CUHK03 Campus</td><td><a href="papers/38b55d95189c5e69cf4ab45098a48fba407609b4.html" target="_blank">Locally Aligned Feature Transforms across Views</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>64%</td><td>258</td><td>164</td><td>94</td><td>15</td><td>136</td><td>117</td></tr><tr><td>833fa04463d90aab4a9fe2870d480f0b40df446e</td><td>sun_attributes</td><td>SUN</td><td><a href="papers/833fa04463d90aab4a9fe2870d480f0b40df446e.html" target="_blank">SUN attribute database: Discovering, annotating, and recognizing scene attributes</a></td><td><span class="gray">[pdf]</a></td><td>2012 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>Brown University</td><td>United States</td><td>41.82686820</td><td>-71.40123146</td><td>60%</td><td>264</td><td>159</td><td>105</td><td>27</td><td>206</td><td>56</td></tr><tr><td>1be498d4bbc30c3bfd0029114c784bc2114d67c0</td><td>adience</td><td>Adience</td><td><a href="papers/1be498d4bbc30c3bfd0029114c784bc2114d67c0.html" target="_blank">Age and Gender Estimation of Unfiltered Faces</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Information Forensics and Security</td><td>edu</td><td>Open University of Israel</td><td>Israel</td><td>32.77824165</td><td>34.99565673</td><td>87%</td><td>179</td><td>156</td><td>23</td><td>1</td><td>98</td><td>80</td></tr><tr><td>140c95e53c619eac594d70f6369f518adfea12ef</td><td>ijb_a</td><td>IJB-A</td><td><a href="papers/140c95e53c619eac594d70f6369f518adfea12ef.html" target="_blank">Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>66%</td><td>237</td><td>156</td><td>81</td><td>14</td><td>159</td><td>76</td></tr><tr><td>2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9</td><td>graz</td><td>Graz Pedestrian</td><td><a href="papers/2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9.html" target="_blank">Generic object recognition with boosting</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td>edu</td><td>TU Graz</td><td>Austria</td><td>47.07071400</td><td>15.43950400</td><td>53%</td><td>293</td><td>155</td><td>138</td><td>16</td><td>195</td><td>97</td></tr><tr><td>6204776d31359d129a582057c2d788a14f8aadeb</td><td>youtube_celebrities</td><td>YouTube Celebrities</td><td><a href="papers/6204776d31359d129a582057c2d788a14f8aadeb.html" target="_blank">Face tracking and recognition with visual constraints in real-world videos</a></td><td><span class="gray">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>Rutgers University</td><td>United States</td><td>40.47913175</td><td>-74.43168868</td><td>57%</td><td>267</td><td>151</td><td>115</td><td>11</td><td>125</td><td>121</td></tr><tr><td>013909077ad843eb6df7a3e8e290cfd5575999d2</td><td>fiw_300</td><td>300-W</td><td><a href="papers/013909077ad843eb6df7a3e8e290cfd5575999d2.html" target="_blank">A Semi-automatic Methodology for Facial Landmark Annotation</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops</td><td></td><td></td><td></td><td></td><td></td><td>80%</td><td>184</td><td>148</td><td>36</td><td>8</td><td>120</td><td>67</td></tr><tr><td>27a2fad58dd8727e280f97036e0d2bc55ef5424c</td><td>duke_mtmc</td><td>Duke MTMC</td><td><a href="papers/27a2fad58dd8727e280f97036e0d2bc55ef5424c.html" target="_blank">Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking</a></td><td><a href="https://arxiv.org/pdf/1609.01775.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Duke University</td><td>United States</td><td>35.99905220</td><td>-78.92906290</td><td>85%</td><td>169</td><td>144</td><td>25</td><td>3</td><td>113</td><td>54</td></tr><tr><td>4c170a0dcc8de75587dae21ca508dab2f9343974</td><td>face_tracer</td><td>FaceTracer</td><td><a href="papers/4c170a0dcc8de75587dae21ca508dab2f9343974.html" target="_blank">FaceTracer: A Search Engine for Large Collections of Images with Faces</a></td><td><a href="https://pdfs.semanticscholar.org/73a8/1d311eedac8dea3ca24dc15b6990fa4a725e.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>64%</td><td>225</td><td>144</td><td>81</td><td>17</td><td>146</td><td>77</td></tr><tr><td>27a2fad58dd8727e280f97036e0d2bc55ef5424c</td><td>mot</td><td>MOT</td><td><a href="papers/27a2fad58dd8727e280f97036e0d2bc55ef5424c.html" target="_blank">Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking</a></td><td><a href="https://arxiv.org/pdf/1609.01775.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Duke University</td><td>United States</td><td>35.99905220</td><td>-78.92906290</td><td>85%</td><td>169</td><td>144</td><td>25</td><td>3</td><td>113</td><td>54</td></tr><tr><td>98bb029afe2a1239c3fdab517323066f0957b81b</td><td>ilids_mcts_vid</td><td>iLIDS-VID</td><td><a href="papers/98bb029afe2a1239c3fdab517323066f0957b81b.html" target="_blank">Person Re-identification by Video Ranking</a></td><td><a href="https://pdfs.semanticscholar.org/98bb/029afe2a1239c3fdab517323066f0957b81b.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>68%</td><td>209</td><td>143</td><td>66</td><td>8</td><td>111</td><td>97</td></tr><tr><td>98bb029afe2a1239c3fdab517323066f0957b81b</td><td>sdu_vid</td><td>SDU-VID</td><td><a href="papers/98bb029afe2a1239c3fdab517323066f0957b81b.html" target="_blank">Person Re-identification by Video Ranking</a></td><td><a href="https://pdfs.semanticscholar.org/98bb/029afe2a1239c3fdab517323066f0957b81b.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>68%</td><td>209</td><td>143</td><td>66</td><td>8</td><td>111</td><td>97</td></tr><tr><td>e8de844fefd54541b71c9823416daa238be65546</td><td>visual_phrases</td><td>Phrasal Recognition</td><td><a href="papers/e8de844fefd54541b71c9823416daa238be65546.html" target="_blank">Recognition using visual phrases</a></td><td><span class="gray">[pdf]</a></td><td>CVPR 2011</td><td>edu</td><td>University of Illinois, Urbana-Champaign</td><td>United States</td><td>40.11116745</td><td>-88.22587665</td><td>58%</td><td>246</td><td>142</td><td>104</td><td>17</td><td>170</td><td>68</td></tr><tr><td>291265db88023e92bb8c8e6390438e5da148e8f5</td><td>msceleb</td><td>MsCeleb</td><td><a href="papers/291265db88023e92bb8c8e6390438e5da148e8f5.html" target="_blank">MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition</a></td><td><a href="https://arxiv.org/pdf/1607.08221.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>company</td><td>Microsoft</td><td>United States</td><td>47.64233180</td><td>-122.13693020</td><td>78%</td><td>180</td><td>141</td><td>39</td><td>8</td><td>120</td><td>59</td></tr><tr><td>7808937b46acad36e43c30ae4e9f3fd57462853d</td><td>bpad</td><td>BPAD</td><td><a href="papers/7808937b46acad36e43c30ae4e9f3fd57462853d.html" target="_blank">Describing people: A poselet-based approach to attribute classification</a></td><td><span class="gray">[pdf]</a></td><td>2011 International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>230</td><td>140</td><td>90</td><td>14</td><td>163</td><td>66</td></tr><tr><td>46a01565e6afe7c074affb752e7069ee3bf2e4ef</td><td>sdu_vid</td><td>SDU-VID</td><td><a href="papers/46a01565e6afe7c074affb752e7069ee3bf2e4ef.html" target="_blank">Local Descriptors Encoded by Fisher Vectors for Person Re-identification</a></td><td><a href="https://pdfs.semanticscholar.org/a105/f1ef67b4b02da38eadce8ffb4e13aa301a93.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>68%</td><td>197</td><td>133</td><td>64</td><td>15</td><td>108</td><td>88</td></tr><tr><td>0c91808994a250d7be332400a534a9291ca3b60e</td><td>graz</td><td>Graz Pedestrian</td><td><a href="papers/0c91808994a250d7be332400a534a9291ca3b60e.html" target="_blank">Weak Hypotheses and Boosting for Generic Object Detection and Recognition</a></td><td><a href="https://pdfs.semanticscholar.org/0c91/808994a250d7be332400a534a9291ca3b60e.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>56%</td><td>236</td><td>131</td><td>105</td><td>17</td><td>161</td><td>77</td></tr><tr><td>35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62</td><td>coco_qa</td><td>COCO QA</td><td><a href="papers/35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62.html" target="_blank">Exploring Models and Data for Image Question Answering</a></td><td><a href="https://arxiv.org/pdf/1505.02074.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>206</td><td>126</td><td>80</td><td>11</td><td>162</td><td>39</td></tr><tr><td>570f37ed63142312e6ccdf00ecc376341ec72b9f</td><td>stanford_drone</td><td>Stanford Drone</td><td><a href="papers/570f37ed63142312e6ccdf00ecc376341ec72b9f.html" target="_blank">Social LSTM: Human Trajectory Prediction in Crowded Spaces</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>56%</td><td>224</td><td>125</td><td>99</td><td>3</td><td>140</td><td>81</td></tr><tr><td>4e4746094bf60ee83e40d8597a6191e463b57f76</td><td>leeds_sports_pose_extended</td><td>Leeds Sports Pose Extended</td><td><a href="papers/4e4746094bf60ee83e40d8597a6191e463b57f76.html" target="_blank">Learning effective human pose estimation from inaccurate annotation</a></td><td><span class="gray">[pdf]</a></td><td>CVPR 2011</td><td>edu</td><td>University of Leeds</td><td>United Kingdom</td><td>53.80387185</td><td>-1.55245712</td><td>70%</td><td>169</td><td>118</td><td>51</td><td>8</td><td>108</td><td>65</td></tr><tr><td>52d7eb0fbc3522434c13cc247549f74bb9609c5d</td><td>wider_face</td><td>WIDER FACE</td><td><a href="papers/52d7eb0fbc3522434c13cc247549f74bb9609c5d.html" target="_blank">WIDER FACE: A Face Detection Benchmark</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>Chinese University of Hong Kong</td><td>China</td><td>22.41626320</td><td>114.21093180</td><td>66%</td><td>178</td><td>117</td><td>61</td><td>12</td><td>112</td><td>66</td></tr><tr><td>22ad2c8c0f4d6aa4328b38d894b814ec22579761</td><td>gallagher</td><td>Gallagher</td><td><a href="papers/22ad2c8c0f4d6aa4328b38d894b814ec22579761.html" target="_blank">Clothing cosegmentation for recognizing people</a></td><td><span class="gray">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>Carnegie Mellon University</td><td>United States</td><td>40.44416190</td><td>-79.94272826</td><td>65%</td><td>178</td><td>116</td><td>62</td><td>7</td><td>100</td><td>86</td></tr><tr><td>b1f4423c227fa37b9680787be38857069247a307</td><td>afew_va</td><td>AFEW-VA</td><td><a href="papers/b1f4423c227fa37b9680787be38857069247a307.html" target="_blank">Collecting Large, Richly Annotated Facial-Expression Databases from Movies</a></td><td><span class="gray">[pdf]</a></td><td>IEEE MultiMedia</td><td>edu</td><td>Australian National University</td><td>Australia</td><td>-35.27769990</td><td>149.11852700</td><td>64%</td><td>181</td><td>115</td><td>66</td><td>8</td><td>87</td><td>97</td></tr><tr><td>c0387e788a52f10bf35d4d50659cfa515d89fbec</td><td>mars</td><td>MARS</td><td><a href="papers/c0387e788a52f10bf35d4d50659cfa515d89fbec.html" target="_blank">MARS: A Video Benchmark for Large-Scale Person Re-Identification</a></td><td><a href="https://pdfs.semanticscholar.org/c038/7e788a52f10bf35d4d50659cfa515d89fbec.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>68%</td><td>168</td><td>115</td><td>53</td><td>4</td><td>97</td><td>69</td></tr><tr><td>18010284894ed0edcca74e5bf768ee2e15ef7841</td><td>deep_fashion</td><td>DeepFashion</td><td><a href="papers/18010284894ed0edcca74e5bf768ee2e15ef7841.html" target="_blank">DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>64%</td><td>176</td><td>112</td><td>64</td><td>2</td><td>113</td><td>62</td></tr><tr><td>133f01aec1534604d184d56de866a4bd531dac87</td><td>lfw</td><td>LFW</td><td><a href="papers/133f01aec1534604d184d56de866a4bd531dac87.html" target="_blank">Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>183</td><td>109</td><td>74</td><td>13</td><td>103</td><td>77</td></tr><tr><td>0df0d1adea39a5bef318b74faa37de7f3e00b452</td><td>mpii_gaze</td><td>MPIIGaze</td><td><a href="papers/0df0d1adea39a5bef318b74faa37de7f3e00b452.html" target="_blank">Appearance-based gaze estimation in the wild</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>73%</td><td>149</td><td>109</td><td>40</td><td>3</td><td>94</td><td>54</td></tr><tr><td>96e0cfcd81cdeb8282e29ef9ec9962b125f379b0</td><td>megaface</td><td>MegaFace</td><td><a href="papers/96e0cfcd81cdeb8282e29ef9ec9962b125f379b0.html" target="_blank">The MegaFace Benchmark: 1 Million Faces for Recognition at Scale</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>76%</td><td>139</td><td>106</td><td>33</td><td>5</td><td>100</td><td>37</td></tr><tr><td>10195a163ab6348eef37213a46f60a3d87f289c5</td><td>imdb_wiki</td><td>IMDB-Wiki</td><td><a href="papers/10195a163ab6348eef37213a46f60a3d87f289c5.html" target="_blank">Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks</a></td><td><a href="http://www.vision.ee.ethz.ch/en/publications/papers/articles/eth_biwi_01299.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td>edu</td><td>ETH Zurich</td><td>Switzerland</td><td>47.37631300</td><td>8.54766990</td><td>72%</td><td>145</td><td>104</td><td>41</td><td>9</td><td>93</td><td>51</td></tr><tr><td>56ffa7d906b08d02d6d5a12c7377a57e24ef3391</td><td>unbc_shoulder_pain</td><td>UNBC-McMaster Pain</td><td><a href="papers/56ffa7d906b08d02d6d5a12c7377a57e24ef3391.html" target="_blank">Painful data: The UNBC-McMaster shoulder pain expression archive database</a></td><td><span class="gray">[pdf]</a></td><td>Face and Gesture 2011</td><td>edu</td><td>Carnegie Mellon University</td><td>United States</td><td>40.44416190</td><td>-79.94272826</td><td>54%</td><td>189</td><td>103</td><td>86</td><td>21</td><td>108</td><td>78</td></tr><tr><td>29a705a5fa76641e0d8963f1fdd67ee4c0d92d3d</td><td>scface</td><td>SCface</td><td><a href="papers/29a705a5fa76641e0d8963f1fdd67ee4c0d92d3d.html" target="_blank">SCface – surveillance cameras face database</a></td><td><a href="http://scface.org/SCface%20-%20Surveillance%20Cameras%20Face%20Database.pdf" target="_blank">[pdf]</a></td><td>Multimedia Tools and Applications</td><td></td><td></td><td></td><td></td><td></td><td>56%</td><td>179</td><td>101</td><td>78</td><td>15</td><td>88</td><td>89</td></tr><tr><td>5a5f0287484f0d480fed1ce585dbf729586f0edc</td><td>disfa</td><td>DISFA</td><td><a href="papers/5a5f0287484f0d480fed1ce585dbf729586f0edc.html" target="_blank">DISFA: A Spontaneous Facial Action Intensity Database</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Affective Computing</td><td>edu</td><td>University of Denver</td><td>United States</td><td>39.67665410</td><td>-104.96220300</td><td>54%</td><td>184</td><td>100</td><td>84</td><td>17</td><td>96</td><td>89</td></tr><tr><td>32c801cb7fbeb742edfd94cccfca4934baec71da</td><td>ucf_crowd</td><td>UCF-CC-50</td><td><a href="papers/32c801cb7fbeb742edfd94cccfca4934baec71da.html" target="_blank">Multi-source Multi-scale Counting in Extremely Dense Crowd Images</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>68%</td><td>148</td><td>100</td><td>48</td><td>5</td><td>80</td><td>65</td></tr><tr><td>1aad2da473888cb7ebc1bfaa15bfa0f1502ce005</td><td>jpl_pose</td><td>JPL-Interaction dataset</td><td><a href="papers/1aad2da473888cb7ebc1bfaa15bfa0f1502ce005.html" target="_blank">First-Person Activity Recognition: What Are They Doing to Me?</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>67%</td><td>148</td><td>99</td><td>49</td><td>7</td><td>105</td><td>43</td></tr><tr><td>3b5b6d19d4733ab606c39c69a889f9e67967f151</td><td>qmul_grid</td><td>GRID</td><td><a href="papers/3b5b6d19d4733ab606c39c69a889f9e67967f151.html" target="_blank">Multi-camera activity correlation analysis</a></td><td><span class="gray">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>Queen Mary University of London</td><td>United Kingdom</td><td>51.52472720</td><td>-0.03931035</td><td>69%</td><td>142</td><td>98</td><td>44</td><td>7</td><td>77</td><td>64</td></tr><tr><td>4f93cd09785c6e77bf4bc5a788e079df524c8d21</td><td>soton</td><td>SOTON HiD</td><td><a href="papers/4f93cd09785c6e77bf4bc5a788e079df524c8d21.html" target="_blank">On a Large Sequence-Based Human Gait Database</a></td><td><a href="https://pdfs.semanticscholar.org/4f93/cd09785c6e77bf4bc5a788e079df524c8d21.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>63%</td><td>150</td><td>95</td><td>55</td><td>17</td><td>103</td><td>51</td></tr><tr><td>8355d095d3534ef511a9af68a3b2893339e3f96b</td><td>imdb_wiki</td><td>IMDB-Wiki</td><td><a href="papers/8355d095d3534ef511a9af68a3b2893339e3f96b.html" target="_blank">DEX: Deep EXpectation of Apparent Age from a Single Image</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision Workshop (ICCVW)</td><td></td><td></td><td></td><td></td><td></td><td>77%</td><td>122</td><td>94</td><td>28</td><td>4</td><td>75</td><td>48</td></tr><tr><td>8b56e33f33e582f3e473dba573a16b598ed9bcdc</td><td>fei</td><td>FEI</td><td><a href="papers/8b56e33f33e582f3e473dba573a16b598ed9bcdc.html" target="_blank">A new ranking method for principal components analysis and its application to face image analysis</a></td><td><span class="gray">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>169</td><td>93</td><td>76</td><td>6</td><td>69</td><td>102</td></tr><tr><td>e4754afaa15b1b53e70743880484b8d0736990ff</td><td>fiw_300</td><td>300-W</td><td><a href="papers/e4754afaa15b1b53e70743880484b8d0736990ff.html" target="_blank">300 Faces In-The-Wild Challenge: database and results</a></td><td><a href="http://ibug.doc.ic.ac.uk/media/uploads/documents/1-s2.0-s0262885616000147-main.pdf" target="_blank">[pdf]</a></td><td>Image Vision Comput.</td><td>edu</td><td>Imperial College London</td><td>United Kingdom</td><td>51.49887085</td><td>-0.17560797</td><td>71%</td><td>129</td><td>92</td><td>37</td><td>6</td><td>74</td><td>55</td></tr><tr><td>066000d44d6691d27202896691f08b27117918b9</td><td>psu</td><td>PSU</td><td><a href="papers/066000d44d6691d27202896691f08b27117918b9.html" target="_blank">Vision-Based Analysis of Small Groups in Pedestrian Crowds</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>168</td><td>90</td><td>78</td><td>10</td><td>85</td><td>79</td></tr><tr><td>0d3bb75852098b25d90f31d2f48fd0cb4944702b</td><td>face_scrub</td><td>FaceScrub</td><td><a href="papers/0d3bb75852098b25d90f31d2f48fd0cb4944702b.html" target="_blank">A data-driven approach to cleaning large face datasets</a></td><td><span class="gray">[pdf]</a></td><td>2014 IEEE International Conference on Image Processing (ICIP)</td><td></td><td></td><td></td><td></td><td></td><td>64%</td><td>138</td><td>89</td><td>49</td><td>1</td><td>95</td><td>41</td></tr><tr><td>2d3482dcff69c7417c7b933f22de606a0e8e42d4</td><td>lfw</td><td>LFW</td><td><a href="papers/2d3482dcff69c7417c7b933f22de606a0e8e42d4.html" target="_blank">Labeled Faces in the Wild : Updates and New Reporting Procedures</a></td><td><a href="https://pdfs.semanticscholar.org/2d34/82dcff69c7417c7b933f22de606a0e8e42d4.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>edu</td><td>University of Massachusetts</td><td>United States</td><td>42.38897850</td><td>-72.52869870</td><td>69%</td><td>123</td><td>85</td><td>38</td><td>3</td><td>71</td><td>51</td></tr><tr><td>0486214fb58ee9a04edfe7d6a74c6d0f661a7668</td><td>chokepoint</td><td>ChokePoint</td><td><a href="papers/0486214fb58ee9a04edfe7d6a74c6d0f661a7668.html" target="_blank">Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition</a></td><td><span class="gray">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>138</td><td>83</td><td>55</td><td>6</td><td>76</td><td>63</td></tr><tr><td>2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e</td><td>3dpes</td><td>3DPeS</td><td><a href="papers/2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e.html" target="_blank">3DPeS: 3D people dataset for surveillance and forensics</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>133</td><td>82</td><td>51</td><td>9</td><td>73</td><td>58</td></tr><tr><td>5a4df9bef1872865f0b619ac3aacc97f49e4a035</td><td>cuhk_train_station</td><td>CUHK Train Station Dataset</td><td><a href="papers/5a4df9bef1872865f0b619ac3aacc97f49e4a035.html" target="_blank">Understanding collective crowd behaviors: Learning a Mixture model of Dynamic pedestrian-Agents</a></td><td><span class="gray">[pdf]</a></td><td>2012 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>Chinese University of Hong Kong</td><td>China</td><td>22.41626320</td><td>114.21093180</td><td>58%</td><td>141</td><td>82</td><td>59</td><td>5</td><td>60</td><td>75</td></tr><tr><td>b91f54e1581fbbf60392364323d00a0cd43e493c</td><td>bp4d_spontanous</td><td>BP4D-Spontanous</td><td><a href="papers/b91f54e1581fbbf60392364323d00a0cd43e493c.html" target="_blank">A high-resolution spontaneous 3D dynamic facial expression database</a></td><td><span class="gray">[pdf]</a></td><td>2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)</td><td>edu</td><td>SUNY Binghamton</td><td>United States</td><td>42.08779975</td><td>-75.97066066</td><td>52%</td><td>154</td><td>80</td><td>74</td><td>6</td><td>80</td><td>75</td></tr><tr><td>a0fd85b3400c7b3e11122f44dc5870ae2de9009a</td><td>mafl</td><td>MAFL</td><td><a href="papers/a0fd85b3400c7b3e11122f44dc5870ae2de9009a.html" target="_blank">Learning Deep Representation for Face Alignment with Auxiliary Attributes</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>108</td><td>77</td><td>31</td><td>7</td><td>66</td><td>44</td></tr><tr><td>a0fd85b3400c7b3e11122f44dc5870ae2de9009a</td><td>mtfl</td><td>MTFL</td><td><a href="papers/a0fd85b3400c7b3e11122f44dc5870ae2de9009a.html" target="_blank">Learning Deep Representation for Face Alignment with Auxiliary Attributes</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>108</td><td>77</td><td>31</td><td>7</td><td>66</td><td>44</td></tr><tr><td>7f23a4bb0c777dd72cca7665a5f370ac7980217e</td><td>duke_mtmc</td><td>Duke MTMC</td><td><a href="papers/7f23a4bb0c777dd72cca7665a5f370ac7980217e.html" target="_blank">Improving Person Re-identification by Attribute and Identity Learning</a></td><td><a href="https://arxiv.org/pdf/1703.07220.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>84%</td><td>87</td><td>73</td><td>14</td><td>0</td><td>43</td><td>42</td></tr><tr><td>7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22</td><td>lfw</td><td>LFW</td><td><a href="papers/7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22.html" target="_blank">Labeled Faces in the Wild: A Survey</a></td><td><a href="https://pdfs.semanticscholar.org/7de6/e81d775e9cd7becbfd1bd685f4e2a5eebb22.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Stevens Institute of Technology</td><td>United States</td><td>40.74225200</td><td>-74.02709490</td><td>64%</td><td>109</td><td>70</td><td>39</td><td>7</td><td>66</td><td>43</td></tr><tr><td>66e6f08873325d37e0ec20a4769ce881e04e964e</td><td>sun_attributes</td><td>SUN</td><td><a href="papers/66e6f08873325d37e0ec20a4769ce881e04e964e.html" target="_blank">The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding</a></td><td><a href="http://www.cc.gatech.edu/~hays/papers/attribute_ijcv.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>116</td><td>70</td><td>46</td><td>14</td><td>84</td><td>31</td></tr><tr><td>2a4bbee0b4cf52d5aadbbc662164f7efba89566c</td><td>peta</td><td>PETA</td><td><a href="papers/2a4bbee0b4cf52d5aadbbc662164f7efba89566c.html" target="_blank">Pedestrian Attribute Recognition At Far Distance</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>75%</td><td>88</td><td>66</td><td>22</td><td>1</td><td>50</td><td>36</td></tr><tr><td>70c59dc3470ae867016f6ab0e008ac8ba03774a1</td><td>vgg_faces2</td><td>VGG Face2</td><td><a href="papers/70c59dc3470ae867016f6ab0e008ac8ba03774a1.html" target="_blank">VGGFace2: A Dataset for Recognising Faces across Pose and Age</a></td><td><a href="https://arxiv.org/pdf/1710.08092.pdf" target="_blank">[pdf]</a></td><td>2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)</td><td></td><td></td><td></td><td></td><td></td><td>80%</td><td>83</td><td>66</td><td>17</td><td>3</td><td>61</td><td>20</td></tr><tr><td>3394168ff0719b03ff65bcea35336a76b21fe5e4</td><td>penn_fudan</td><td>Penn Fudan</td><td><a href="papers/3394168ff0719b03ff65bcea35336a76b21fe5e4.html" target="_blank">Object Detection Combining Recognition and Segmentation</a></td><td><a href="https://pdfs.semanticscholar.org/3394/168ff0719b03ff65bcea35336a76b21fe5e4.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>105</td><td>64</td><td>41</td><td>9</td><td>58</td><td>43</td></tr><tr><td>3b4ec8af470948a72a6ed37a9fd226719a874ebc</td><td>sdu_vid</td><td>SDU-VID</td><td><a href="papers/3b4ec8af470948a72a6ed37a9fd226719a874ebc.html" target="_blank">A Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>66%</td><td>95</td><td>63</td><td>32</td><td>6</td><td>50</td><td>45</td></tr><tr><td>04c2cda00e5536f4b1508cbd80041e9552880e67</td><td>hipsterwars</td><td>Hipsterwars</td><td><a href="papers/04c2cda00e5536f4b1508cbd80041e9552880e67.html" target="_blank">Hipster Wars: Discovering Elements of Fashion Styles</a></td><td><a href="https://pdfs.semanticscholar.org/04c2/cda00e5536f4b1508cbd80041e9552880e67.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>64%</td><td>95</td><td>61</td><td>34</td><td>4</td><td>59</td><td>35</td></tr><tr><td>06f02199690961ba52997cde1527e714d2b3bf8f</td><td>columbia_gaze</td><td>Columbia Gaze</td><td><a href="papers/06f02199690961ba52997cde1527e714d2b3bf8f.html" target="_blank">Gaze locking: passive eye contact detection for human-object interaction</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Columbia University</td><td>United States</td><td>40.84198360</td><td>-73.94368971</td><td>76%</td><td>79</td><td>60</td><td>19</td><td>0</td><td>49</td><td>34</td></tr><tr><td>4df3143922bcdf7db78eb91e6b5359d6ada004d2</td><td>cfd</td><td>CFD</td><td><a href="papers/4df3143922bcdf7db78eb91e6b5359d6ada004d2.html" target="_blank">The Chicago face database: A free stimulus set of faces and norming data.</a></td><td><a href="https://pdfs.semanticscholar.org/4df3/143922bcdf7db78eb91e6b5359d6ada004d2.pdf" target="_blank">[pdf]</a></td><td>Behavior research methods</td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>99</td><td>59</td><td>40</td><td>1</td><td>73</td><td>21</td></tr><tr><td>0c4a139bb87c6743c7905b29a3cfec27a5130652</td><td>feret</td><td>FERET</td><td><a href="papers/0c4a139bb87c6743c7905b29a3cfec27a5130652.html" target="_blank">The FERET Verification Testing Protocol for Face Recognition Algorithms</a></td><td><a href="https://pdfs.semanticscholar.org/8d2a/1c768fce6f71584dd993fb97e7b6419aaf60.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>edu</td><td>City University of New York</td><td>United States</td><td>40.87228250</td><td>-73.89489171</td><td>51%</td><td>115</td><td>59</td><td>56</td><td>8</td><td>75</td><td>37</td></tr><tr><td>08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7</td><td>kin_face</td><td>UB KinFace</td><td><a href="papers/08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7.html" target="_blank">Understanding Kin Relationships in a Photo</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Multimedia</td><td></td><td></td><td></td><td></td><td></td><td>63%</td><td>94</td><td>59</td><td>35</td><td>1</td><td>33</td><td>61</td></tr><tr><td>2ce2560cf59db59ce313bbeb004e8ce55c5ce928</td><td>texas_3dfrd</td><td>Texas 3DFRD</td><td><a href="papers/2ce2560cf59db59ce313bbeb004e8ce55c5ce928.html" target="_blank">Anthropometric 3D Face Recognition</a></td><td><a href="http://live.ece.utexas.edu/publications/2010/sg_ijcv_june10.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>63%</td><td>91</td><td>57</td><td>34</td><td>5</td><td>60</td><td>31</td></tr><tr><td>5194cbd51f9769ab25260446b4fa17204752e799</td><td>violent_flows</td><td>Violent Flows</td><td><a href="papers/5194cbd51f9769ab25260446b4fa17204752e799.html" target="_blank">Violent flows: Real-time detection of violent crowd behavior</a></td><td><span class="gray">[pdf]</a></td><td>2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops</td><td>edu</td><td>Open University of Israel</td><td>Israel</td><td>32.77824165</td><td>34.99565673</td><td>65%</td><td>88</td><td>57</td><td>31</td><td>6</td><td>45</td><td>44</td></tr><tr><td>3cd40bfa1ff193a96bde0207e5140a399476466c</td><td>tvhi</td><td>TVHI</td><td><a href="papers/3cd40bfa1ff193a96bde0207e5140a399476466c.html" target="_blank">High Five: Recognising human interactions in TV shows</a></td><td><a href="https://pdfs.semanticscholar.org/3cd4/0bfa1ff193a96bde0207e5140a399476466c.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>98</td><td>56</td><td>42</td><td>10</td><td>66</td><td>28</td></tr><tr><td>2160788824c4c29ffe213b2cbeb3f52972d73f37</td><td>3d_rma</td><td>3D-RMA</td><td><a href="papers/2160788824c4c29ffe213b2cbeb3f52972d73f37.html" target="_blank">Automatic 3D face authentication</a></td><td><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.31.9190&rep=rep1&type=pdf" target="_blank">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>100</td><td>54</td><td>46</td><td>8</td><td>63</td><td>36</td></tr><tr><td>ae0aee03d946efffdc7af2362a42d3750e7dd48a</td><td>put_face</td><td>Put Face</td><td><a href="papers/ae0aee03d946efffdc7af2362a42d3750e7dd48a.html" target="_blank">The put face database</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>99</td><td>54</td><td>45</td><td>5</td><td>55</td><td>48</td></tr><tr><td>2edb87494278ad11641b6cf7a3f8996de12b8e14</td><td>qmul_grid</td><td>GRID</td><td><a href="papers/2edb87494278ad11641b6cf7a3f8996de12b8e14.html" target="_blank">Time-Delayed Correlation Analysis for Multi-Camera Activity Understanding</a></td><td><a href="http://www.eecs.qmul.ac.uk/~ccloy/files/ijcv_2010.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td>edu</td><td>Queen Mary University of London</td><td>United Kingdom</td><td>51.52472720</td><td>-0.03931035</td><td>63%</td><td>84</td><td>53</td><td>31</td><td>4</td><td>51</td><td>33</td></tr><tr><td>0b84f07af44f964817675ad961def8a51406dd2e</td><td>prw</td><td>PRW</td><td><a href="papers/0b84f07af44f964817675ad961def8a51406dd2e.html" target="_blank">Person Re-identification in the Wild</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>68%</td><td>77</td><td>52</td><td>25</td><td>1</td><td>47</td><td>27</td></tr><tr><td>0b440695c822a8e35184fb2f60dcdaa8a6de84ae</td><td>kinectface</td><td>KinectFaceDB</td><td><a href="papers/0b440695c822a8e35184fb2f60dcdaa8a6de84ae.html" target="_blank">KinectFaceDB: A Kinect Database for Face Recognition</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Systems, Man, and Cybernetics: Systems</td><td>edu</td><td>University of North Carolina at Chapel Hill</td><td>United States</td><td>35.91139710</td><td>-79.05045290</td><td>61%</td><td>82</td><td>50</td><td>32</td><td>6</td><td>28</td><td>52</td></tr><tr><td>0dc11a37cadda92886c56a6fb5191ded62099c28</td><td>stickmen_family</td><td>We Are Family Stickmen</td><td><a href="papers/0dc11a37cadda92886c56a6fb5191ded62099c28.html" target="_blank">We Are Family: Joint Pose Estimation of Multiple Persons</a></td><td><a href="http://eprints.pascal-network.org/archive/00007964/01/eichner10eccv.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>64%</td><td>78</td><td>50</td><td>28</td><td>5</td><td>54</td><td>23</td></tr><tr><td>0a85bdff552615643dd74646ac881862a7c7072d</td><td>pipa</td><td>PIPA</td><td><a href="papers/0a85bdff552615643dd74646ac881862a7c7072d.html" target="_blank">Beyond frontal faces: Improving Person Recognition using multiple cues</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>company</td><td>Facebook</td><td>United States</td><td>37.39367170</td><td>-122.08072620</td><td>91%</td><td>54</td><td>49</td><td>4</td><td>1</td><td>41</td><td>12</td></tr><tr><td>c900e0ad4c95948baaf0acd8449fde26f9b4952a</td><td>emotio_net</td><td>EmotioNet Database</td><td><a href="papers/c900e0ad4c95948baaf0acd8449fde26f9b4952a.html" target="_blank">EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>86</td><td>45</td><td>41</td><td>7</td><td>54</td><td>29</td></tr><tr><td>f1af714b92372c8e606485a3982eab2f16772ad8</td><td>mug_faces</td><td>MUG Faces</td><td><a href="papers/f1af714b92372c8e606485a3982eab2f16772ad8.html" target="_blank">The MUG facial expression database</a></td><td><span class="gray">[pdf]</a></td><td>11th International Workshop on Image Analysis for Multimedia Interactive Services WIAMIS 10</td><td>edu</td><td>Aristotle University of Thessaloniki</td><td>Greece</td><td>40.62984145</td><td>22.95889350</td><td>55%</td><td>82</td><td>45</td><td>37</td><td>4</td><td>34</td><td>47</td></tr><tr><td>636b8ffc09b1b23ff714ac8350bb35635e49fa3c</td><td>caltech_10k_web_faces</td><td>Caltech 10K Web Faces</td><td><a href="papers/636b8ffc09b1b23ff714ac8350bb35635e49fa3c.html" target="_blank">Pruning training sets for learning of object categories</a></td><td><span class="gray">[pdf]</a></td><td>2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)</td><td></td><td></td><td></td><td></td><td></td><td>70%</td><td>63</td><td>44</td><td>19</td><td>4</td><td>42</td><td>20</td></tr><tr><td>2bf8541199728262f78d4dced6fb91479b39b738</td><td>clothing_co_parsing</td><td>CCP</td><td><a href="papers/2bf8541199728262f78d4dced6fb91479b39b738.html" target="_blank">Clothing Co-parsing by Joint Image Segmentation and Labeling</a></td><td><span class="gray">[pdf]</a></td><td>2014 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>70%</td><td>60</td><td>42</td><td>18</td><td>0</td><td>34</td><td>28</td></tr><tr><td>6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c</td><td>afad</td><td>AFAD</td><td><a href="papers/6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c.html" target="_blank">Ordinal Regression with Multiple Output CNN for Age Estimation</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>53%</td><td>78</td><td>41</td><td>37</td><td>8</td><td>44</td><td>31</td></tr><tr><td>4793f11fbca4a7dba898b9fff68f70d868e2497c</td><td>kin_face</td><td>UB KinFace</td><td><a href="papers/4793f11fbca4a7dba898b9fff68f70d868e2497c.html" target="_blank">Kinship Verification through Transfer Learning</a></td><td><a href="https://pdfs.semanticscholar.org/4793/f11fbca4a7dba898b9fff68f70d868e2497c.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>58%</td><td>71</td><td>41</td><td>30</td><td>2</td><td>29</td><td>42</td></tr><tr><td>f41c7bb02fc97d5fb9cadd7a49c3e558a1c58a44</td><td>pa_100k</td><td>PA-100K</td><td><a href="papers/f41c7bb02fc97d5fb9cadd7a49c3e558a1c58a44.html" target="_blank">HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>75%</td><td>55</td><td>41</td><td>14</td><td>0</td><td>36</td><td>17</td></tr><tr><td>faf40ce28857aedf183e193486f5b4b0a8c478a2</td><td>iit_dehli_ear</td><td>IIT Dehli Ear</td><td><a href="papers/faf40ce28857aedf183e193486f5b4b0a8c478a2.html" target="_blank">Automated Human Identification Using Ear Imaging</a></td><td><a href="https://pdfs.semanticscholar.org/faf4/0ce28857aedf183e193486f5b4b0a8c478a2.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>80</td><td>40</td><td>40</td><td>6</td><td>35</td><td>44</td></tr><tr><td>4d58f886f5150b2d5e48fd1b5a49e09799bf895d</td><td>texas_3dfrd</td><td>Texas 3DFRD</td><td><a href="papers/4d58f886f5150b2d5e48fd1b5a49e09799bf895d.html" target="_blank">Texas 3D Face Recognition Database</a></td><td><span class="gray">[pdf]</a></td><td>2010 IEEE Southwest Symposium on Image Analysis & Interpretation (SSIAI)</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>66</td><td>40</td><td>26</td><td>3</td><td>40</td><td>27</td></tr><tr><td>31de9b3dd6106ce6eec9a35991b2b9083395fd0b</td><td>feret</td><td>FERET</td><td><a href="papers/31de9b3dd6106ce6eec9a35991b2b9083395fd0b.html" target="_blank">FERET ( Face Recognition Technology ) Recognition Algorithm Development and Test Results</a></td><td><a href="https://pdfs.semanticscholar.org/31de/9b3dd6106ce6eec9a35991b2b9083395fd0b.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>75</td><td>39</td><td>36</td><td>5</td><td>54</td><td>20</td></tr><tr><td>47aeb3b82f54b5ae8142b4bdda7b614433e69b9a</td><td>am_fed</td><td>AM-FED</td><td><a href="papers/47aeb3b82f54b5ae8142b4bdda7b614433e69b9a.html" target="_blank">Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops</td><td></td><td></td><td></td><td></td><td></td><td>46%</td><td>83</td><td>38</td><td>45</td><td>6</td><td>43</td><td>39</td></tr><tr><td>22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b</td><td>saivt</td><td>SAIVT SoftBio</td><td><a href="papers/22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b.html" target="_blank">A Database for Person Re-Identification in Multi-Camera Surveillance Networks</a></td><td><span class="gray">[pdf]</a></td><td>2012 International Conference on Digital Image Computing Techniques and Applications (DICTA)</td><td></td><td></td><td></td><td></td><td></td><td>58%</td><td>65</td><td>38</td><td>27</td><td>6</td><td>45</td><td>20</td></tr><tr><td>79828e6e9f137a583082b8b5a9dfce0c301989b8</td><td>mapillary</td><td>Mapillary</td><td><a href="papers/79828e6e9f137a583082b8b5a9dfce0c301989b8.html" target="_blank">The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>61</td><td>37</td><td>24</td><td>0</td><td>43</td><td>16</td></tr><tr><td>3dc3f0b64ef80f573e3a5f96e456e52ee980b877</td><td>georgia_tech_face_database</td><td>Georgia Tech Face</td><td><a href="papers/3dc3f0b64ef80f573e3a5f96e456e52ee980b877.html" target="_blank">Maximum Likelihood Training of the Embedded HMM for Face Detection and Recognition</a></td><td><a href="http://www.researchgate.net/profile/Monson_Hayes/publication/221124512_Maximum_Likelihood_Training_of_the_Embedded_HMM_for_Face_Detection_and_Recognition/links/0deec53509be9d6f55000000.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>67</td><td>36</td><td>31</td><td>4</td><td>29</td><td>28</td></tr><tr><td>6f3c76b7c0bd8e1d122c6ea808a271fd4749c951</td><td>ward</td><td>WARD</td><td><a href="papers/6f3c76b7c0bd8e1d122c6ea808a271fd4749c951.html" target="_blank">Re-identify people in wide area camera network</a></td><td><span class="gray">[pdf]</a></td><td>2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops</td><td>edu</td><td>University of Udine</td><td>Italy</td><td>46.08107230</td><td>13.21194740</td><td>60%</td><td>60</td><td>36</td><td>24</td><td>1</td><td>38</td><td>21</td></tr><tr><td>fcc6fe6007c322641796cb8792718641856a22a7</td><td>miw</td><td>MIW</td><td><a href="papers/fcc6fe6007c322641796cb8792718641856a22a7.html" target="_blank">Automatic facial makeup detection with application in face recognition</a></td><td><span class="gray">[pdf]</a></td><td>2013 International Conference on Biometrics (ICB)</td><td>edu</td><td>West Virginia University</td><td>United States</td><td>39.65404635</td><td>-79.96475355</td><td>71%</td><td>49</td><td>35</td><td>14</td><td>1</td><td>19</td><td>29</td></tr><tr><td>09d78009687bec46e70efcf39d4612822e61cb8c</td><td>raid</td><td>RAiD</td><td><a href="papers/09d78009687bec46e70efcf39d4612822e61cb8c.html" target="_blank">Consistent Re-identification in a Camera Network</a></td><td><a href="https://pdfs.semanticscholar.org/c27f/099e6e7e3f7f9979cbe9e0a5175fc5848ea0.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>49</td><td>35</td><td>14</td><td>3</td><td>34</td><td>13</td></tr><tr><td>fcc6fe6007c322641796cb8792718641856a22a7</td><td>youtube_makeup</td><td>YMU</td><td><a href="papers/fcc6fe6007c322641796cb8792718641856a22a7.html" target="_blank">Automatic facial makeup detection with application in face recognition</a></td><td><span class="gray">[pdf]</a></td><td>2013 International Conference on Biometrics (ICB)</td><td>edu</td><td>West Virginia University</td><td>United States</td><td>39.65404635</td><td>-79.96475355</td><td>71%</td><td>49</td><td>35</td><td>14</td><td>1</td><td>19</td><td>29</td></tr><tr><td>51eba481dac6b229a7490f650dff7b17ce05df73</td><td>imsitu</td><td>imSitu</td><td><a href="papers/51eba481dac6b229a7490f650dff7b17ce05df73.html" target="_blank">Situation Recognition: Visual Semantic Role Labeling for Image Understanding</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>52</td><td>34</td><td>18</td><td>1</td><td>46</td><td>6</td></tr><tr><td>8be57cdad86fdf8c8290df4ca3149592f3c46dd3</td><td>m2vts</td><td>m2vts</td><td><a href="papers/8be57cdad86fdf8c8290df4ca3149592f3c46dd3.html" target="_blank">The M2VTS Multimodal Face Database (Release 1.00)</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>45%</td><td>73</td><td>33</td><td>40</td><td>2</td><td>39</td><td>33</td></tr><tr><td>31b05f65405534a696a847dd19c621b7b8588263</td><td>umd_faces</td><td>UMD</td><td><a href="papers/31b05f65405534a696a847dd19c621b7b8588263.html" target="_blank">UMDFaces: An annotated face dataset for training deep networks</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE International Joint Conference on Biometrics (IJCB)</td><td>edu</td><td>University of Maryland</td><td>United States</td><td>39.28996850</td><td>-76.62196103</td><td>79%</td><td>42</td><td>33</td><td>9</td><td>2</td><td>30</td><td>11</td></tr><tr><td>37d6f0eb074d207b53885bd2eb78ccc8a04be597</td><td>vmu</td><td>VMU</td><td><a href="papers/37d6f0eb074d207b53885bd2eb78ccc8a04be597.html" target="_blank">Can facial cosmetics affect the matching accuracy of face recognition systems?</a></td><td><span class="gray">[pdf]</a></td><td>2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td>edu</td><td>West Virginia University</td><td>United States</td><td>39.65404635</td><td>-79.96475355</td><td>62%</td><td>53</td><td>33</td><td>20</td><td>0</td><td>19</td><td>31</td></tr><tr><td>37d6f0eb074d207b53885bd2eb78ccc8a04be597</td><td>youtube_makeup</td><td>YMU</td><td><a href="papers/37d6f0eb074d207b53885bd2eb78ccc8a04be597.html" target="_blank">Can facial cosmetics affect the matching accuracy of face recognition systems?</a></td><td><span class="gray">[pdf]</a></td><td>2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td>edu</td><td>West Virginia University</td><td>United States</td><td>39.65404635</td><td>-79.96475355</td><td>62%</td><td>53</td><td>33</td><td>20</td><td>0</td><td>19</td><td>31</td></tr><tr><td>9c23859ec7313f2e756a3e85575735e0c52249f4</td><td>facebook_100</td><td>Facebook100</td><td><a href="papers/9c23859ec7313f2e756a3e85575735e0c52249f4.html" target="_blank">Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook</a></td><td><span class="gray">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>Harvard University</td><td>United States</td><td>42.36782045</td><td>-71.12666653</td><td>62%</td><td>52</td><td>32</td><td>20</td><td>3</td><td>38</td><td>13</td></tr><tr><td>2161f6b7ee3c0acc81603b01dc0df689683577b9</td><td>large_scale_person_search</td><td>Large Scale Person Search</td><td><a href="papers/2161f6b7ee3c0acc81603b01dc0df689683577b9.html" target="_blank">End-to-End Deep Learning for Person Search</a></td><td><a href="https://pdfs.semanticscholar.org/2161/f6b7ee3c0acc81603b01dc0df689683577b9.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>70%</td><td>46</td><td>32</td><td>14</td><td>0</td><td>27</td><td>16</td></tr><tr><td>9c23859ec7313f2e756a3e85575735e0c52249f4</td><td>pubfig_83</td><td>pubfig83</td><td><a href="papers/9c23859ec7313f2e756a3e85575735e0c52249f4.html" target="_blank">Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook</a></td><td><span class="gray">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>Harvard University</td><td>United States</td><td>42.36782045</td><td>-71.12666653</td><td>62%</td><td>52</td><td>32</td><td>20</td><td>3</td><td>38</td><td>13</td></tr><tr><td>18858cc936947fc96b5c06bbe3c6c2faa5614540</td><td>pilot_parliament</td><td>PPB</td><td><a href="papers/18858cc936947fc96b5c06bbe3c6c2faa5614540.html" target="_blank">Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification</a></td><td><a href="http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a-supp.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>53%</td><td>59</td><td>31</td><td>28</td><td>0</td><td>47</td><td>10</td></tr><tr><td>16e8b0a1e8451d5f697b94c0c2b32a00abee1d52</td><td>umb</td><td>UMB</td><td><a href="papers/16e8b0a1e8451d5f697b94c0c2b32a00abee1d52.html" target="_blank">UMB-DB: A database of partially occluded 3D faces</a></td><td><span class="gray">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td></td><td></td><td></td><td></td><td></td><td>66%</td><td>47</td><td>31</td><td>16</td><td>2</td><td>22</td><td>24</td></tr><tr><td>356b431d4f7a2a0a38cf971c84568207dcdbf189</td><td>wider</td><td>WIDER</td><td><a href="papers/356b431d4f7a2a0a38cf971c84568207dcdbf189.html" target="_blank">Recognize complex events from static images by fusing deep channels</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>68%</td><td>44</td><td>30</td><td>14</td><td>1</td><td>29</td><td>15</td></tr><tr><td>8b2dd5c61b23ead5ae5508bb8ce808b5ea266730</td><td>10k_US_adult_faces</td><td>10K US Adult Faces</td><td><a href="papers/8b2dd5c61b23ead5ae5508bb8ce808b5ea266730.html" target="_blank">The intrinsic memorability of face photographs.</a></td><td><a href="https://pdfs.semanticscholar.org/8b2d/d5c61b23ead5ae5508bb8ce808b5ea266730.pdf" target="_blank">[pdf]</a></td><td>Journal of experimental psychology. General</td><td></td><td></td><td></td><td></td><td></td><td>56%</td><td>52</td><td>29</td><td>23</td><td>2</td><td>36</td><td>14</td></tr><tr><td>28d4e027c7e90b51b7d8908fce68128d1964668a</td><td>megaface</td><td>MegaFace</td><td><a href="papers/28d4e027c7e90b51b7d8908fce68128d1964668a.html" target="_blank">Level Playing Field for Million Scale Face Recognition</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>University of Washington</td><td>United States</td><td>47.65432380</td><td>-122.30800894</td><td>72%</td><td>39</td><td>28</td><td>11</td><td>2</td><td>29</td><td>9</td></tr><tr><td>070de852bc6eb275d7ca3a9cdde8f6be8795d1a3</td><td>d3dfacs</td><td>D3DFACS</td><td><a href="papers/070de852bc6eb275d7ca3a9cdde8f6be8795d1a3.html" target="_blank">A FACS valid 3D dynamic action unit database with applications to 3D dynamic morphable facial modeling</a></td><td><span class="gray">[pdf]</a></td><td>2011 International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>50</td><td>26</td><td>24</td><td>5</td><td>31</td><td>18</td></tr><tr><td>47662d1a368daf70ba70ef2d59eb6209f98b675d</td><td>fia</td><td>CMU FiA</td><td><a href="papers/47662d1a368daf70ba70ef2d59eb6209f98b675d.html" target="_blank">The CMU Face In Action (FIA) Database</a></td><td><a href="https://pdfs.semanticscholar.org/4766/2d1a368daf70ba70ef2d59eb6209f98b675d.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>48%</td><td>54</td><td>26</td><td>28</td><td>5</td><td>40</td><td>16</td></tr><tr><td>298cbc3dfbbb3a20af4eed97906650a4ea1c29e0</td><td>ferplus</td><td>FER+</td><td><a href="papers/298cbc3dfbbb3a20af4eed97906650a4ea1c29e0.html" target="_blank">Training deep networks for facial expression recognition with crowd-sourced label distribution</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>74%</td><td>34</td><td>25</td><td>9</td><td>0</td><td>18</td><td>16</td></tr><tr><td>e27ef52c641c2b5100a1b34fd0b819e84a31b4df</td><td>sarc3d</td><td>Sarc3D</td><td><a href="papers/e27ef52c641c2b5100a1b34fd0b819e84a31b4df.html" target="_blank">SARC3D: A New 3D Body Model for People Tracking and Re-identification</a></td><td><a href="https://pdfs.semanticscholar.org/e27e/f52c641c2b5100a1b34fd0b819e84a31b4df.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>74%</td><td>34</td><td>25</td><td>9</td><td>2</td><td>21</td><td>12</td></tr><tr><td>57fe081950f21ca03b5b375ae3e84b399c015861</td><td>cvc_01_barcelona</td><td>CVC-01</td><td><a href="papers/57fe081950f21ca03b5b375ae3e84b399c015861.html" target="_blank">Adaptive Image Sampling and Windows Classification for On-board Pedestrian Detection</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>51%</td><td>47</td><td>24</td><td>23</td><td>1</td><td>23</td><td>24</td></tr><tr><td>758d7e1be64cc668c59ef33ba8882c8597406e53</td><td>affectnet</td><td>AffectNet</td><td><a href="papers/758d7e1be64cc668c59ef33ba8882c8597406e53.html" target="_blank">AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild</a></td><td><a href="https://arxiv.org/pdf/1708.03985.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>37</td><td>23</td><td>14</td><td>0</td><td>25</td><td>11</td></tr><tr><td>d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae</td><td>b3d_ac</td><td>B3D(AC)</td><td><a href="papers/d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae.html" target="_blank">A 3-D Audio-Visual Corpus of Affective Communication</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Multimedia</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>42</td><td>23</td><td>19</td><td>2</td><td>26</td><td>15</td></tr><tr><td>53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4</td><td>bp4d_plus</td><td>BP4D+</td><td><a href="papers/53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4.html" target="_blank">Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>42</td><td>23</td><td>19</td><td>0</td><td>17</td><td>26</td></tr><tr><td>1bd1645a629f1b612960ab9bba276afd4cf7c666</td><td>brainwash</td><td>Brainwash</td><td><a href="papers/1bd1645a629f1b612960ab9bba276afd4cf7c666.html" target="_blank">End-to-End People Detection in Crowded Scenes</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>Stanford University</td><td>United States</td><td>37.43131385</td><td>-122.16936535</td><td>55%</td><td>42</td><td>23</td><td>19</td><td>1</td><td>19</td><td>19</td></tr><tr><td>1c2802c2199b6d15ecefe7ba0c39bfe44363de38</td><td>youtube_poses</td><td>YouTube Pose</td><td><a href="papers/1c2802c2199b6d15ecefe7ba0c39bfe44363de38.html" target="_blank">Personalizing Human Video Pose Estimation</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>Oxford University</td><td>United Kingdom</td><td>51.75208490</td><td>-1.25166460</td><td>64%</td><td>36</td><td>23</td><td>13</td><td>2</td><td>30</td><td>8</td></tr><tr><td>0cb2dd5f178e3a297a0c33068961018659d0f443</td><td>ijb_b</td><td>IJB-B</td><td><a href="papers/0cb2dd5f178e3a297a0c33068961018659d0f443.html" target="_blank">IARPA Janus Benchmark-B Face Dataset</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</td><td>edu</td><td>Michigan State University</td><td>United States</td><td>42.71856800</td><td>-84.47791571</td><td>63%</td><td>35</td><td>22</td><td>13</td><td>3</td><td>25</td><td>8</td></tr><tr><td>a0cc5f73a37723a6dd465924143f1cb4976d0169</td><td>msmt_17</td><td>MSMT17</td><td><a href="papers/a0cc5f73a37723a6dd465924143f1cb4976d0169.html" target="_blank">Person Transfer GAN to Bridge Domain Gap for Person Re-identification</a></td><td><span class="gray">[pdf]</a></td><td>2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>92%</td><td>24</td><td>22</td><td>2</td><td>1</td><td>20</td><td>4</td></tr><tr><td>fd8168f1c50de85bac58a8d328df0a50248b16ae</td><td>nd_2006</td><td>ND-2006</td><td><a href="papers/fd8168f1c50de85bac58a8d328df0a50248b16ae.html" target="_blank">Using a Multi-Instance Enrollment Representation to Improve 3D Face Recognition</a></td><td><span class="gray">[pdf]</a></td><td>2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems</td><td>edu</td><td>University of Notre Dame</td><td>United States</td><td>41.70456775</td><td>-86.23822026</td><td>63%</td><td>35</td><td>22</td><td>13</td><td>3</td><td>18</td><td>15</td></tr><tr><td>488e475eeb3bb39a145f23ede197cd3620f1d98a</td><td>apis</td><td>APiS1.0</td><td><a href="papers/488e475eeb3bb39a145f23ede197cd3620f1d98a.html" target="_blank">Pedestrian Attribute Classification in Surveillance: Database and Evaluation</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision Workshops</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>28</td><td>20</td><td>8</td><td>0</td><td>13</td><td>15</td></tr><tr><td>84fe5b4ac805af63206012d29523a1e033bc827e</td><td>awe_ears</td><td>AWE Ears</td><td><a href="papers/84fe5b4ac805af63206012d29523a1e033bc827e.html" target="_blank">Ear Recognition: More Than a Survey</a></td><td><a href="https://arxiv.org/pdf/1611.06203.pdf" target="_blank">[pdf]</a></td><td>Neurocomputing</td><td></td><td></td><td></td><td></td><td></td><td>77%</td><td>26</td><td>20</td><td>6</td><td>0</td><td>10</td><td>16</td></tr><tr><td>20388099cc415c772926e47bcbbe554e133343d1</td><td>cafe</td><td>#N/A</td><td><a href="papers/20388099cc415c772926e47bcbbe554e133343d1.html" target="_blank">The Child Affective Facial Expression (CAFE) set: validity and reliability from untrained adults</a></td><td><a href="https://pdfs.semanticscholar.org/2038/8099cc415c772926e47bcbbe554e133343d1.pdf" target="_blank">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>37</td><td>20</td><td>17</td><td>3</td><td>30</td><td>7</td></tr><tr><td>0ceda9dae8b9f322df65ca2ef02caca9758aec6f</td><td>casablanca</td><td>Casablanca</td><td><a href="papers/0ceda9dae8b9f322df65ca2ef02caca9758aec6f.html" target="_blank">Context-Aware CNNs for Person Head Detection</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>33</td><td>20</td><td>13</td><td>1</td><td>23</td><td>11</td></tr><tr><td>0ceda9dae8b9f322df65ca2ef02caca9758aec6f</td><td>hollywood_headset</td><td>HollywoodHeads</td><td><a href="papers/0ceda9dae8b9f322df65ca2ef02caca9758aec6f.html" target="_blank">Context-Aware CNNs for Person Head Detection</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>33</td><td>20</td><td>13</td><td>1</td><td>23</td><td>11</td></tr><tr><td>0297448f3ed948e136bb06ceff10eccb34e5bb77</td><td>ilids_mcts</td><td>i-LIDS Multiple-Camera</td><td><a href="papers/0297448f3ed948e136bb06ceff10eccb34e5bb77.html" target="_blank">Imagery Library for Intelligent Detection Systems (i-LIDS); A Standard for Testing Video Based Detection Systems</a></td><td><span class="gray">[pdf]</a></td><td>Proceedings 40th Annual 2006 International Carnahan Conference on Security Technology</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>35</td><td>20</td><td>15</td><td>2</td><td>21</td><td>14</td></tr><tr><td>41976ebc8ab76d9a6861487c97cc7fcbe3b6015f</td><td>moments_in_time</td><td>Moments in Time</td><td><a href="papers/41976ebc8ab76d9a6861487c97cc7fcbe3b6015f.html" target="_blank">Moments in Time Dataset: one million videos for event understanding</a></td><td><a href="https://arxiv.org/pdf/1801.03150.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>69%</td><td>29</td><td>20</td><td>9</td><td>2</td><td>27</td><td>2</td></tr><tr><td>488e475eeb3bb39a145f23ede197cd3620f1d98a</td><td>svs</td><td>SVS</td><td><a href="papers/488e475eeb3bb39a145f23ede197cd3620f1d98a.html" target="_blank">Pedestrian Attribute Classification in Surveillance: Database and Evaluation</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision Workshops</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>28</td><td>20</td><td>8</td><td>0</td><td>13</td><td>15</td></tr><tr><td>4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7</td><td>deep_fashion</td><td>DeepFashion</td><td><a href="papers/4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7.html" target="_blank">Fashion Landmark Detection in the Wild</a></td><td><a href="https://arxiv.org/pdf/1608.03049.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>73%</td><td>26</td><td>19</td><td>7</td><td>1</td><td>16</td><td>10</td></tr><tr><td>64e0690dd176a93de9d4328f6e31fc4afe1e7536</td><td>duke_mtmc</td><td>Duke MTMC</td><td><a href="papers/64e0690dd176a93de9d4328f6e31fc4afe1e7536.html" target="_blank">Tracking Multiple People Online and in Real Time</a></td><td><a href="https://pdfs.semanticscholar.org/64e0/690dd176a93de9d4328f6e31fc4afe1e7536.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>78%</td><td>23</td><td>18</td><td>5</td><td>1</td><td>12</td><td>10</td></tr><tr><td>221c18238b829c12b911706947ab38fd017acef7</td><td>rap_pedestrian</td><td>RAP</td><td><a href="papers/221c18238b829c12b911706947ab38fd017acef7.html" target="_blank">A Richly Annotated Dataset for Pedestrian Attribute Recognition</a></td><td><a href="https://arxiv.org/pdf/1603.07054.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>69%</td><td>26</td><td>18</td><td>8</td><td>0</td><td>16</td><td>10</td></tr><tr><td>d818568838433a6d6831adde49a58cef05e0c89f</td><td>agedb</td><td>AgeDB</td><td><a href="papers/d818568838433a6d6831adde49a58cef05e0c89f.html" target="_blank">AgeDB: The First Manually Collected, In-the-Wild Age Database</a></td><td><a href="http://eprints.mdx.ac.uk/22044/1/agedb_kotsia.pdf" target="_blank">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</td><td>edu</td><td>Imperial College London</td><td>United Kingdom</td><td>51.49887085</td><td>-0.17560797</td><td>94%</td><td>18</td><td>17</td><td>1</td><td>0</td><td>14</td><td>3</td></tr><tr><td>213a579af9e4f57f071b884aa872651372b661fd</td><td>bbc_pose</td><td>BBC Pose</td><td><a href="papers/213a579af9e4f57f071b884aa872651372b661fd.html" target="_blank">Automatic and Efficient Human Pose Estimation for Sign Language Videos</a></td><td><a href="http://tomas.pfister.fi/files/charles13ijcv.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>26</td><td>17</td><td>9</td><td>1</td><td>16</td><td>11</td></tr><tr><td>4946ba10a4d5a7d0a38372f23e6622bd347ae273</td><td>coco_action</td><td>COCO-a</td><td><a href="papers/4946ba10a4d5a7d0a38372f23e6622bd347ae273.html" target="_blank">Describing Common Human Visual Actions in Images</a></td><td><a href="https://arxiv.org/pdf/1506.02203.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>68%</td><td>25</td><td>17</td><td>8</td><td>0</td><td>23</td><td>2</td></tr><tr><td>d178cde92ab3dc0dd2ebee5a76a33d556c39448b</td><td>jiku_mobile</td><td>Jiku Mobile Video Dataset</td><td><a href="papers/d178cde92ab3dc0dd2ebee5a76a33d556c39448b.html" target="_blank">The jiku mobile video dataset</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td>edu</td><td>National University of Singapore</td><td>Singapore</td><td>1.29620180</td><td>103.77689944</td><td>71%</td><td>24</td><td>17</td><td>7</td><td>0</td><td>6</td><td>19</td></tr><tr><td>2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d</td><td>kin_face</td><td>UB KinFace</td><td><a href="papers/2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d.html" target="_blank">Genealogical face recognition based on UB KinFace database</a></td><td><span class="gray">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>SUNY Buffalo</td><td>United States</td><td>42.93362780</td><td>-78.88394479</td><td>55%</td><td>31</td><td>17</td><td>14</td><td>0</td><td>11</td><td>21</td></tr><tr><td>ea050801199f98a1c7c1df6769f23f658299a3ae</td><td>mpi_large</td><td>Large MPI Facial Expression</td><td><a href="papers/ea050801199f98a1c7c1df6769f23f658299a3ae.html" target="_blank">The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</a></td><td><a href="https://pdfs.semanticscholar.org/ea05/0801199f98a1c7c1df6769f23f658299a3ae.pdf" target="_blank">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>33</td><td>17</td><td>16</td><td>4</td><td>29</td><td>4</td></tr><tr><td>ea050801199f98a1c7c1df6769f23f658299a3ae</td><td>mpi_small</td><td>Small MPI Facial Expression</td><td><a href="papers/ea050801199f98a1c7c1df6769f23f658299a3ae.html" target="_blank">The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</a></td><td><a href="https://pdfs.semanticscholar.org/ea05/0801199f98a1c7c1df6769f23f658299a3ae.pdf" target="_blank">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>33</td><td>17</td><td>16</td><td>4</td><td>29</td><td>4</td></tr><tr><td>1e3df3ca8feab0b36fd293fe689f93bb2aaac591</td><td>immediacy</td><td>Immediacy</td><td><a href="papers/1e3df3ca8feab0b36fd293fe689f93bb2aaac591.html" target="_blank">Multi-task Recurrent Neural Network for Immediacy Prediction</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>26</td><td>16</td><td>10</td><td>2</td><td>21</td><td>6</td></tr><tr><td>71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6</td><td>umd_faces</td><td>UMD</td><td><a href="papers/71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6.html" target="_blank">The Do’s and Don’ts for CNN-Based Face Verification</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE International Conference on Computer Vision Workshops (ICCVW)</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>26</td><td>16</td><td>10</td><td>2</td><td>16</td><td>8</td></tr><tr><td>19d1b811df60f86cbd5e04a094b07f32fff7a32a</td><td>york_3d</td><td>UOY 3D Face Database</td><td><a href="papers/19d1b811df60f86cbd5e04a094b07f32fff7a32a.html" target="_blank">Three-dimensional face recognition: an eigensurface approach</a></td><td><span class="gray">[pdf]</a></td><td>2004 International Conference on Image Processing, 2004. ICIP '04.</td><td></td><td></td><td></td><td></td><td></td><td>42%</td><td>38</td><td>16</td><td>22</td><td>4</td><td>24</td><td>13</td></tr><tr><td>b6c293f0420f7e945b5916ae44269fb53e139275</td><td>erce</td><td>ERCe</td><td><a href="papers/b6c293f0420f7e945b5916ae44269fb53e139275.html" target="_blank">Video Synopsis by Heterogeneous Multi-source Correlation</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>29</td><td>15</td><td>14</td><td>2</td><td>14</td><td>13</td></tr><tr><td>b6c293f0420f7e945b5916ae44269fb53e139275</td><td>tisi</td><td>Times Square Intersection</td><td><a href="papers/b6c293f0420f7e945b5916ae44269fb53e139275.html" target="_blank">Video Synopsis by Heterogeneous Multi-source Correlation</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>29</td><td>15</td><td>14</td><td>2</td><td>14</td><td>13</td></tr><tr><td>2a171f8d14b6b8735001a11c217af9587d095848</td><td>social_relation</td><td>Social Relation</td><td><a href="papers/2a171f8d14b6b8735001a11c217af9587d095848.html" target="_blank">Learning Social Relation Traits from Face Images</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>23</td><td>14</td><td>9</td><td>4</td><td>16</td><td>7</td></tr><tr><td>5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725</td><td>fpoq</td><td>50 People One Question</td><td><a href="papers/5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725.html" target="_blank">Merging Pose Estimates Across Space and Time</a></td><td><a href="http://authors.library.caltech.edu/41565/1/tracking_bmvc.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>81%</td><td>16</td><td>13</td><td>3</td><td>0</td><td>13</td><td>4</td></tr><tr><td>44d23df380af207f5ac5b41459c722c87283e1eb</td><td>wider_attribute</td><td>WIDER Attribute</td><td><a href="papers/44d23df380af207f5ac5b41459c722c87283e1eb.html" target="_blank">Human Attribute Recognition by Deep Hierarchical Contexts</a></td><td><a href="https://pdfs.semanticscholar.org/8e28/07f2dd53b03a759e372e07f7191cae65c9fd.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>72%</td><td>18</td><td>13</td><td>5</td><td>0</td><td>14</td><td>4</td></tr><tr><td>45e616093a92e5f1e61a7c6037d5f637aa8964af</td><td>malf</td><td>MALF</td><td><a href="papers/45e616093a92e5f1e61a7c6037d5f637aa8964af.html" target="_blank">Fine-grained evaluation on face detection in the wild</a></td><td><span class="gray">[pdf]</a></td><td>2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>17</td><td>12</td><td>5</td><td>0</td><td>12</td><td>5</td></tr><tr><td>774cbb45968607a027ae4729077734db000a1ec5</td><td>urban_tribes</td><td>Urban Tribes</td><td><a href="papers/774cbb45968607a027ae4729077734db000a1ec5.html" target="_blank">From Bikers to Surfers: Visual Recognition of Urban Tribes</a></td><td><a href="http://vision.cornell.edu/se3/wp-content/uploads/2014/09/utribes_bmvc13_final.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>67%</td><td>18</td><td>12</td><td>6</td><td>1</td><td>12</td><td>6</td></tr><tr><td>a94cae786d515d3450d48267e12ca954aab791c4</td><td>yawdd</td><td>YawDD</td><td><a href="papers/a94cae786d515d3450d48267e12ca954aab791c4.html" target="_blank">YawDD: a yawning detection dataset</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>80%</td><td>15</td><td>12</td><td>3</td><td>1</td><td>2</td><td>13</td></tr><tr><td>4e6ee936eb50dd032f7138702fa39b7c18ee8907</td><td>dartmouth_children</td><td>Dartmouth Children</td><td><a href="papers/4e6ee936eb50dd032f7138702fa39b7c18ee8907.html" target="_blank">The Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set</a></td><td><a href="https://pdfs.semanticscholar.org/4e6e/e936eb50dd032f7138702fa39b7c18ee8907.pdf" target="_blank">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>21</td><td>11</td><td>10</td><td>2</td><td>18</td><td>3</td></tr><tr><td>4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06</td><td>distance_nighttime</td><td>Long Distance Heterogeneous Face</td><td><a href="papers/4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06.html" target="_blank">Nighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching</a></td><td><a href="https://pdfs.semanticscholar.org/4156/b7e88f2e0ab0a7c095b9bab199ae2b23bd06.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>22</td><td>11</td><td>11</td><td>3</td><td>11</td><td>10</td></tr><tr><td>b71d1aa90dcbe3638888725314c0d56640c1fef1</td><td>ifdb</td><td>IFDB</td><td><a href="papers/b71d1aa90dcbe3638888725314c0d56640c1fef1.html" target="_blank">Iranian Face Database with age, pose and expression</a></td><td><span class="gray">[pdf]</a></td><td>2007 International Conference on Machine Vision</td><td>edu</td><td>Islamic Azad University</td><td>Iran</td><td>34.84529990</td><td>48.55962120</td><td>48%</td><td>23</td><td>11</td><td>12</td><td>2</td><td>14</td><td>9</td></tr><tr><td>57178b36c21fd7f4529ac6748614bb3374714e91</td><td>ijb_c</td><td>IJB-C</td><td><a href="papers/57178b36c21fd7f4529ac6748614bb3374714e91.html" target="_blank">IARPA Janus Benchmark - C: Face Dataset and Protocol</a></td><td><span class="gray">[pdf]</a></td><td>2018 International Conference on Biometrics (ICB)</td><td></td><td></td><td></td><td></td><td></td><td>79%</td><td>14</td><td>11</td><td>3</td><td>0</td><td>12</td><td>1</td></tr><tr><td>ca3e88d87e1344d076c964ea89d91a75c417f5ee</td><td>imfdb</td><td>IMFDB</td><td><a href="papers/ca3e88d87e1344d076c964ea89d91a75c417f5ee.html" target="_blank">Indian Movie Face Database: A benchmark for face recognition under wide variations</a></td><td><span class="gray">[pdf]</a></td><td>2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)</td><td>edu</td><td>BVBCET, Hubli, India</td><td>India</td><td>15.36883320</td><td>75.12137960</td><td>65%</td><td>17</td><td>11</td><td>6</td><td>0</td><td>11</td><td>5</td></tr><tr><td>25474c21613607f6bb7687a281d5f9d4ffa1f9f3</td><td>faceplace</td><td>Face Place</td><td><a href="papers/25474c21613607f6bb7687a281d5f9d4ffa1f9f3.html" target="_blank">Recognizing disguised faces</a></td><td><a href="https://pdfs.semanticscholar.org/d936/7ceb0be378c3a9ddf7cb741c678c1a3c574c.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>34%</td><td>29</td><td>10</td><td>19</td><td>0</td><td>18</td><td>10</td></tr><tr><td>0f0fcf041559703998abf310e56f8a2f90ee6f21</td><td>feret</td><td>FERET</td><td><a href="papers/0f0fcf041559703998abf310e56f8a2f90ee6f21.html" target="_blank">The FERET Evaluation Methodology for Face-Recognition Algorithms</a></td><td><a href="https://pdfs.semanticscholar.org/5099/7a5605c1f61e09e9a96789ed7495be6625aa.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>34%</td><td>29</td><td>10</td><td>19</td><td>3</td><td>18</td><td>9</td></tr><tr><td>2f43b614607163abf41dfe5d17ef6749a1b61304</td><td>hrt_transgender</td><td>HRT Transgender</td><td><a href="papers/2f43b614607163abf41dfe5d17ef6749a1b61304.html" target="_blank">Investigating the Periocular-Based Face Recognition Across Gender Transformation</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Information Forensics and Security</td><td>edu</td><td>University of North Carolina at Wilmington</td><td>United States</td><td>34.22498270</td><td>-77.86907744</td><td>77%</td><td>13</td><td>10</td><td>3</td><td>0</td><td>6</td><td>8</td></tr><tr><td>4563b46d42079242f06567b3f2e2f7a80cb3befe</td><td>vadana</td><td>VADANA</td><td><a href="papers/4563b46d42079242f06567b3f2e2f7a80cb3befe.html" target="_blank">VADANA: A dense dataset for facial image analysis</a></td><td><span class="gray">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td>edu</td><td>University of Delaware</td><td>United States</td><td>39.68103280</td><td>-75.75401840</td><td>67%</td><td>15</td><td>10</td><td>5</td><td>0</td><td>5</td><td>10</td></tr><tr><td>2624d84503bc2f8e190e061c5480b6aa4d89277a</td><td>afew_va</td><td>AFEW-VA</td><td><a href="papers/2624d84503bc2f8e190e061c5480b6aa4d89277a.html" target="_blank">AFEW-VA database for valence and arousal estimation in-the-wild</a></td><td><a href="https://ibug.doc.ic.ac.uk/media/uploads/documents/afew-va.pdf" target="_blank">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>18</td><td>9</td><td>9</td><td>0</td><td>12</td><td>5</td></tr><tr><td>6403117f9c005ae81f1e8e6d1302f4a045e3d99d</td><td>alert_airport</td><td>ALERT Airport</td><td><a href="papers/6403117f9c005ae81f1e8e6d1302f4a045e3d99d.html" target="_blank">A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>45%</td><td>20</td><td>9</td><td>11</td><td>0</td><td>9</td><td>11</td></tr><tr><td>8d5998cd984e7cce307da7d46f155f9db99c6590</td><td>chalearn</td><td>ChaLearn</td><td><a href="papers/8d5998cd984e7cce307da7d46f155f9db99c6590.html" target="_blank">ChaLearn looking at people: A review of events and resources</a></td><td><span class="gray">[pdf]</a></td><td>2017 International Joint Conference on Neural Networks (IJCNN)</td><td></td><td></td><td></td><td></td><td></td><td>69%</td><td>13</td><td>9</td><td>4</td><td>1</td><td>8</td><td>4</td></tr><tr><td>a8d0b149c2eadaa02204d3e4356fbc8eccf3b315</td><td>hi4d_adsip</td><td>Hi4D-ADSIP</td><td><a href="papers/a8d0b149c2eadaa02204d3e4356fbc8eccf3b315.html" target="_blank">Hi4D-ADSIP 3-D dynamic facial articulation database</a></td><td><span class="gray">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>15</td><td>9</td><td>6</td><td>1</td><td>4</td><td>11</td></tr><tr><td>bd26dabab576adb6af30484183c9c9c8379bf2e0</td><td>scut_fbp</td><td>SCUT-FBP</td><td><a href="papers/bd26dabab576adb6af30484183c9c9c8379bf2e0.html" target="_blank">SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Systems, Man, and Cybernetics</td><td></td><td></td><td></td><td></td><td></td><td>47%</td><td>19</td><td>9</td><td>10</td><td>2</td><td>6</td><td>13</td></tr><tr><td>060820f110a72cbf02c14a6d1085bd6e1d994f6a</td><td>caltech_crp</td><td>Caltech CRP</td><td><a href="papers/060820f110a72cbf02c14a6d1085bd6e1d994f6a.html" target="_blank">Fine-grained classification of pedestrians in video: Benchmark and state of the art</a></td><td><a href="https://arxiv.org/pdf/1605.06177.pdf" target="_blank">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>47%</td><td>17</td><td>8</td><td>9</td><td>0</td><td>9</td><td>8</td></tr><tr><td>2b926b3586399d028b46315d7d9fb9d879e4f79c</td><td>frav3d</td><td>FRAV3D</td><td><a href="papers/2b926b3586399d028b46315d7d9fb9d879e4f79c.html" target="_blank">Multimodal 2D, 2.5D & 3D Face Verification</a></td><td><span class="gray">[pdf]</a></td><td>2006 International Conference on Image Processing</td><td>edu</td><td>Universidad Rey Juan Carlos, Spain</td><td>Spain</td><td>40.33586610</td><td>-3.87694320</td><td>57%</td><td>14</td><td>8</td><td>6</td><td>0</td><td>2</td><td>12</td></tr><tr><td>8f02ec0be21461fbcedf51d864f944cfc42c875f</td><td>hda_plus</td><td>HDA+</td><td><a href="papers/8f02ec0be21461fbcedf51d864f944cfc42c875f.html" target="_blank">The HDA+ Data Set for Research on Fully Automated Re-identification Systems</a></td><td><a href="https://pdfs.semanticscholar.org/8f02/ec0be21461fbcedf51d864f944cfc42c875f.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>16</td><td>8</td><td>8</td><td>1</td><td>10</td><td>6</td></tr><tr><td>c570d1247e337f91e555c3be0e8c8a5aba539d9f</td><td>mcgill</td><td>McGill Real World</td><td><a href="papers/c570d1247e337f91e555c3be0e8c8a5aba539d9f.html" target="_blank">Robust semi-automatic head pose labeling for real-world face video sequences</a></td><td><span class="gray">[pdf]</a></td><td>Multimedia Tools and Applications</td><td>edu</td><td>McGill University</td><td>Canada</td><td>45.50397610</td><td>-73.57496870</td><td>44%</td><td>18</td><td>8</td><td>10</td><td>0</td><td>13</td><td>7</td></tr><tr><td>041d3eedf5e45ce5c5229f0181c5c576ed1fafd6</td><td>ucf_selfie</td><td>UCF Selfie</td><td><a href="papers/041d3eedf5e45ce5c5229f0181c5c576ed1fafd6.html" target="_blank">How to Take a Good Selfie?</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>73%</td><td>11</td><td>8</td><td>3</td><td>0</td><td>7</td><td>5</td></tr><tr><td>633c851ebf625ad7abdda2324e9de093cf623141</td><td>appa_real</td><td>APPA-REAL</td><td><a href="papers/633c851ebf625ad7abdda2324e9de093cf623141.html" target="_blank">Apparent and Real Age Estimation in Still Images with Deep Residual Regressors on Appa-Real Database</a></td><td><span class="gray">[pdf]</a></td><td>2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017)</td><td></td><td></td><td></td><td></td><td></td><td>70%</td><td>10</td><td>7</td><td>3</td><td>0</td><td>8</td><td>3</td></tr><tr><td>2cd7821fcf5fae53a185624f7eeda007434ae037</td><td>geofaces</td><td>GeoFaces</td><td><a href="papers/2cd7821fcf5fae53a185624f7eeda007434ae037.html" target="_blank">Exploring the geo-dependence of human face appearance</a></td><td><a href="http://cs.uky.edu/~jacobs/papers/islam2014faces.pdf" target="_blank">[pdf]</a></td><td>IEEE Winter Conference on Applications of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>88%</td><td>8</td><td>7</td><td>1</td><td>0</td><td>5</td><td>3</td></tr><tr><td>2cd7821fcf5fae53a185624f7eeda007434ae037</td><td>geofaces</td><td>GeoFaces</td><td><a href="papers/2cd7821fcf5fae53a185624f7eeda007434ae037.html" target="_blank">Exploring the geo-dependence of human face appearance</a></td><td><a href="http://cs.uky.edu/~jacobs/papers/islam2014faces.pdf" target="_blank">[pdf]</a></td><td>IEEE Winter Conference on Applications of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>88%</td><td>8</td><td>7</td><td>1</td><td>0</td><td>5</td><td>3</td></tr><tr><td>7f4040b482d16354d5938c1d1b926b544652bf5b</td><td>nova_emotions</td><td>Novaemötions Dataset</td><td><a href="papers/7f4040b482d16354d5938c1d1b926b544652bf5b.html" target="_blank">Competitive affective gaming: winning with a smile</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Universidade NOVA de Lisboa, Caparica, Portugal</td><td>Portugal</td><td>38.66096400</td><td>-9.20581300</td><td>78%</td><td>9</td><td>7</td><td>2</td><td>0</td><td>5</td><td>4</td></tr><tr><td>4b4106614c1d553365bad75d7866bff0de6056ed</td><td>ufi</td><td>UFI</td><td><a href="papers/4b4106614c1d553365bad75d7866bff0de6056ed.html" target="_blank">Unconstrained Facial Images: Database for Face Recognition Under Real-World Conditions</a></td><td><a href="http://home.zcu.cz/~pkral/papers/kral_micai15.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>12</td><td>6</td><td>6</td><td>0</td><td>4</td><td>6</td></tr><tr><td>22f656d0f8426c84a33a267977f511f127bfd7f3</td><td>expw</td><td>ExpW</td><td><a href="papers/22f656d0f8426c84a33a267977f511f127bfd7f3.html" target="_blank">From Facial Expression Recognition to Interpersonal Relation Prediction</a></td><td><a href="https://arxiv.org/pdf/1609.06426.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>11</td><td>6</td><td>5</td><td>0</td><td>5</td><td>4</td></tr><tr><td>4af89578ac237278be310f7660a408b03f12d603</td><td>geofaces</td><td>GeoFaces</td><td><a href="papers/4af89578ac237278be310f7660a408b03f12d603.html" target="_blank">Large-scale geo-facial image analysis</a></td><td><a href="https://pdfs.semanticscholar.org/3ede/3ed28329bf48fbd06438a69c4f855bef003f.pdf" target="_blank">[pdf]</a></td><td>EURASIP J. Image and Video Processing</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>6</td><td>6</td><td>0</td><td>0</td><td>4</td><td>2</td></tr><tr><td>2d45cfd838016a6e39f6b766ffe85acd649440c7</td><td>mcgill</td><td>McGill Real World</td><td><a href="papers/2d45cfd838016a6e39f6b766ffe85acd649440c7.html" target="_blank">Hierarchical temporal graphical model for head pose estimation and subsequent attribute classification in real-world videos</a></td><td><span class="gray">[pdf]</a></td><td>Computer Vision and Image Understanding</td><td></td><td></td><td></td><td></td><td></td><td>75%</td><td>8</td><td>6</td><td>2</td><td>0</td><td>5</td><td>3</td></tr><tr><td>1a40092b493c6b8840257ab7f96051d1a4dbfeb2</td><td>sports_videos_in_the_wild</td><td>SVW</td><td><a href="papers/1a40092b493c6b8840257ab7f96051d1a4dbfeb2.html" target="_blank">Sports Videos in the Wild (SVW): A video dataset for sports analysis</a></td><td><span class="gray">[pdf]</a></td><td>2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)</td><td></td><td></td><td></td><td></td><td></td><td>86%</td><td>7</td><td>6</td><td>1</td><td>1</td><td>5</td><td>2</td></tr><tr><td>0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e</td><td>lag</td><td>LAG</td><td><a href="papers/0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e.html" target="_blank">Large age-gap face verification by feature injection in deep networks</a></td><td><a href="https://arxiv.org/pdf/1602.06149.pdf" target="_blank">[pdf]</a></td><td>Pattern Recognition Letters</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>7</td><td>5</td><td>2</td><td>0</td><td>3</td><td>4</td></tr><tr><td>9e5378e7b336c89735d3bb15cf67eff96f86d39a</td><td>precarious</td><td>Precarious</td><td><a href="papers/9e5378e7b336c89735d3bb15cf67eff96f86d39a.html" target="_blank">Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>36%</td><td>14</td><td>5</td><td>9</td><td>0</td><td>12</td><td>1</td></tr><tr><td>07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1</td><td>uccs</td><td>UCCS</td><td><a href="papers/07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1.html" target="_blank">Large scale unconstrained open set face database</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td>company</td><td>Securics Inc., Colorado Springs, CO</td><td>United States</td><td>38.83388160</td><td>-104.82136340</td><td>83%</td><td>6</td><td>5</td><td>1</td><td>0</td><td>4</td><td>2</td></tr><tr><td>d4f1eb008eb80595bcfdac368e23ae9754e1e745</td><td>uccs</td><td>UCCS</td><td><a href="papers/d4f1eb008eb80595bcfdac368e23ae9754e1e745.html" target="_blank">Unconstrained Face Detection and Open-Set Face Recognition Challenge</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE International Joint Conference on Biometrics (IJCB)</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>5</td><td>5</td><td>0</td><td>0</td><td>4</td><td>1</td></tr><tr><td>8627f019882b024aef92e4eb9355c499c733e5b7</td><td>used</td><td>USED Social Event Dataset</td><td><a href="papers/8627f019882b024aef92e4eb9355c499c733e5b7.html" target="_blank">USED: a large-scale social event detection dataset</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td>edu</td><td>University of Trento</td><td>Italy</td><td>46.06588360</td><td>11.11598940</td><td>71%</td><td>7</td><td>5</td><td>2</td><td>0</td><td>3</td><td>4</td></tr><tr><td>922e0a51a3b8c67c4c6ac09a577ff674cbd28b34</td><td>v47</td><td>V47</td><td><a href="papers/922e0a51a3b8c67c4c6ac09a577ff674cbd28b34.html" target="_blank">Re-identification of pedestrians with variable occlusion and scale</a></td><td><span class="gray">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td>edu</td><td>Kingston University</td><td>United Kingdom</td><td>51.42930860</td><td>-0.26840440</td><td>56%</td><td>9</td><td>5</td><td>4</td><td>1</td><td>5</td><td>4</td></tr><tr><td>7ebb153704706e457ab57b432793d2b6e5d12592</td><td>vgg_celebs_in_places</td><td>CIP</td><td><a href="papers/7ebb153704706e457ab57b432793d2b6e5d12592.html" target="_blank">Faces in Places: compound query retrieval</a></td><td><a href="https://pdfs.semanticscholar.org/7ebb/153704706e457ab57b432793d2b6e5d12592.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>5</td><td>5</td><td>0</td><td>0</td><td>3</td><td>2</td></tr><tr><td>56ae6d94fc6097ec4ca861f0daa87941d1c10b70</td><td>cmdp</td><td>CMDP</td><td><a href="papers/56ae6d94fc6097ec4ca861f0daa87941d1c10b70.html" target="_blank">Distance Estimation of an Unknown Person from a Portrait</a></td><td><a href="http://authors.library.caltech.edu/49084/13/FaceDistanceEstimation_RONCHI.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>44%</td><td>9</td><td>4</td><td>5</td><td>0</td><td>6</td><td>3</td></tr><tr><td>563c940054e4b456661762c1ab858e6f730c3159</td><td>data_61</td><td>Data61 Pedestrian</td><td><a href="papers/563c940054e4b456661762c1ab858e6f730c3159.html" target="_blank">A Multi-modal Graphical Model for Scene Analysis</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE Winter Conference on Applications of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>8</td><td>4</td><td>4</td><td>0</td><td>5</td><td>3</td></tr><tr><td>287ddcb3db5562235d83aee318f318b8d5e43fb1</td><td>erce</td><td>ERCe</td><td><a href="papers/287ddcb3db5562235d83aee318f318b8d5e43fb1.html" target="_blank">Learning from Multiple Sources for Video Summarisation</a></td><td><a href="https://arxiv.org/pdf/1501.03069.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>7</td><td>4</td><td>3</td><td>0</td><td>4</td><td>3</td></tr><tr><td>dd65f71dac86e36eecbd3ed225d016c3336b4a13</td><td>families_in_the_wild</td><td>FIW</td><td><a href="papers/dd65f71dac86e36eecbd3ed225d016c3336b4a13.html" target="_blank">Visual Kinship Recognition of Families in the Wild</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td>edu</td><td>University of Massachusetts Dartmouth</td><td>United States</td><td>41.62772475</td><td>-71.00724501</td><td>80%</td><td>5</td><td>4</td><td>1</td><td>0</td><td>2</td><td>3</td></tr><tr><td>137aa2f891d474fce1e7a1d1e9b3aefe21e22b34</td><td>hrt_transgender</td><td>HRT Transgender</td><td><a href="papers/137aa2f891d474fce1e7a1d1e9b3aefe21e22b34.html" target="_blank">Is the eye region more reliable than the face? A preliminary study of face-based recognition on a transgender dataset</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>7</td><td>4</td><td>3</td><td>1</td><td>3</td><td>5</td></tr><tr><td>23e824d1dfc33f3780dd18076284f07bd99f1c43</td><td>mifs</td><td>MIFS</td><td><a href="papers/23e824d1dfc33f3780dd18076284f07bd99f1c43.html" target="_blank">Spoofing faces using makeup: An investigative study</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)</td><td>edu</td><td>INRIA Méditerranée</td><td>France</td><td>43.61581310</td><td>7.06838000</td><td>67%</td><td>6</td><td>4</td><td>2</td><td>0</td><td>1</td><td>5</td></tr><tr><td>22909dd19a0ec3b6065334cb5be5392cb24d839d</td><td>pets</td><td>PETS 2017</td><td><a href="papers/22909dd19a0ec3b6065334cb5be5392cb24d839d.html" target="_blank">PETS 2017: Dataset and Challenge</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</td><td></td><td></td><td></td><td></td><td></td><td>44%</td><td>9</td><td>4</td><td>5</td><td>0</td><td>1</td><td>8</td></tr><tr><td>54983972aafc8e149259d913524581357b0f91c3</td><td>reseed</td><td>ReSEED</td><td><a href="papers/54983972aafc8e149259d913524581357b0f91c3.html" target="_blank">ReSEED: social event dEtection dataset</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>67%</td><td>6</td><td>4</td><td>2</td><td>1</td><td>1</td><td>5</td></tr><tr><td>287ddcb3db5562235d83aee318f318b8d5e43fb1</td><td>tisi</td><td>Times Square Intersection</td><td><a href="papers/287ddcb3db5562235d83aee318f318b8d5e43fb1.html" target="_blank">Learning from Multiple Sources for Video Summarisation</a></td><td><a href="https://arxiv.org/pdf/1501.03069.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>7</td><td>4</td><td>3</td><td>0</td><td>4</td><td>3</td></tr><tr><td>9e31e77f9543ab42474ba4e9330676e18c242e72</td><td>imdb_face</td><td>IMDb Face</td><td><a href="papers/9e31e77f9543ab42474ba4e9330676e18c242e72.html" target="_blank">The Devil of Face Recognition is in the Noise</a></td><td><a href="https://arxiv.org/pdf/1807.11649.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Nanyang Technological University</td><td>Singapore</td><td>1.34841040</td><td>103.68297965</td><td>50%</td><td>6</td><td>3</td><td>3</td><td>0</td><td>4</td><td>1</td></tr><tr><td>9cc8cf0c7d7fa7607659921b6ff657e17e135ecc</td><td>mafa</td><td>MAsked FAces</td><td><a href="papers/9cc8cf0c7d7fa7607659921b6ff657e17e135ecc.html" target="_blank">Detecting Masked Faces in the Wild with LLE-CNNs</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>5</td><td>3</td><td>2</td><td>1</td><td>4</td><td>1</td></tr><tr><td>a7fe834a0af614ce6b50dc093132b031dd9a856b</td><td>market_1501</td><td>Market 1501</td><td><a href="papers/a7fe834a0af614ce6b50dc093132b031dd9a856b.html" target="_blank">Orientation Driven Bag of Appearances for Person Re-identification</a></td><td><a href="https://arxiv.org/pdf/1605.02464.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>7</td><td>3</td><td>4</td><td>0</td><td>4</td><td>4</td></tr><tr><td>c72a2ea819df9b0e8cd267eebcc6528b8741e03d</td><td>megaage</td><td>MegaAge</td><td><a href="papers/c72a2ea819df9b0e8cd267eebcc6528b8741e03d.html" target="_blank">Quantifying Facial Age by Posterior of Age Comparisons</a></td><td><a href="https://arxiv.org/pdf/1708.09687.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>75%</td><td>4</td><td>3</td><td>1</td><td>0</td><td>4</td><td>0</td></tr><tr><td>578d4ad74818086bb64f182f72e2c8bd31e3d426</td><td>mr2</td><td>MR2</td><td><a href="papers/578d4ad74818086bb64f182f72e2c8bd31e3d426.html" target="_blank">The MR2: A multi-racial, mega-resolution database of facial stimuli.</a></td><td><a href="http://www.mpmlab.org/The%20MR2%20face%20database.pdf" target="_blank">[pdf]</a></td><td>Behavior research methods</td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>7</td><td>3</td><td>4</td><td>0</td><td>7</td><td>0</td></tr><tr><td>ad01687649d95cd5b56d7399a9603c4b8e2217d7</td><td>mrp_drone</td><td>MRP Drone</td><td><a href="papers/ad01687649d95cd5b56d7399a9603c4b8e2217d7.html" target="_blank">Investigating Open-World Person Re-identification Using a Drone</a></td><td><a href="https://pdfs.semanticscholar.org/ad01/687649d95cd5b56d7399a9603c4b8e2217d7.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>7</td><td>3</td><td>4</td><td>1</td><td>5</td><td>2</td></tr><tr><td>a7fe834a0af614ce6b50dc093132b031dd9a856b</td><td>pku_reid</td><td>PKU-Reid</td><td><a href="papers/a7fe834a0af614ce6b50dc093132b031dd9a856b.html" target="_blank">Orientation Driven Bag of Appearances for Person Re-identification</a></td><td><a href="https://arxiv.org/pdf/1605.02464.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>7</td><td>3</td><td>4</td><td>0</td><td>4</td><td>4</td></tr><tr><td>17b46e2dad927836c689d6787ddb3387c6159ece</td><td>geofaces</td><td>GeoFaces</td><td><a href="papers/17b46e2dad927836c689d6787ddb3387c6159ece.html" target="_blank">GeoFaceExplorer: exploring the geo-dependence of facial attributes</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>2</td><td>2</td><td>0</td><td>0</td><td>1</td><td>1</td></tr><tr><td>e58dd160a76349d46f881bd6ddbc2921f08d1050</td><td>gfw</td><td>Grouping Face in the Wild</td><td><a href="papers/e58dd160a76349d46f881bd6ddbc2921f08d1050.html" target="_blank">Merge or Not? Learning to Group Faces via Imitation Learning</a></td><td><a href="https://arxiv.org/pdf/1707.03986.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>2</td><td>2</td><td>0</td><td>0</td><td>2</td><td>0</td></tr><tr><td>4eab317b5ac436a949849ed286baa3de2a541eef</td><td>laofiw</td><td>LAOFIW</td><td><a href="papers/4eab317b5ac436a949849ed286baa3de2a541eef.html" target="_blank">Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings</a></td><td><a href="https://arxiv.org/pdf/1809.02169.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>2</td><td>2</td><td>0</td><td>0</td><td>2</td><td>0</td></tr><tr><td>f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f</td><td>pku_reid</td><td>PKU-Reid</td><td><a href="papers/f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f.html" target="_blank">Swiss-System Based Cascade Ranking for Gait-Based Person Re-Identification</a></td><td><a href="https://pdfs.semanticscholar.org/f6c8/d5e35d7e4d60a0104f233ac1a3ab757da53f.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>4</td><td>2</td><td>2</td><td>0</td><td>1</td><td>2</td></tr><tr><td>3531332efe19be21e7401ba1f04570a142617236</td><td>ufdd</td><td>UFDD</td><td><a href="papers/3531332efe19be21e7401ba1f04570a142617236.html" target="_blank">Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results</a></td><td><a href="https://arxiv.org/pdf/1804.10275.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>4</td><td>2</td><td>2</td><td>1</td><td>4</td><td>0</td></tr><tr><td>8875ae233bc074f5cd6c4ebba447b536a7e847a5</td><td>voxceleb2</td><td>VoxCeleb2</td><td><a href="papers/8875ae233bc074f5cd6c4ebba447b536a7e847a5.html" target="_blank">VoxCeleb2: Deep Speaker Recognition.</a></td><td><a href="https://pdfs.semanticscholar.org/8875/ae233bc074f5cd6c4ebba447b536a7e847a5.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>6%</td><td>34</td><td>2</td><td>31</td><td>1</td><td>3</td><td>2</td></tr><tr><td>4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b38461</td><td>3dddb_unconstrained</td><td>3D Dynamic</td><td><a href="papers/4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b38461.html" target="_blank">A 3D Dynamic Database for Unconstrained Face Recognition</a></td><td><a href="https://pdfs.semanticscholar.org/4d4b/b462c9f1d4e4ab1e4aa6a75cc0bc71b38461.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>2</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td></tr><tr><td>a40f9bfd3c45658ee8da70e1f2dfbe1f0c744d43</td><td>4dfab</td><td>4DFAB</td><td><a href="papers/a40f9bfd3c45658ee8da70e1f2dfbe1f0c744d43.html" target="_blank">4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications</a></td><td><a href="https://arxiv.org/pdf/1712.01443.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>25%</td><td>4</td><td>1</td><td>3</td><td>0</td><td>2</td><td>2</td></tr><tr><td>65355cbb581a219bd7461d48b3afd115263ea760</td><td>complex_activities</td><td>Ongoing Complex Activities</td><td><a href="papers/65355cbb581a219bd7461d48b3afd115263ea760.html" target="_blank">Recognition of ongoing complex activities by sequence prediction over a hierarchical label space</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Winter Conference on Applications of Computer Vision (WACV)</td><td></td><td></td><td></td><td></td><td></td><td>33%</td><td>3</td><td>1</td><td>2</td><td>0</td><td>3</td><td>0</td></tr><tr><td>55c40cbcf49a0225e72d911d762c27bb1c2d14aa</td><td>ifad</td><td>IFAD</td><td><a href="papers/55c40cbcf49a0225e72d911d762c27bb1c2d14aa.html" target="_blank">Indian Face Age Database: A Database for Face Recognition with Age Variation</a></td><td><a href="https://pdfs.semanticscholar.org/025e/4cf3fd3fdeced91e9373b56ee14af7ca432c.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>2</td><td>1</td><td>1</td><td>0</td><td>2</td><td>0</td></tr><tr><td>c06b13d0ec3f5c43e2782cd22542588e233733c3</td><td>nova_emotions</td><td>Novaemötions Dataset</td><td><a href="papers/c06b13d0ec3f5c43e2782cd22542588e233733c3.html" target="_blank">Crowdsourcing facial expressions for affective-interaction</a></td><td><span class="gray">[pdf]</a></td><td>Computer Vision and Image Understanding</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><td>2306b2a8fba28539306052764a77a0d0f5d1236a</td><td>qmul_surv_face</td><td>QMUL-SurvFace</td><td><a href="papers/2306b2a8fba28539306052764a77a0d0f5d1236a.html" target="_blank">Surveillance Face Recognition Challenge</a></td><td><a href="https://arxiv.org/pdf/1804.09691.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td>edu</td><td>Queen Mary University of London</td><td>United Kingdom</td><td>51.52472720</td><td>-0.03931035</td><td>100%</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><td>d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9</td><td>stair_actions</td><td>STAIR Action</td><td><a href="papers/d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9.html" target="_blank">STAIR Actions: A Video Dataset of Everyday Home Actions</a></td><td><a href="https://arxiv.org/pdf/1804.04326.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><td>5ad4e9f947c1653c247d418f05dad758a3f9277b</td><td>wlfdb</td><td>WLFDB</td><td><a href="papers/5ad4e9f947c1653c247d418f05dad758a3f9277b.html" target="_blank">WLFDB : Weakly Labeled Face Databases</a></td><td><a href="https://pdfs.semanticscholar.org/5ad4/e9f947c1653c247d418f05dad758a3f9277b.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><td>7b92d1e53cc87f7a4256695de590098a2f30261e</td><td>appa_real</td><td>APPA-REAL</td><td><a href="papers/7b92d1e53cc87f7a4256695de590098a2f30261e.html" target="_blank">From Apparent to Real Age: Gender, Age, Ethnic, Makeup, and Expression Bias Analysis in Real Age Estimation</a></td><td><span class="gray">[pdf]</a></td><td>2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>1dc35905a1deff8bc74688f2d7e2f48fd2273275</td><td>caltech_pedestrians</td><td>Caltech Pedestrians</td><td><a href="papers/1dc35905a1deff8bc74688f2d7e2f48fd2273275.html" target="_blank">Pedestrian detection: A benchmark</a></td><td><span class="gray">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>15e1af79939dbf90790b03d8aa02477783fb1d0f</td><td>duke_mtmc</td><td>Duke MTMC</td><td><a href="papers/15e1af79939dbf90790b03d8aa02477783fb1d0f.html" target="_blank">Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro</a></td><td><a href="https://arxiv.org/pdf/1701.07717.pdf" target="_blank">[pdf]</a></td><td>2017 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>72a155c987816ae81c858fddbd6beab656d86220</td><td>europersons</td><td>EuroCity Persons</td><td><a href="papers/72a155c987816ae81c858fddbd6beab656d86220.html" target="_blank">The EuroCity Persons Dataset: A Novel Benchmark for Object Detection</a></td><td><a href="https://arxiv.org/pdf/1805.07193.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>2</td><td>0</td><td>2</td><td>0</td><td>2</td><td>0</td></tr><tr><td>670637d0303a863c1548d5b19f705860a23e285c</td><td>face_tracer</td><td>FaceTracer</td><td><a href="papers/670637d0303a863c1548d5b19f705860a23e285c.html" target="_blank">Face swapping: automatically replacing faces in photographs</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>12ad3b5bbbf407f8e54ea692c07633d1a867c566</td><td>graz</td><td>Graz Pedestrian</td><td><a href="papers/12ad3b5bbbf407f8e54ea692c07633d1a867c566.html" target="_blank">Object recognition using segmentation for feature detection</a></td><td><span class="gray">[pdf]</a></td><td>Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.</td><td>edu</td><td>Inst. of Comput. Sci., Univ. of Leoben, Austria</td><td>Austria</td><td>47.38473720</td><td>15.09302010</td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>bd88bb2e4f351352d88ee7375af834360e223498</td><td>hda_plus</td><td>HDA+</td><td><a href="papers/bd88bb2e4f351352d88ee7375af834360e223498.html" target="_blank">HDA dataset-DRAFT 1 A Multi-camera video data set for research on High-Definition surveillance</a></td><td><a href="https://pdfs.semanticscholar.org/bd88/bb2e4f351352d88ee7375af834360e223498.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>2</td><td>0</td><td>2</td><td>0</td><td>1</td><td>2</td></tr><tr><td>0ab7cff2ccda7269b73ff6efd9d37e1318f7db25</td><td>ibm_dif</td><td>IBM Diversity in Faces</td><td><a href="papers/0ab7cff2ccda7269b73ff6efd9d37e1318f7db25.html" target="_blank">Facial Coding Scheme Reference 1 Craniofacial Distances</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>066d71fcd997033dce4ca58df924397dfe0b5fd1</td><td>ifdb</td><td>IFDB</td><td><a href="papers/066d71fcd997033dce4ca58df924397dfe0b5fd1.html" target="_blank">Iranian Face Database and Evaluation with a New Detection Algorithm</a></td><td><a href="https://pdfs.semanticscholar.org/066d/71fcd997033dce4ca58df924397dfe0b5fd1.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>21d9d0deed16f0ad62a4865e9acf0686f4f15492</td><td>images_of_groups</td><td>Images of Groups</td><td><a href="papers/21d9d0deed16f0ad62a4865e9acf0686f4f15492.html" target="_blank">Understanding images of groups of people</a></td><td><span class="gray">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>Carnegie Mellon University</td><td>United States</td><td>40.44416190</td><td>-79.94272826</td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>140438a77a771a8fb656b39a78ff488066eb6b50</td><td>lfpw</td><td>LFPW</td><td><a href="papers/140438a77a771a8fb656b39a78ff488066eb6b50.html" target="_blank">Localizing Parts of Faces Using a Consensus of Exemplars</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>079a0a3bf5200994e1f972b1b9197bf2f90e87d4</td><td>mit_cbcl</td><td>MIT CBCL</td><td><a href="papers/079a0a3bf5200994e1f972b1b9197bf2f90e87d4.html" target="_blank">Component-Based Face Recognition with 3D Morphable Models</a></td><td><span class="gray">[pdf]</a></td><td>2004 Conference on Computer Vision and Pattern Recognition Workshop</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>2fda164863a06a92d3a910b96eef927269aeb730</td><td>names_and_faces</td><td>News Dataset</td><td><a href="papers/2fda164863a06a92d3a910b96eef927269aeb730.html" target="_blank">Names and faces in the news</a></td><td><span class="gray">[pdf]</a></td><td>Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>d3200d49a19a4a4e4e9745ee39649b65d80c834b</td><td>scut_head</td><td>SCUT HEAD</td><td><a href="papers/d3200d49a19a4a4e4e9745ee39649b65d80c834b.html" target="_blank">Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture</a></td><td><a href="https://arxiv.org/pdf/1803.09256.pdf" target="_blank">[pdf]</a></td><td>2018 24th International Conference on Pattern Recognition (ICPR)</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>8990cdce3f917dad622e43e033db686b354d057c</td><td>tiny_faces</td><td>TinyFace</td><td><a href="papers/8990cdce3f917dad622e43e033db686b354d057c.html" target="_blank">Low-Resolution Face Recognition</a></td><td><a href="https://arxiv.org/pdf/1811.08965.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>6ad5a38df8dd4cdddd74f31996ce096d41219f72</td><td>tud_brussels</td><td>TUD-Brussels</td><td><a href="papers/6ad5a38df8dd4cdddd74f31996ce096d41219f72.html" target="_blank">Multi-cue onboard pedestrian detection</a></td><td><span class="gray">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>6ad5a38df8dd4cdddd74f31996ce096d41219f72</td><td>tud_motionpairs</td><td>TUD-Motionparis</td><td><a href="papers/6ad5a38df8dd4cdddd74f31996ce096d41219f72.html" target="_blank">Multi-cue onboard pedestrian detection</a></td><td><span class="gray">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>01959ef569f74c286956024866c1d107099199f7</td><td>vqa</td><td>VQA</td><td><a href="papers/01959ef569f74c286956024866c1d107099199f7.html" target="_blank">VQA: Visual Question Answering</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>9b9bf5e623cb8af7407d2d2d857bc3f1b531c182</td><td>who_goes_there</td><td>WGT</td><td><a href="papers/9b9bf5e623cb8af7407d2d2d857bc3f1b531c182.html" target="_blank">Who goes there?: approaches to mapping facial appearance diversity</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td>edu</td><td>University of Kentucky</td><td>United States</td><td>38.03337420</td><td>-84.50177580</td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>36bccfb2ad847096bc76777e544f305813cd8f5b</td><td>wildtrack</td><td>WildTrack</td><td><a href="papers/36bccfb2ad847096bc76777e544f305813cd8f5b.html" target="_blank">WILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection</a></td><td><a href="http://openaccess.thecvf.com/content_cvpr_2018/Supplemental/1562-supp.pdf" target="_blank">[pdf]</a></td><td>2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr></table></body></html>
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+<!doctype html><html><head><meta charset='utf-8'><title>Coverage</title><link rel='stylesheet' href='reports.css'></head><body><h2>Coverage</h2><table border='1' cellpadding='3' cellspacing='3'><th>Paper ID</th><th>Megapixels Key</th><th>Megapixels Name</th><th>Report Link</th><th>PDF Link</th><th>Journal</th><th>Type</th><th>Address</th><th>Country</th><th>Lat</th><th>Lng</th><th>Coverage</th><th>Total Citations</th><th>Geocoded Citations</th><th>Unknown Citations</th><th>Empty Citations</th><th>With PDF</th><th>With DOI</th><tr><td>0e986f51fe45b00633de9fd0c94d082d2be51406</td><td>afw</td><td>AFW</td><td><a href="papers/0e986f51fe45b00633de9fd0c94d082d2be51406.html" target="_blank">Face detection, pose estimation, and landmark localization in the wild</a></td><td><span class="gray">[pdf]</a></td><td>2012 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>999</td><td>709</td><td>290</td><td>35</td><td>576</td><td>422</td></tr><tr><td>162ea969d1929ed180cc6de9f0bf116993ff6e06</td><td>vgg_faces</td><td>VGG Face</td><td><a href="papers/162ea969d1929ed180cc6de9f0bf116993ff6e06.html" target="_blank">Deep Face Recognition</a></td><td><a href="https://pdfs.semanticscholar.org/f372/ab9b3270d4e4f6a0258c83c2736c3a5c0454.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>999</td><td>646</td><td>353</td><td>48</td><td>558</td><td>429</td></tr><tr><td>b5f2846a506fc417e7da43f6a7679146d99c5e96</td><td>ucf_101</td><td>UCF101</td><td><a href="papers/b5f2846a506fc417e7da43f6a7679146d99c5e96.html" target="_blank">UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild</a></td><td><a href="https://arxiv.org/pdf/1212.0402.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>64%</td><td>999</td><td>643</td><td>356</td><td>56</td><td>628</td><td>362</td></tr><tr><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td><td>lfw</td><td>LFW</td><td><a href="papers/370b5757a5379b15e30d619e4d3fb9e8e13f3256.html" target="_blank">Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments</a></td><td><a href="https://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>63%</td><td>999</td><td>632</td><td>367</td><td>59</td><td>598</td><td>382</td></tr><tr><td>5e0f8c355a37a5a89351c02f174e7a5ddcb98683</td><td>coco</td><td>COCO</td><td><a href="papers/5e0f8c355a37a5a89351c02f174e7a5ddcb98683.html" target="_blank">Microsoft COCO: Common Objects in Context</a></td><td><a href="https://arxiv.org/pdf/1405.0312.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>999</td><td>608</td><td>391</td><td>25</td><td>722</td><td>259</td></tr><tr><td>4d9a02d080636e9666c4d1cc438b9893391ec6c7</td><td>cohn_kanade_plus</td><td>CK+</td><td><a href="papers/4d9a02d080636e9666c4d1cc438b9893391ec6c7.html" target="_blank">The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression</a></td><td><span class="gray">[pdf]</a></td><td>2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops</td><td>edu</td><td>University of Pittsburgh</td><td>United States</td><td>40.44415295</td><td>-79.96243993</td><td>61%</td><td>999</td><td>607</td><td>392</td><td>57</td><td>470</td><td>518</td></tr><tr><td>0ee1916a0cb2dc7d3add086b5f1092c3d4beb38a</td><td>voc</td><td>VOC</td><td><a href="papers/0ee1916a0cb2dc7d3add086b5f1092c3d4beb38a.html" target="_blank">The Pascal Visual Object Classes (VOC) Challenge</a></td><td><a href="http://eprints.pascal-network.org/archive/00006187/01/PascalVOC_IJCV2009.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td>company</td><td>Microsoft</td><td>United States</td><td>47.64233180</td><td>-122.13693020</td><td>61%</td><td>999</td><td>607</td><td>391</td><td>28</td><td>557</td><td>422</td></tr><tr><td>026e3363b7f76b51cc711886597a44d5f1fd1de2</td><td>kitti</td><td>KITTI</td><td><a href="papers/026e3363b7f76b51cc711886597a44d5f1fd1de2.html" target="_blank">Vision meets robotics: The KITTI dataset</a></td><td><a href="https://pdfs.semanticscholar.org/026e/3363b7f76b51cc711886597a44d5f1fd1de2.pdf" target="_blank">[pdf]</a></td><td>I. J. Robotics Res.</td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>999</td><td>602</td><td>397</td><td>36</td><td>553</td><td>462</td></tr><tr><td>f72f6a45ee240cc99296a287ff725aaa7e7ebb35</td><td>caltech_pedestrians</td><td>Caltech Pedestrians</td><td><a href="papers/f72f6a45ee240cc99296a287ff725aaa7e7ebb35.html" target="_blank">Pedestrian Detection: An Evaluation of the State of the Art</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td>edu</td><td>California Institute of Technology</td><td>United States</td><td>34.13710185</td><td>-118.12527487</td><td>60%</td><td>999</td><td>597</td><td>402</td><td>70</td><td>527</td><td>466</td></tr><tr><td>759a3b3821d9f0e08e0b0a62c8b693230afc3f8d</td><td>pubfig</td><td>PubFig</td><td><a href="papers/759a3b3821d9f0e08e0b0a62c8b693230afc3f8d.html" target="_blank">Attribute and simile classifiers for face verification</a></td><td><span class="gray">[pdf]</a></td><td>2009 IEEE 12th International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>64%</td><td>914</td><td>585</td><td>329</td><td>47</td><td>586</td><td>316</td></tr><tr><td>6d96f946aaabc734af7fe3fc4454cf8547fcd5ed</td><td>ar_facedb</td><td>AR Face</td><td><a href="papers/6d96f946aaabc734af7fe3fc4454cf8547fcd5ed.html" target="_blank">The AR face database</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>58%</td><td>999</td><td>579</td><td>420</td><td>58</td><td>458</td><td>530</td></tr><tr><td>31b58ced31f22eab10bd3ee2d9174e7c14c27c01</td><td>tiny_images</td><td>Tiny Images</td><td><a href="papers/31b58ced31f22eab10bd3ee2d9174e7c14c27c01.html" target="_blank">80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>999</td><td>574</td><td>425</td><td>89</td><td>644</td><td>337</td></tr><tr><td>10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5</td><td>inria_person</td><td>INRIA Pedestrian</td><td><a href="papers/10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5.html" target="_blank">Histograms of oriented gradients for human detection</a></td><td><span class="gray">[pdf]</a></td><td>2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)</td><td>edu</td><td>INRIA Rhone-Alps, Montbonnot, France</td><td>France</td><td>45.21788600</td><td>5.80736900</td><td>57%</td><td>999</td><td>572</td><td>427</td><td>42</td><td>419</td><td>509</td></tr><tr><td>18ae7c9a4bbc832b8b14bc4122070d7939f5e00e</td><td>frgc</td><td>FRGC</td><td><a href="papers/18ae7c9a4bbc832b8b14bc4122070d7939f5e00e.html" target="_blank">Overview of the face recognition grand challenge</a></td><td><span class="gray">[pdf]</a></td><td>2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)</td><td>edu</td><td>NIST</td><td>United States</td><td>39.14004000</td><td>-77.21850600</td><td>57%</td><td>999</td><td>568</td><td>430</td><td>85</td><td>549</td><td>442</td></tr><tr><td>18c72175ddbb7d5956d180b65a96005c100f6014</td><td>yale_faces</td><td>YaleFaces</td><td><a href="papers/18c72175ddbb7d5956d180b65a96005c100f6014.html" target="_blank">From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose</a></td><td><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.147.1487&rep=rep1&type=pdf" target="_blank">[pdf]</a></td><td>IEEE Trans. Pattern Anal. Mach. Intell.</td><td></td><td></td><td></td><td></td><td></td><td>56%</td><td>999</td><td>561</td><td>438</td><td>66</td><td>498</td><td>462</td></tr><tr><td>2ad0ee93d029e790ebb50574f403a09854b65b7e</td><td>yale_faces</td><td>YaleFaces</td><td><a href="papers/2ad0ee93d029e790ebb50574f403a09854b65b7e.html" target="_blank">Acquiring linear subspaces for face recognition under variable lighting</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>999</td><td>554</td><td>445</td><td>94</td><td>495</td><td>491</td></tr><tr><td>23fc83c8cfff14a16df7ca497661264fc54ed746</td><td>cohn_kanade</td><td>CK</td><td><a href="papers/23fc83c8cfff14a16df7ca497661264fc54ed746.html" target="_blank">Comprehensive Database for Facial Expression Analysis</a></td><td><a href="https://pdfs.semanticscholar.org/23fc/83c8cfff14a16df7ca497661264fc54ed746.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>999</td><td>553</td><td>446</td><td>69</td><td>540</td><td>439</td></tr><tr><td>b62628ac06bbac998a3ab825324a41a11bc3a988</td><td>m2vtsdb_extended</td><td>xm2vtsdb</td><td><a href="papers/b62628ac06bbac998a3ab825324a41a11bc3a988.html" target="_blank">XM2VTSDB : The extended M2VTS database</a></td><td><a href="https://pdfs.semanticscholar.org/b626/28ac06bbac998a3ab825324a41a11bc3a988.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>864</td><td>539</td><td>325</td><td>37</td><td>493</td><td>404</td></tr><tr><td>6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4</td><td>celeba</td><td>CelebA</td><td><a href="papers/6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4.html" target="_blank">Deep Learning Face Attributes in the Wild</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td>edu</td><td>Chinese University of Hong Kong</td><td>China</td><td>22.41626320</td><td>114.21093180</td><td>57%</td><td>919</td><td>526</td><td>392</td><td>61</td><td>694</td><td>201</td></tr><tr><td>dc8b25e35a3acb812beb499844734081722319b4</td><td>feret</td><td>FERET</td><td><a href="papers/dc8b25e35a3acb812beb499844734081722319b4.html" target="_blank">The FERET database and evaluation procedure for face-recognition algorithms</a></td><td><a href="http://biometrics.nist.gov/cs_links/face/frvt/feret/FERET_Database_evaluation_procedure.pdf" target="_blank">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>999</td><td>521</td><td>478</td><td>103</td><td>591</td><td>421</td></tr><tr><td>45c31cde87258414f33412b3b12fc5bec7cb3ba9</td><td>jaffe</td><td>JAFFE</td><td><a href="papers/45c31cde87258414f33412b3b12fc5bec7cb3ba9.html" target="_blank">Coding Facial Expressions with Gabor Wavelets</a></td><td><a href="http://physics.lbl.gov/patrecog/images/Facerecog_gabor.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>899</td><td>508</td><td>391</td><td>51</td><td>431</td><td>451</td></tr><tr><td>55206f0b5f57ce17358999145506cd01e570358c</td><td>orl</td><td>ORL</td><td><a href="papers/55206f0b5f57ce17358999145506cd01e570358c.html" target="_blank">Parameterisation of a stochastic model for human face identification</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>999</td><td>501</td><td>498</td><td>94</td><td>543</td><td>427</td></tr><tr><td>4d423acc78273b75134e2afd1777ba6d3a398973</td><td>cmu_pie</td><td>CMU PIE</td><td><a href="papers/4d423acc78273b75134e2afd1777ba6d3a398973.html" target="_blank">The CMU Pose, Illumination, and Expression (PIE) Database</a></td><td><a href="http://www.comp.nus.edu.sg/~tsim/piedb.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>59%</td><td>760</td><td>449</td><td>310</td><td>49</td><td>404</td><td>345</td></tr><tr><td>4d423acc78273b75134e2afd1777ba6d3a398973</td><td>multi_pie</td><td>MULTIPIE</td><td><a href="papers/4d423acc78273b75134e2afd1777ba6d3a398973.html" target="_blank">The CMU Pose, Illumination, and Expression (PIE) Database</a></td><td><a href="http://www.comp.nus.edu.sg/~tsim/piedb.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>59%</td><td>760</td><td>449</td><td>310</td><td>49</td><td>404</td><td>345</td></tr><tr><td>2830fb5282de23d7784b4b4bc37065d27839a412</td><td>h3d</td><td>H3D</td><td><a href="papers/2830fb5282de23d7784b4b4bc37065d27839a412.html" target="_blank">Poselets: Body part detectors trained using 3D human pose annotations</a></td><td><span class="gray">[pdf]</a></td><td>2009 IEEE 12th International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>58%</td><td>716</td><td>415</td><td>301</td><td>59</td><td>492</td><td>222</td></tr><tr><td>6273b3491e94ea4dd1ce42b791d77bdc96ee73a8</td><td>viper</td><td>VIPeR</td><td><a href="papers/6273b3491e94ea4dd1ce42b791d77bdc96ee73a8.html" target="_blank">Evaluating Appearance Models for Recognition, Reacquisition, and Tracking</a></td><td><a href="https://pdfs.semanticscholar.org/7847/b1fbccadb780b655e72c66d3f9e93ddb880c.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>edu</td><td>University of California, Santa Cruz</td><td>United States</td><td>36.99158470</td><td>-122.05827710</td><td>66%</td><td>624</td><td>414</td><td>210</td><td>33</td><td>342</td><td>276</td></tr><tr><td>6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3</td><td>cuhk_campus_03</td><td>CUHK03 Campus</td><td><a href="papers/6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3.html" target="_blank">DeepReID: Deep Filter Pairing Neural Network for Person Re-identification</a></td><td><span class="gray">[pdf]</a></td><td>2014 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>73%</td><td>568</td><td>412</td><td>156</td><td>19</td><td>320</td><td>235</td></tr><tr><td>2258e01865367018ed6f4262c880df85b94959f8</td><td>mot</td><td>MOT</td><td><a href="papers/2258e01865367018ed6f4262c880df85b94959f8.html" target="_blank">Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics</a></td><td><a href="https://cvhci.anthropomatik.kit.edu/images/stories/msmmi/papers/eurasip2008.pdf" target="_blank">[pdf]</a></td><td>EURASIP J. Image and Video Processing</td><td></td><td></td><td></td><td></td><td></td><td>58%</td><td>632</td><td>366</td><td>264</td><td>44</td><td>358</td><td>264</td></tr><tr><td>4308bd8c28e37e2ed9a3fcfe74d5436cce34b410</td><td>market_1501</td><td>Market 1501</td><td><a href="papers/4308bd8c28e37e2ed9a3fcfe74d5436cce34b410.html" target="_blank">Scalable Person Re-identification: A Benchmark</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td>company</td><td>Microsoft</td><td>United States</td><td>47.64233180</td><td>-122.13693020</td><td>77%</td><td>460</td><td>355</td><td>105</td><td>9</td><td>263</td><td>185</td></tr><tr><td>853bd61bc48a431b9b1c7cab10c603830c488e39</td><td>casia_webface</td><td>CASIA Webface</td><td><a href="papers/853bd61bc48a431b9b1c7cab10c603830c488e39.html" target="_blank">Learning Face Representation from Scratch</a></td><td><a href="https://arxiv.org/pdf/1411.7923.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td>edu</td><td>Chinese Academy of Sciences</td><td>China</td><td>40.00447950</td><td>116.37023800</td><td>71%</td><td>476</td><td>339</td><td>137</td><td>19</td><td>290</td><td>182</td></tr><tr><td>560e0e58d0059259ddf86fcec1fa7975dee6a868</td><td>youtube_faces</td><td>YouTubeFaces</td><td><a href="papers/560e0e58d0059259ddf86fcec1fa7975dee6a868.html" target="_blank">Face recognition in unconstrained videos with matched background similarity</a></td><td><span class="gray">[pdf]</a></td><td>CVPR 2011</td><td>edu</td><td>Tel Aviv University</td><td>Israel</td><td>32.11198890</td><td>34.80459702</td><td>66%</td><td>509</td><td>338</td><td>170</td><td>23</td><td>294</td><td>216</td></tr><tr><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td><td>tud_campus</td><td>TUD-Campus</td><td><a href="papers/3316521a5527c7700af8ae6aef32a79a8b83672c.html" target="_blank">People-tracking-by-detection and people-detection-by-tracking</a></td><td><span class="gray">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>59%</td><td>545</td><td>324</td><td>220</td><td>37</td><td>330</td><td>218</td></tr><tr><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td><td>tud_crossing</td><td>TUD-Crossing</td><td><a href="papers/3316521a5527c7700af8ae6aef32a79a8b83672c.html" target="_blank">People-tracking-by-detection and people-detection-by-tracking</a></td><td><span class="gray">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>59%</td><td>545</td><td>324</td><td>220</td><td>37</td><td>330</td><td>218</td></tr><tr><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td><td>tud_pedestrian</td><td>TUD-Pedestrian</td><td><a href="papers/3316521a5527c7700af8ae6aef32a79a8b83672c.html" target="_blank">People-tracking-by-detection and people-detection-by-tracking</a></td><td><span class="gray">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>59%</td><td>545</td><td>324</td><td>220</td><td>37</td><td>330</td><td>218</td></tr><tr><td>cc589c499dcf323fe4a143bbef0074c3e31f9b60</td><td>bu_3dfe</td><td>BU-3DFE</td><td><a href="papers/cc589c499dcf323fe4a143bbef0074c3e31f9b60.html" target="_blank">A 3D facial expression database for facial behavior research</a></td><td><span class="gray">[pdf]</a></td><td>7th International Conference on Automatic Face and Gesture Recognition (FGR06)</td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>588</td><td>316</td><td>271</td><td>44</td><td>306</td><td>282</td></tr><tr><td>95f12d27c3b4914e0668a268360948bce92f7db3</td><td>helen</td><td>Helen</td><td><a href="papers/95f12d27c3b4914e0668a268360948bce92f7db3.html" target="_blank">Interactive Facial Feature Localization</a></td><td><a href="https://pdfs.semanticscholar.org/95f1/2d27c3b4914e0668a268360948bce92f7db3.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>company</td><td>Adobe</td><td>United States</td><td>37.33077030</td><td>-121.89409510</td><td>85%</td><td>352</td><td>298</td><td>54</td><td>8</td><td>212</td><td>146</td></tr><tr><td>4053e3423fb70ad9140ca89351df49675197196a</td><td>bio_id</td><td>BioID Face</td><td><a href="papers/4053e3423fb70ad9140ca89351df49675197196a.html" target="_blank">Robust Face Detection Using the Hausdorff Distance</a></td><td><a href="https://pdfs.semanticscholar.org/4053/e3423fb70ad9140ca89351df49675197196a.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>511</td><td>289</td><td>222</td><td>49</td><td>329</td><td>182</td></tr><tr><td>8a3c5507237957d013a0fe0f082cab7f757af6ee</td><td>mafl</td><td>MAFL</td><td><a href="papers/8a3c5507237957d013a0fe0f082cab7f757af6ee.html" target="_blank">Facial Landmark Detection by Deep Multi-task Learning</a></td><td><a href="https://pdfs.semanticscholar.org/fcd7/1c18192928a2e0b264edd4d919ab2f8f652a.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>70%</td><td>407</td><td>283</td><td>124</td><td>16</td><td>252</td><td>153</td></tr><tr><td>8a3c5507237957d013a0fe0f082cab7f757af6ee</td><td>mtfl</td><td>MTFL</td><td><a href="papers/8a3c5507237957d013a0fe0f082cab7f757af6ee.html" target="_blank">Facial Landmark Detection by Deep Multi-task Learning</a></td><td><a href="https://pdfs.semanticscholar.org/fcd7/1c18192928a2e0b264edd4d919ab2f8f652a.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>70%</td><td>407</td><td>283</td><td>124</td><td>16</td><td>252</td><td>153</td></tr><tr><td>16c7c31a7553d99f1837fc6e88e77b5ccbb346b8</td><td>prid</td><td>PRID</td><td><a href="papers/16c7c31a7553d99f1837fc6e88e77b5ccbb346b8.html" target="_blank">Person Re-identification by Descriptive and Discriminative Classification</a></td><td><a href="https://pdfs.semanticscholar.org/4c1b/f0592be3e535faf256c95e27982db9b3d3d3.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>68%</td><td>386</td><td>263</td><td>123</td><td>23</td><td>204</td><td>180</td></tr><tr><td>9055b155cbabdce3b98e16e5ac9c0edf00f9552f</td><td>morph</td><td>MORPH Commercial</td><td><a href="papers/9055b155cbabdce3b98e16e5ac9c0edf00f9552f.html" target="_blank">MORPH: a longitudinal image database of normal adult age-progression</a></td><td><span class="gray">[pdf]</a></td><td>7th International Conference on Automatic Face and Gesture Recognition (FGR06)</td><td>edu</td><td>North Carolina University</td><td>United States</td><td>34.22398690</td><td>-77.87013250</td><td>59%</td><td>437</td><td>258</td><td>178</td><td>22</td><td>228</td><td>203</td></tr><tr><td>9055b155cbabdce3b98e16e5ac9c0edf00f9552f</td><td>morph_nc</td><td>MORPH Non-Commercial</td><td><a href="papers/9055b155cbabdce3b98e16e5ac9c0edf00f9552f.html" target="_blank">MORPH: a longitudinal image database of normal adult age-progression</a></td><td><span class="gray">[pdf]</a></td><td>7th International Conference on Automatic Face and Gesture Recognition (FGR06)</td><td>edu</td><td>North Carolina University</td><td>United States</td><td>34.22398690</td><td>-77.87013250</td><td>59%</td><td>437</td><td>258</td><td>178</td><td>22</td><td>228</td><td>203</td></tr><tr><td>044d9a8c61383312cdafbcc44b9d00d650b21c70</td><td>fiw_300</td><td>300-W</td><td><a href="papers/044d9a8c61383312cdafbcc44b9d00d650b21c70.html" target="_blank">300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision Workshops</td><td></td><td></td><td></td><td></td><td></td><td>79%</td><td>323</td><td>255</td><td>68</td><td>15</td><td>208</td><td>120</td></tr><tr><td>2485c98aa44131d1a2f7d1355b1e372f2bb148ad</td><td>cas_peal</td><td>CAS-PEAL</td><td><a href="papers/2485c98aa44131d1a2f7d1355b1e372f2bb148ad.html" target="_blank">The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans</td><td></td><td></td><td></td><td></td><td></td><td>59%</td><td>429</td><td>254</td><td>175</td><td>38</td><td>198</td><td>234</td></tr><tr><td>3325860c0c82a93b2eac654f5324dd6a776f609e</td><td>mpii_human_pose</td><td>MPII Human Pose</td><td><a href="papers/3325860c0c82a93b2eac654f5324dd6a776f609e.html" target="_blank">2D Human Pose Estimation: New Benchmark and State of the Art Analysis</a></td><td><span class="gray">[pdf]</a></td><td>2014 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>387</td><td>251</td><td>136</td><td>21</td><td>291</td><td>96</td></tr><tr><td>2a75f34663a60ab1b04a0049ed1d14335129e908</td><td>mmi_facial_expression</td><td>MMI Facial Expression Dataset</td><td><a href="papers/2a75f34663a60ab1b04a0049ed1d14335129e908.html" target="_blank">Web-based database for facial expression analysis</a></td><td><span class="gray">[pdf]</a></td><td>2005 IEEE International Conference on Multimedia and Expo</td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>464</td><td>250</td><td>214</td><td>45</td><td>282</td><td>188</td></tr><tr><td>75da1df4ed319926c544eefe17ec8d720feef8c0</td><td>fddb</td><td>FDDB</td><td><a href="papers/75da1df4ed319926c544eefe17ec8d720feef8c0.html" target="_blank">FDDB: A benchmark for face detection in unconstrained settings</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>380</td><td>247</td><td>133</td><td>16</td><td>202</td><td>164</td></tr><tr><td>3765df816dc5a061bc261e190acc8bdd9d47bec0</td><td>rafd</td><td>RaFD</td><td><a href="papers/3765df816dc5a061bc261e190acc8bdd9d47bec0.html" target="_blank">Presentation and validation of the Radboud Faces Database</a></td><td><a href="https://pdfs.semanticscholar.org/3765/df816dc5a061bc261e190acc8bdd9d47bec0.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>48%</td><td>487</td><td>234</td><td>253</td><td>39</td><td>342</td><td>144</td></tr><tr><td>2724ba85ec4a66de18da33925e537f3902f21249</td><td>cofw</td><td>COFW</td><td><a href="papers/2724ba85ec4a66de18da33925e537f3902f21249.html" target="_blank">Robust Face Landmark Estimation under Occlusion</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision</td><td>edu</td><td>California Institute of Technology</td><td>United States</td><td>34.13710185</td><td>-118.12527487</td><td>72%</td><td>325</td><td>233</td><td>92</td><td>12</td><td>194</td><td>133</td></tr><tr><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td><td>stickmen_buffy</td><td>Buffy Stickmen</td><td><a href="papers/6dd0597f8513dc100cd0bc1b493768cde45098a9.html" target="_blank">Learning to parse images of articulated bodies</a></td><td><a href="http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2006_899.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>369</td><td>227</td><td>141</td><td>32</td><td>237</td><td>131</td></tr><tr><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td><td>stickmen_pascal</td><td>Stickmen PASCAL</td><td><a href="papers/6dd0597f8513dc100cd0bc1b493768cde45098a9.html" target="_blank">Learning to parse images of articulated bodies</a></td><td><a href="http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2006_899.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>369</td><td>227</td><td>141</td><td>32</td><td>237</td><td>131</td></tr><tr><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td><td>stickmen_pascal</td><td>Stickmen PASCAL</td><td><a href="papers/6dd0597f8513dc100cd0bc1b493768cde45098a9.html" target="_blank">Learning to parse images of articulated bodies</a></td><td><a href="http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2006_899.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>369</td><td>227</td><td>141</td><td>32</td><td>237</td><td>131</td></tr><tr><td>9361b784e73e9238d5cefbea5ac40d35d1e3103f</td><td>oxford_town_centre</td><td>TownCentre</td><td><a href="papers/9361b784e73e9238d5cefbea5ac40d35d1e3103f.html" target="_blank">Stable multi-target tracking in real-time surveillance video</a></td><td><span class="gray">[pdf]</a></td><td>CVPR 2011</td><td>edu</td><td>University of Oxford</td><td>United Kingdom</td><td>51.75345380</td><td>-1.25400997</td><td>67%</td><td>328</td><td>221</td><td>107</td><td>13</td><td>186</td><td>140</td></tr><tr><td>a74251efa970b92925b89eeef50a5e37d9281ad0</td><td>aflw</td><td>AFLW</td><td><a href="papers/a74251efa970b92925b89eeef50a5e37d9281ad0.html" target="_blank">Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization</a></td><td><span class="gray">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td>edu</td><td>TU Graz</td><td>Austria</td><td>47.07071400</td><td>15.43950400</td><td>69%</td><td>318</td><td>218</td><td>100</td><td>27</td><td>211</td><td>107</td></tr><tr><td>13f06b08f371ba8b5d31c3e288b4deb61335b462</td><td>eth_andreas_ess</td><td>ETHZ Pedestrian</td><td><a href="papers/13f06b08f371ba8b5d31c3e288b4deb61335b462.html" target="_blank">Depth and Appearance for Mobile Scene Analysis</a></td><td><span class="gray">[pdf]</a></td><td>2007 IEEE 11th International Conference on Computer Vision</td><td>edu</td><td>ETH Zurich</td><td>Switzerland</td><td>47.37631300</td><td>8.54766990</td><td>63%</td><td>324</td><td>203</td><td>121</td><td>26</td><td>193</td><td>127</td></tr><tr><td>5981e6479c3fd4e31644db35d236bfb84ae46514</td><td>mot</td><td>MOT</td><td><a href="papers/5981e6479c3fd4e31644db35d236bfb84ae46514.html" target="_blank">Learning to associate: HybridBoosted multi-target tracker for crowded scene</a></td><td><span class="gray">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>University of Southern California</td><td>United States</td><td>34.02241490</td><td>-118.28634407</td><td>61%</td><td>326</td><td>200</td><td>125</td><td>22</td><td>190</td><td>137</td></tr><tr><td>2acf7e58f0a526b957be2099c10aab693f795973</td><td>bosphorus</td><td>The Bosphorus</td><td><a href="papers/2acf7e58f0a526b957be2099c10aab693f795973.html" target="_blank">Bosphorus Database for 3D Face Analysis</a></td><td><a href="https://pdfs.semanticscholar.org/4254/fbba3846008f50671edc9cf70b99d7304543.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>56%</td><td>352</td><td>198</td><td>154</td><td>17</td><td>162</td><td>188</td></tr><tr><td>639937b3a1b8bded3f7e9a40e85bd3770016cf3c</td><td>bfm</td><td>BFM</td><td><a href="papers/639937b3a1b8bded3f7e9a40e85bd3770016cf3c.html" target="_blank">A 3D Face Model for Pose and Illumination Invariant Face Recognition</a></td><td><span class="gray">[pdf]</a></td><td>2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>343</td><td>194</td><td>149</td><td>23</td><td>223</td><td>114</td></tr><tr><td>44484d2866f222bbb9b6b0870890f9eea1ffb2d0</td><td>cuhk_campus_03</td><td>CUHK03 Campus</td><td><a href="papers/44484d2866f222bbb9b6b0870890f9eea1ffb2d0.html" target="_blank">Human Reidentification with Transferred Metric Learning</a></td><td><a href="http://www.ee.cuhk.edu.hk/~xgwang/papers/liZWaccv12.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>69%</td><td>280</td><td>194</td><td>86</td><td>9</td><td>139</td><td>137</td></tr><tr><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td><td>leeds_sports_pose</td><td>Leeds Sports Pose</td><td><a href="papers/4b1d23d17476fcf78f4cbadf69fb130b1aa627c0.html" target="_blank">Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</a></td><td><a href="http://www.bmva.org/bmvc/2010/conference/paper12/abstract12.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>285</td><td>185</td><td>100</td><td>13</td><td>197</td><td>93</td></tr><tr><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td><td>stickmen_buffy</td><td>Buffy Stickmen</td><td><a href="papers/4b1d23d17476fcf78f4cbadf69fb130b1aa627c0.html" target="_blank">Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</a></td><td><a href="http://www.bmva.org/bmvc/2010/conference/paper12/abstract12.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>285</td><td>185</td><td>100</td><td>13</td><td>197</td><td>93</td></tr><tr><td>436f798d1a4e54e5947c1e7d7375c31b2bdb4064</td><td>tud_multiview</td><td>TUD-Multiview</td><td><a href="papers/436f798d1a4e54e5947c1e7d7375c31b2bdb4064.html" target="_blank">Monocular 3D pose estimation and tracking by detection</a></td><td><span class="gray">[pdf]</a></td><td>2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>TU Darmstadt</td><td>Germany</td><td>49.87482770</td><td>8.65632810</td><td>59%</td><td>311</td><td>185</td><td>126</td><td>34</td><td>208</td><td>105</td></tr><tr><td>436f798d1a4e54e5947c1e7d7375c31b2bdb4064</td><td>tud_stadtmitte</td><td>TUD-Stadtmitte</td><td><a href="papers/436f798d1a4e54e5947c1e7d7375c31b2bdb4064.html" target="_blank">Monocular 3D pose estimation and tracking by detection</a></td><td><span class="gray">[pdf]</a></td><td>2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>TU Darmstadt</td><td>Germany</td><td>49.87482770</td><td>8.65632810</td><td>59%</td><td>311</td><td>185</td><td>126</td><td>34</td><td>208</td><td>105</td></tr><tr><td>010f0f4929e6a6644fb01f0e43820f91d0fad292</td><td>yfcc_100m</td><td>YFCC100M</td><td><a href="papers/010f0f4929e6a6644fb01f0e43820f91d0fad292.html" target="_blank">YFCC100M: the new data in multimedia research</a></td><td><span class="gray">[pdf]</a></td><td>Commun. ACM</td><td>edu</td><td>Carnegie Mellon University</td><td>United States</td><td>40.44416190</td><td>-79.94272826</td><td>64%</td><td>274</td><td>176</td><td>98</td><td>23</td><td>172</td><td>100</td></tr><tr><td>38b55d95189c5e69cf4ab45098a48fba407609b4</td><td>cuhk_campus_03</td><td>CUHK03 Campus</td><td><a href="papers/38b55d95189c5e69cf4ab45098a48fba407609b4.html" target="_blank">Locally Aligned Feature Transforms across Views</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>64%</td><td>258</td><td>164</td><td>94</td><td>15</td><td>136</td><td>117</td></tr><tr><td>833fa04463d90aab4a9fe2870d480f0b40df446e</td><td>sun_attributes</td><td>SUN</td><td><a href="papers/833fa04463d90aab4a9fe2870d480f0b40df446e.html" target="_blank">SUN attribute database: Discovering, annotating, and recognizing scene attributes</a></td><td><span class="gray">[pdf]</a></td><td>2012 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>Brown University</td><td>United States</td><td>41.82686820</td><td>-71.40123146</td><td>60%</td><td>264</td><td>159</td><td>105</td><td>27</td><td>206</td><td>56</td></tr><tr><td>1be498d4bbc30c3bfd0029114c784bc2114d67c0</td><td>adience</td><td>Adience</td><td><a href="papers/1be498d4bbc30c3bfd0029114c784bc2114d67c0.html" target="_blank">Age and Gender Estimation of Unfiltered Faces</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Information Forensics and Security</td><td>edu</td><td>Open University of Israel</td><td>Israel</td><td>32.77824165</td><td>34.99565673</td><td>87%</td><td>179</td><td>156</td><td>23</td><td>1</td><td>98</td><td>80</td></tr><tr><td>140c95e53c619eac594d70f6369f518adfea12ef</td><td>ijb_a</td><td>IJB-A</td><td><a href="papers/140c95e53c619eac594d70f6369f518adfea12ef.html" target="_blank">Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>66%</td><td>237</td><td>156</td><td>81</td><td>14</td><td>159</td><td>76</td></tr><tr><td>2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9</td><td>graz</td><td>Graz Pedestrian</td><td><a href="papers/2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9.html" target="_blank">Generic object recognition with boosting</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td>edu</td><td>TU Graz</td><td>Austria</td><td>47.07071400</td><td>15.43950400</td><td>53%</td><td>293</td><td>155</td><td>138</td><td>16</td><td>195</td><td>97</td></tr><tr><td>6204776d31359d129a582057c2d788a14f8aadeb</td><td>youtube_celebrities</td><td>YouTube Celebrities</td><td><a href="papers/6204776d31359d129a582057c2d788a14f8aadeb.html" target="_blank">Face tracking and recognition with visual constraints in real-world videos</a></td><td><span class="gray">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>Rutgers University</td><td>United States</td><td>40.47913175</td><td>-74.43168868</td><td>57%</td><td>267</td><td>151</td><td>115</td><td>11</td><td>125</td><td>121</td></tr><tr><td>013909077ad843eb6df7a3e8e290cfd5575999d2</td><td>fiw_300</td><td>300-W</td><td><a href="papers/013909077ad843eb6df7a3e8e290cfd5575999d2.html" target="_blank">A Semi-automatic Methodology for Facial Landmark Annotation</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops</td><td></td><td></td><td></td><td></td><td></td><td>80%</td><td>184</td><td>148</td><td>36</td><td>8</td><td>120</td><td>67</td></tr><tr><td>27a2fad58dd8727e280f97036e0d2bc55ef5424c</td><td>duke_mtmc</td><td>Duke MTMC</td><td><a href="papers/27a2fad58dd8727e280f97036e0d2bc55ef5424c.html" target="_blank">Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking</a></td><td><a href="https://arxiv.org/pdf/1609.01775.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Duke University</td><td>United States</td><td>35.99905220</td><td>-78.92906290</td><td>85%</td><td>169</td><td>144</td><td>25</td><td>3</td><td>113</td><td>54</td></tr><tr><td>4c170a0dcc8de75587dae21ca508dab2f9343974</td><td>face_tracer</td><td>FaceTracer</td><td><a href="papers/4c170a0dcc8de75587dae21ca508dab2f9343974.html" target="_blank">FaceTracer: A Search Engine for Large Collections of Images with Faces</a></td><td><a href="https://pdfs.semanticscholar.org/73a8/1d311eedac8dea3ca24dc15b6990fa4a725e.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>64%</td><td>225</td><td>144</td><td>81</td><td>17</td><td>146</td><td>77</td></tr><tr><td>27a2fad58dd8727e280f97036e0d2bc55ef5424c</td><td>mot</td><td>MOT</td><td><a href="papers/27a2fad58dd8727e280f97036e0d2bc55ef5424c.html" target="_blank">Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking</a></td><td><a href="https://arxiv.org/pdf/1609.01775.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Duke University</td><td>United States</td><td>35.99905220</td><td>-78.92906290</td><td>85%</td><td>169</td><td>144</td><td>25</td><td>3</td><td>113</td><td>54</td></tr><tr><td>98bb029afe2a1239c3fdab517323066f0957b81b</td><td>ilids_mcts_vid</td><td>iLIDS-VID</td><td><a href="papers/98bb029afe2a1239c3fdab517323066f0957b81b.html" target="_blank">Person Re-identification by Video Ranking</a></td><td><a href="https://pdfs.semanticscholar.org/98bb/029afe2a1239c3fdab517323066f0957b81b.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>68%</td><td>209</td><td>143</td><td>66</td><td>8</td><td>111</td><td>97</td></tr><tr><td>98bb029afe2a1239c3fdab517323066f0957b81b</td><td>sdu_vid</td><td>SDU-VID</td><td><a href="papers/98bb029afe2a1239c3fdab517323066f0957b81b.html" target="_blank">Person Re-identification by Video Ranking</a></td><td><a href="https://pdfs.semanticscholar.org/98bb/029afe2a1239c3fdab517323066f0957b81b.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>68%</td><td>209</td><td>143</td><td>66</td><td>8</td><td>111</td><td>97</td></tr><tr><td>e8de844fefd54541b71c9823416daa238be65546</td><td>visual_phrases</td><td>Phrasal Recognition</td><td><a href="papers/e8de844fefd54541b71c9823416daa238be65546.html" target="_blank">Recognition using visual phrases</a></td><td><span class="gray">[pdf]</a></td><td>CVPR 2011</td><td>edu</td><td>University of Illinois, Urbana-Champaign</td><td>United States</td><td>40.11116745</td><td>-88.22587665</td><td>58%</td><td>246</td><td>143</td><td>103</td><td>17</td><td>170</td><td>68</td></tr><tr><td>291265db88023e92bb8c8e6390438e5da148e8f5</td><td>msceleb</td><td>MsCeleb</td><td><a href="papers/291265db88023e92bb8c8e6390438e5da148e8f5.html" target="_blank">MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition</a></td><td><a href="https://arxiv.org/pdf/1607.08221.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>company</td><td>Microsoft</td><td>United States</td><td>47.64233180</td><td>-122.13693020</td><td>78%</td><td>180</td><td>141</td><td>39</td><td>8</td><td>120</td><td>59</td></tr><tr><td>7808937b46acad36e43c30ae4e9f3fd57462853d</td><td>bpad</td><td>BPAD</td><td><a href="papers/7808937b46acad36e43c30ae4e9f3fd57462853d.html" target="_blank">Describing people: A poselet-based approach to attribute classification</a></td><td><span class="gray">[pdf]</a></td><td>2011 International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>230</td><td>140</td><td>90</td><td>14</td><td>163</td><td>66</td></tr><tr><td>46a01565e6afe7c074affb752e7069ee3bf2e4ef</td><td>sdu_vid</td><td>SDU-VID</td><td><a href="papers/46a01565e6afe7c074affb752e7069ee3bf2e4ef.html" target="_blank">Local Descriptors Encoded by Fisher Vectors for Person Re-identification</a></td><td><a href="https://pdfs.semanticscholar.org/a105/f1ef67b4b02da38eadce8ffb4e13aa301a93.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>67%</td><td>197</td><td>132</td><td>65</td><td>15</td><td>108</td><td>88</td></tr><tr><td>0c91808994a250d7be332400a534a9291ca3b60e</td><td>graz</td><td>Graz Pedestrian</td><td><a href="papers/0c91808994a250d7be332400a534a9291ca3b60e.html" target="_blank">Weak Hypotheses and Boosting for Generic Object Detection and Recognition</a></td><td><a href="https://pdfs.semanticscholar.org/0c91/808994a250d7be332400a534a9291ca3b60e.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>56%</td><td>236</td><td>131</td><td>105</td><td>17</td><td>161</td><td>77</td></tr><tr><td>35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62</td><td>coco_qa</td><td>COCO QA</td><td><a href="papers/35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62.html" target="_blank">Exploring Models and Data for Image Question Answering</a></td><td><a href="https://arxiv.org/pdf/1505.02074.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>206</td><td>126</td><td>80</td><td>11</td><td>162</td><td>39</td></tr><tr><td>570f37ed63142312e6ccdf00ecc376341ec72b9f</td><td>stanford_drone</td><td>Stanford Drone</td><td><a href="papers/570f37ed63142312e6ccdf00ecc376341ec72b9f.html" target="_blank">Social LSTM: Human Trajectory Prediction in Crowded Spaces</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>56%</td><td>224</td><td>125</td><td>99</td><td>3</td><td>140</td><td>81</td></tr><tr><td>4e4746094bf60ee83e40d8597a6191e463b57f76</td><td>leeds_sports_pose_extended</td><td>Leeds Sports Pose Extended</td><td><a href="papers/4e4746094bf60ee83e40d8597a6191e463b57f76.html" target="_blank">Learning effective human pose estimation from inaccurate annotation</a></td><td><span class="gray">[pdf]</a></td><td>CVPR 2011</td><td>edu</td><td>University of Leeds</td><td>United Kingdom</td><td>53.80387185</td><td>-1.55245712</td><td>70%</td><td>169</td><td>118</td><td>51</td><td>8</td><td>108</td><td>65</td></tr><tr><td>52d7eb0fbc3522434c13cc247549f74bb9609c5d</td><td>wider_face</td><td>WIDER FACE</td><td><a href="papers/52d7eb0fbc3522434c13cc247549f74bb9609c5d.html" target="_blank">WIDER FACE: A Face Detection Benchmark</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>Chinese University of Hong Kong</td><td>China</td><td>22.41626320</td><td>114.21093180</td><td>66%</td><td>178</td><td>118</td><td>60</td><td>11</td><td>112</td><td>66</td></tr><tr><td>22ad2c8c0f4d6aa4328b38d894b814ec22579761</td><td>gallagher</td><td>Gallagher</td><td><a href="papers/22ad2c8c0f4d6aa4328b38d894b814ec22579761.html" target="_blank">Clothing cosegmentation for recognizing people</a></td><td><span class="gray">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>Carnegie Mellon University</td><td>United States</td><td>40.44416190</td><td>-79.94272826</td><td>65%</td><td>178</td><td>116</td><td>62</td><td>7</td><td>100</td><td>86</td></tr><tr><td>b1f4423c227fa37b9680787be38857069247a307</td><td>afew_va</td><td>AFEW-VA</td><td><a href="papers/b1f4423c227fa37b9680787be38857069247a307.html" target="_blank">Collecting Large, Richly Annotated Facial-Expression Databases from Movies</a></td><td><span class="gray">[pdf]</a></td><td>IEEE MultiMedia</td><td>edu</td><td>Australian National University</td><td>Australia</td><td>-35.27769990</td><td>149.11852700</td><td>64%</td><td>181</td><td>115</td><td>66</td><td>8</td><td>87</td><td>97</td></tr><tr><td>c0387e788a52f10bf35d4d50659cfa515d89fbec</td><td>mars</td><td>MARS</td><td><a href="papers/c0387e788a52f10bf35d4d50659cfa515d89fbec.html" target="_blank">MARS: A Video Benchmark for Large-Scale Person Re-Identification</a></td><td><a href="https://pdfs.semanticscholar.org/c038/7e788a52f10bf35d4d50659cfa515d89fbec.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>68%</td><td>168</td><td>115</td><td>53</td><td>4</td><td>97</td><td>69</td></tr><tr><td>18010284894ed0edcca74e5bf768ee2e15ef7841</td><td>deep_fashion</td><td>DeepFashion</td><td><a href="papers/18010284894ed0edcca74e5bf768ee2e15ef7841.html" target="_blank">DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>64%</td><td>176</td><td>112</td><td>64</td><td>2</td><td>113</td><td>62</td></tr><tr><td>133f01aec1534604d184d56de866a4bd531dac87</td><td>lfw</td><td>LFW</td><td><a href="papers/133f01aec1534604d184d56de866a4bd531dac87.html" target="_blank">Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>183</td><td>109</td><td>74</td><td>13</td><td>103</td><td>77</td></tr><tr><td>0df0d1adea39a5bef318b74faa37de7f3e00b452</td><td>mpii_gaze</td><td>MPIIGaze</td><td><a href="papers/0df0d1adea39a5bef318b74faa37de7f3e00b452.html" target="_blank">Appearance-based gaze estimation in the wild</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>73%</td><td>149</td><td>109</td><td>40</td><td>3</td><td>94</td><td>54</td></tr><tr><td>96e0cfcd81cdeb8282e29ef9ec9962b125f379b0</td><td>megaface</td><td>MegaFace</td><td><a href="papers/96e0cfcd81cdeb8282e29ef9ec9962b125f379b0.html" target="_blank">The MegaFace Benchmark: 1 Million Faces for Recognition at Scale</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>76%</td><td>139</td><td>106</td><td>33</td><td>5</td><td>100</td><td>37</td></tr><tr><td>10195a163ab6348eef37213a46f60a3d87f289c5</td><td>imdb_wiki</td><td>IMDB-Wiki</td><td><a href="papers/10195a163ab6348eef37213a46f60a3d87f289c5.html" target="_blank">Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks</a></td><td><a href="http://www.vision.ee.ethz.ch/en/publications/papers/articles/eth_biwi_01299.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td>edu</td><td>ETH Zurich</td><td>Switzerland</td><td>47.37631300</td><td>8.54766990</td><td>72%</td><td>145</td><td>105</td><td>40</td><td>9</td><td>93</td><td>51</td></tr><tr><td>56ffa7d906b08d02d6d5a12c7377a57e24ef3391</td><td>unbc_shoulder_pain</td><td>UNBC-McMaster Pain</td><td><a href="papers/56ffa7d906b08d02d6d5a12c7377a57e24ef3391.html" target="_blank">Painful data: The UNBC-McMaster shoulder pain expression archive database</a></td><td><span class="gray">[pdf]</a></td><td>Face and Gesture 2011</td><td>edu</td><td>Carnegie Mellon University</td><td>United States</td><td>40.44416190</td><td>-79.94272826</td><td>54%</td><td>189</td><td>103</td><td>86</td><td>21</td><td>108</td><td>78</td></tr><tr><td>29a705a5fa76641e0d8963f1fdd67ee4c0d92d3d</td><td>scface</td><td>SCface</td><td><a href="papers/29a705a5fa76641e0d8963f1fdd67ee4c0d92d3d.html" target="_blank">SCface – surveillance cameras face database</a></td><td><a href="http://scface.org/SCface%20-%20Surveillance%20Cameras%20Face%20Database.pdf" target="_blank">[pdf]</a></td><td>Multimedia Tools and Applications</td><td></td><td></td><td></td><td></td><td></td><td>56%</td><td>179</td><td>101</td><td>78</td><td>15</td><td>88</td><td>89</td></tr><tr><td>5a5f0287484f0d480fed1ce585dbf729586f0edc</td><td>disfa</td><td>DISFA</td><td><a href="papers/5a5f0287484f0d480fed1ce585dbf729586f0edc.html" target="_blank">DISFA: A Spontaneous Facial Action Intensity Database</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Affective Computing</td><td>edu</td><td>University of Denver</td><td>United States</td><td>39.67665410</td><td>-104.96220300</td><td>54%</td><td>184</td><td>100</td><td>84</td><td>17</td><td>96</td><td>89</td></tr><tr><td>32c801cb7fbeb742edfd94cccfca4934baec71da</td><td>ucf_crowd</td><td>UCF-CC-50</td><td><a href="papers/32c801cb7fbeb742edfd94cccfca4934baec71da.html" target="_blank">Multi-source Multi-scale Counting in Extremely Dense Crowd Images</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>68%</td><td>148</td><td>100</td><td>48</td><td>5</td><td>80</td><td>65</td></tr><tr><td>1aad2da473888cb7ebc1bfaa15bfa0f1502ce005</td><td>jpl_pose</td><td>JPL-Interaction dataset</td><td><a href="papers/1aad2da473888cb7ebc1bfaa15bfa0f1502ce005.html" target="_blank">First-Person Activity Recognition: What Are They Doing to Me?</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>67%</td><td>148</td><td>99</td><td>49</td><td>7</td><td>105</td><td>43</td></tr><tr><td>3b5b6d19d4733ab606c39c69a889f9e67967f151</td><td>qmul_grid</td><td>GRID</td><td><a href="papers/3b5b6d19d4733ab606c39c69a889f9e67967f151.html" target="_blank">Multi-camera activity correlation analysis</a></td><td><span class="gray">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>Queen Mary University of London</td><td>United Kingdom</td><td>51.52472720</td><td>-0.03931035</td><td>69%</td><td>142</td><td>98</td><td>44</td><td>7</td><td>77</td><td>64</td></tr><tr><td>8355d095d3534ef511a9af68a3b2893339e3f96b</td><td>imdb_wiki</td><td>IMDB-Wiki</td><td><a href="papers/8355d095d3534ef511a9af68a3b2893339e3f96b.html" target="_blank">DEX: Deep EXpectation of Apparent Age from a Single Image</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision Workshop (ICCVW)</td><td></td><td></td><td></td><td></td><td></td><td>78%</td><td>122</td><td>95</td><td>27</td><td>4</td><td>75</td><td>48</td></tr><tr><td>4f93cd09785c6e77bf4bc5a788e079df524c8d21</td><td>soton</td><td>SOTON HiD</td><td><a href="papers/4f93cd09785c6e77bf4bc5a788e079df524c8d21.html" target="_blank">On a Large Sequence-Based Human Gait Database</a></td><td><a href="https://pdfs.semanticscholar.org/4f93/cd09785c6e77bf4bc5a788e079df524c8d21.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>63%</td><td>150</td><td>95</td><td>55</td><td>17</td><td>103</td><td>51</td></tr><tr><td>8b56e33f33e582f3e473dba573a16b598ed9bcdc</td><td>fei</td><td>FEI</td><td><a href="papers/8b56e33f33e582f3e473dba573a16b598ed9bcdc.html" target="_blank">A new ranking method for principal components analysis and its application to face image analysis</a></td><td><span class="gray">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>169</td><td>93</td><td>76</td><td>6</td><td>69</td><td>102</td></tr><tr><td>e4754afaa15b1b53e70743880484b8d0736990ff</td><td>fiw_300</td><td>300-W</td><td><a href="papers/e4754afaa15b1b53e70743880484b8d0736990ff.html" target="_blank">300 Faces In-The-Wild Challenge: database and results</a></td><td><a href="http://ibug.doc.ic.ac.uk/media/uploads/documents/1-s2.0-s0262885616000147-main.pdf" target="_blank">[pdf]</a></td><td>Image Vision Comput.</td><td>edu</td><td>Imperial College London</td><td>United Kingdom</td><td>51.49887085</td><td>-0.17560797</td><td>71%</td><td>129</td><td>92</td><td>37</td><td>6</td><td>74</td><td>55</td></tr><tr><td>066000d44d6691d27202896691f08b27117918b9</td><td>psu</td><td>PSU</td><td><a href="papers/066000d44d6691d27202896691f08b27117918b9.html" target="_blank">Vision-Based Analysis of Small Groups in Pedestrian Crowds</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>168</td><td>90</td><td>78</td><td>10</td><td>85</td><td>79</td></tr><tr><td>0d3bb75852098b25d90f31d2f48fd0cb4944702b</td><td>face_scrub</td><td>FaceScrub</td><td><a href="papers/0d3bb75852098b25d90f31d2f48fd0cb4944702b.html" target="_blank">A data-driven approach to cleaning large face datasets</a></td><td><span class="gray">[pdf]</a></td><td>2014 IEEE International Conference on Image Processing (ICIP)</td><td></td><td></td><td></td><td></td><td></td><td>64%</td><td>138</td><td>89</td><td>49</td><td>1</td><td>95</td><td>41</td></tr><tr><td>2d3482dcff69c7417c7b933f22de606a0e8e42d4</td><td>lfw</td><td>LFW</td><td><a href="papers/2d3482dcff69c7417c7b933f22de606a0e8e42d4.html" target="_blank">Labeled Faces in the Wild : Updates and New Reporting Procedures</a></td><td><a href="https://pdfs.semanticscholar.org/2d34/82dcff69c7417c7b933f22de606a0e8e42d4.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>edu</td><td>University of Massachusetts</td><td>United States</td><td>42.38897850</td><td>-72.52869870</td><td>69%</td><td>123</td><td>85</td><td>38</td><td>3</td><td>71</td><td>51</td></tr><tr><td>0486214fb58ee9a04edfe7d6a74c6d0f661a7668</td><td>chokepoint</td><td>ChokePoint</td><td><a href="papers/0486214fb58ee9a04edfe7d6a74c6d0f661a7668.html" target="_blank">Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition</a></td><td><span class="gray">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>138</td><td>83</td><td>55</td><td>6</td><td>76</td><td>63</td></tr><tr><td>2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e</td><td>3dpes</td><td>3DPeS</td><td><a href="papers/2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e.html" target="_blank">3DPeS: 3D people dataset for surveillance and forensics</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>133</td><td>82</td><td>51</td><td>9</td><td>73</td><td>58</td></tr><tr><td>5a4df9bef1872865f0b619ac3aacc97f49e4a035</td><td>cuhk_train_station</td><td>CUHK Train Station Dataset</td><td><a href="papers/5a4df9bef1872865f0b619ac3aacc97f49e4a035.html" target="_blank">Understanding collective crowd behaviors: Learning a Mixture model of Dynamic pedestrian-Agents</a></td><td><span class="gray">[pdf]</a></td><td>2012 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>Chinese University of Hong Kong</td><td>China</td><td>22.41626320</td><td>114.21093180</td><td>58%</td><td>141</td><td>82</td><td>59</td><td>5</td><td>60</td><td>75</td></tr><tr><td>b91f54e1581fbbf60392364323d00a0cd43e493c</td><td>bp4d_spontanous</td><td>BP4D-Spontanous</td><td><a href="papers/b91f54e1581fbbf60392364323d00a0cd43e493c.html" target="_blank">A high-resolution spontaneous 3D dynamic facial expression database</a></td><td><span class="gray">[pdf]</a></td><td>2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)</td><td>edu</td><td>SUNY Binghamton</td><td>United States</td><td>42.08779975</td><td>-75.97066066</td><td>52%</td><td>154</td><td>80</td><td>74</td><td>6</td><td>80</td><td>75</td></tr><tr><td>a0fd85b3400c7b3e11122f44dc5870ae2de9009a</td><td>mafl</td><td>MAFL</td><td><a href="papers/a0fd85b3400c7b3e11122f44dc5870ae2de9009a.html" target="_blank">Learning Deep Representation for Face Alignment with Auxiliary Attributes</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>108</td><td>77</td><td>31</td><td>7</td><td>66</td><td>44</td></tr><tr><td>a0fd85b3400c7b3e11122f44dc5870ae2de9009a</td><td>mtfl</td><td>MTFL</td><td><a href="papers/a0fd85b3400c7b3e11122f44dc5870ae2de9009a.html" target="_blank">Learning Deep Representation for Face Alignment with Auxiliary Attributes</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>108</td><td>77</td><td>31</td><td>7</td><td>66</td><td>44</td></tr><tr><td>7f23a4bb0c777dd72cca7665a5f370ac7980217e</td><td>duke_mtmc</td><td>Duke MTMC</td><td><a href="papers/7f23a4bb0c777dd72cca7665a5f370ac7980217e.html" target="_blank">Improving Person Re-identification by Attribute and Identity Learning</a></td><td><a href="https://arxiv.org/pdf/1703.07220.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>84%</td><td>87</td><td>73</td><td>14</td><td>0</td><td>43</td><td>42</td></tr><tr><td>7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22</td><td>lfw</td><td>LFW</td><td><a href="papers/7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22.html" target="_blank">Labeled Faces in the Wild: A Survey</a></td><td><a href="https://pdfs.semanticscholar.org/7de6/e81d775e9cd7becbfd1bd685f4e2a5eebb22.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Stevens Institute of Technology</td><td>United States</td><td>40.74225200</td><td>-74.02709490</td><td>64%</td><td>109</td><td>70</td><td>39</td><td>7</td><td>66</td><td>43</td></tr><tr><td>66e6f08873325d37e0ec20a4769ce881e04e964e</td><td>sun_attributes</td><td>SUN</td><td><a href="papers/66e6f08873325d37e0ec20a4769ce881e04e964e.html" target="_blank">The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding</a></td><td><a href="http://www.cc.gatech.edu/~hays/papers/attribute_ijcv.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>116</td><td>70</td><td>46</td><td>14</td><td>84</td><td>31</td></tr><tr><td>2a4bbee0b4cf52d5aadbbc662164f7efba89566c</td><td>peta</td><td>PETA</td><td><a href="papers/2a4bbee0b4cf52d5aadbbc662164f7efba89566c.html" target="_blank">Pedestrian Attribute Recognition At Far Distance</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>75%</td><td>88</td><td>66</td><td>22</td><td>1</td><td>50</td><td>36</td></tr><tr><td>70c59dc3470ae867016f6ab0e008ac8ba03774a1</td><td>vgg_faces2</td><td>VGG Face2</td><td><a href="papers/70c59dc3470ae867016f6ab0e008ac8ba03774a1.html" target="_blank">VGGFace2: A Dataset for Recognising Faces across Pose and Age</a></td><td><a href="https://arxiv.org/pdf/1710.08092.pdf" target="_blank">[pdf]</a></td><td>2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)</td><td></td><td></td><td></td><td></td><td></td><td>80%</td><td>83</td><td>66</td><td>17</td><td>3</td><td>61</td><td>20</td></tr><tr><td>3394168ff0719b03ff65bcea35336a76b21fe5e4</td><td>penn_fudan</td><td>Penn Fudan</td><td><a href="papers/3394168ff0719b03ff65bcea35336a76b21fe5e4.html" target="_blank">Object Detection Combining Recognition and Segmentation</a></td><td><a href="https://pdfs.semanticscholar.org/3394/168ff0719b03ff65bcea35336a76b21fe5e4.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>105</td><td>64</td><td>41</td><td>9</td><td>58</td><td>43</td></tr><tr><td>3b4ec8af470948a72a6ed37a9fd226719a874ebc</td><td>sdu_vid</td><td>SDU-VID</td><td><a href="papers/3b4ec8af470948a72a6ed37a9fd226719a874ebc.html" target="_blank">A Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>66%</td><td>95</td><td>63</td><td>32</td><td>6</td><td>50</td><td>45</td></tr><tr><td>04c2cda00e5536f4b1508cbd80041e9552880e67</td><td>hipsterwars</td><td>Hipsterwars</td><td><a href="papers/04c2cda00e5536f4b1508cbd80041e9552880e67.html" target="_blank">Hipster Wars: Discovering Elements of Fashion Styles</a></td><td><a href="https://pdfs.semanticscholar.org/04c2/cda00e5536f4b1508cbd80041e9552880e67.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>64%</td><td>95</td><td>61</td><td>34</td><td>4</td><td>59</td><td>35</td></tr><tr><td>06f02199690961ba52997cde1527e714d2b3bf8f</td><td>columbia_gaze</td><td>Columbia Gaze</td><td><a href="papers/06f02199690961ba52997cde1527e714d2b3bf8f.html" target="_blank">Gaze locking: passive eye contact detection for human-object interaction</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Columbia University</td><td>United States</td><td>40.84198360</td><td>-73.94368971</td><td>76%</td><td>79</td><td>60</td><td>19</td><td>0</td><td>49</td><td>34</td></tr><tr><td>4df3143922bcdf7db78eb91e6b5359d6ada004d2</td><td>cfd</td><td>CFD</td><td><a href="papers/4df3143922bcdf7db78eb91e6b5359d6ada004d2.html" target="_blank">The Chicago face database: A free stimulus set of faces and norming data.</a></td><td><a href="https://pdfs.semanticscholar.org/4df3/143922bcdf7db78eb91e6b5359d6ada004d2.pdf" target="_blank">[pdf]</a></td><td>Behavior research methods</td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>99</td><td>59</td><td>40</td><td>1</td><td>73</td><td>21</td></tr><tr><td>0c4a139bb87c6743c7905b29a3cfec27a5130652</td><td>feret</td><td>FERET</td><td><a href="papers/0c4a139bb87c6743c7905b29a3cfec27a5130652.html" target="_blank">The FERET Verification Testing Protocol for Face Recognition Algorithms</a></td><td><a href="https://pdfs.semanticscholar.org/8d2a/1c768fce6f71584dd993fb97e7b6419aaf60.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>edu</td><td>City University of New York</td><td>United States</td><td>40.87228250</td><td>-73.89489171</td><td>51%</td><td>115</td><td>59</td><td>56</td><td>8</td><td>75</td><td>37</td></tr><tr><td>08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7</td><td>kin_face</td><td>UB KinFace</td><td><a href="papers/08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7.html" target="_blank">Understanding Kin Relationships in a Photo</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Multimedia</td><td></td><td></td><td></td><td></td><td></td><td>63%</td><td>94</td><td>59</td><td>35</td><td>1</td><td>33</td><td>61</td></tr><tr><td>2ce2560cf59db59ce313bbeb004e8ce55c5ce928</td><td>texas_3dfrd</td><td>Texas 3DFRD</td><td><a href="papers/2ce2560cf59db59ce313bbeb004e8ce55c5ce928.html" target="_blank">Anthropometric 3D Face Recognition</a></td><td><a href="http://live.ece.utexas.edu/publications/2010/sg_ijcv_june10.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>63%</td><td>91</td><td>57</td><td>34</td><td>5</td><td>60</td><td>31</td></tr><tr><td>5194cbd51f9769ab25260446b4fa17204752e799</td><td>violent_flows</td><td>Violent Flows</td><td><a href="papers/5194cbd51f9769ab25260446b4fa17204752e799.html" target="_blank">Violent flows: Real-time detection of violent crowd behavior</a></td><td><span class="gray">[pdf]</a></td><td>2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops</td><td>edu</td><td>Open University of Israel</td><td>Israel</td><td>32.77824165</td><td>34.99565673</td><td>65%</td><td>88</td><td>57</td><td>31</td><td>6</td><td>45</td><td>44</td></tr><tr><td>3cd40bfa1ff193a96bde0207e5140a399476466c</td><td>tvhi</td><td>TVHI</td><td><a href="papers/3cd40bfa1ff193a96bde0207e5140a399476466c.html" target="_blank">High Five: Recognising human interactions in TV shows</a></td><td><a href="https://pdfs.semanticscholar.org/3cd4/0bfa1ff193a96bde0207e5140a399476466c.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>98</td><td>56</td><td>42</td><td>10</td><td>66</td><td>28</td></tr><tr><td>2160788824c4c29ffe213b2cbeb3f52972d73f37</td><td>3d_rma</td><td>3D-RMA</td><td><a href="papers/2160788824c4c29ffe213b2cbeb3f52972d73f37.html" target="_blank">Automatic 3D face authentication</a></td><td><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.31.9190&rep=rep1&type=pdf" target="_blank">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>100</td><td>54</td><td>46</td><td>8</td><td>63</td><td>36</td></tr><tr><td>ae0aee03d946efffdc7af2362a42d3750e7dd48a</td><td>put_face</td><td>Put Face</td><td><a href="papers/ae0aee03d946efffdc7af2362a42d3750e7dd48a.html" target="_blank">The put face database</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>99</td><td>54</td><td>45</td><td>5</td><td>55</td><td>48</td></tr><tr><td>2edb87494278ad11641b6cf7a3f8996de12b8e14</td><td>qmul_grid</td><td>GRID</td><td><a href="papers/2edb87494278ad11641b6cf7a3f8996de12b8e14.html" target="_blank">Time-Delayed Correlation Analysis for Multi-Camera Activity Understanding</a></td><td><a href="http://www.eecs.qmul.ac.uk/~ccloy/files/ijcv_2010.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td>edu</td><td>Queen Mary University of London</td><td>United Kingdom</td><td>51.52472720</td><td>-0.03931035</td><td>63%</td><td>84</td><td>53</td><td>31</td><td>4</td><td>51</td><td>33</td></tr><tr><td>0b84f07af44f964817675ad961def8a51406dd2e</td><td>prw</td><td>PRW</td><td><a href="papers/0b84f07af44f964817675ad961def8a51406dd2e.html" target="_blank">Person Re-identification in the Wild</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>68%</td><td>77</td><td>52</td><td>25</td><td>1</td><td>47</td><td>27</td></tr><tr><td>0dc11a37cadda92886c56a6fb5191ded62099c28</td><td>stickmen_family</td><td>We Are Family Stickmen</td><td><a href="papers/0dc11a37cadda92886c56a6fb5191ded62099c28.html" target="_blank">We Are Family: Joint Pose Estimation of Multiple Persons</a></td><td><a href="http://eprints.pascal-network.org/archive/00007964/01/eichner10eccv.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>78</td><td>51</td><td>27</td><td>5</td><td>54</td><td>23</td></tr><tr><td>0b440695c822a8e35184fb2f60dcdaa8a6de84ae</td><td>kinectface</td><td>KinectFaceDB</td><td><a href="papers/0b440695c822a8e35184fb2f60dcdaa8a6de84ae.html" target="_blank">KinectFaceDB: A Kinect Database for Face Recognition</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Systems, Man, and Cybernetics: Systems</td><td>edu</td><td>University of North Carolina at Chapel Hill</td><td>United States</td><td>35.91139710</td><td>-79.05045290</td><td>61%</td><td>82</td><td>50</td><td>32</td><td>6</td><td>28</td><td>52</td></tr><tr><td>0a85bdff552615643dd74646ac881862a7c7072d</td><td>pipa</td><td>PIPA</td><td><a href="papers/0a85bdff552615643dd74646ac881862a7c7072d.html" target="_blank">Beyond frontal faces: Improving Person Recognition using multiple cues</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>company</td><td>Facebook</td><td>United States</td><td>37.39367170</td><td>-122.08072620</td><td>91%</td><td>54</td><td>49</td><td>4</td><td>1</td><td>41</td><td>12</td></tr><tr><td>c900e0ad4c95948baaf0acd8449fde26f9b4952a</td><td>emotio_net</td><td>EmotioNet Database</td><td><a href="papers/c900e0ad4c95948baaf0acd8449fde26f9b4952a.html" target="_blank">EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>86</td><td>45</td><td>41</td><td>7</td><td>54</td><td>29</td></tr><tr><td>f1af714b92372c8e606485a3982eab2f16772ad8</td><td>mug_faces</td><td>MUG Faces</td><td><a href="papers/f1af714b92372c8e606485a3982eab2f16772ad8.html" target="_blank">The MUG facial expression database</a></td><td><span class="gray">[pdf]</a></td><td>11th International Workshop on Image Analysis for Multimedia Interactive Services WIAMIS 10</td><td>edu</td><td>Aristotle University of Thessaloniki</td><td>Greece</td><td>40.62984145</td><td>22.95889350</td><td>55%</td><td>82</td><td>45</td><td>37</td><td>4</td><td>34</td><td>47</td></tr><tr><td>636b8ffc09b1b23ff714ac8350bb35635e49fa3c</td><td>caltech_10k_web_faces</td><td>Caltech 10K Web Faces</td><td><a href="papers/636b8ffc09b1b23ff714ac8350bb35635e49fa3c.html" target="_blank">Pruning training sets for learning of object categories</a></td><td><span class="gray">[pdf]</a></td><td>2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)</td><td></td><td></td><td></td><td></td><td></td><td>70%</td><td>63</td><td>44</td><td>19</td><td>4</td><td>42</td><td>20</td></tr><tr><td>6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c</td><td>afad</td><td>AFAD</td><td><a href="papers/6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c.html" target="_blank">Ordinal Regression with Multiple Output CNN for Age Estimation</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>78</td><td>42</td><td>36</td><td>8</td><td>44</td><td>31</td></tr><tr><td>2bf8541199728262f78d4dced6fb91479b39b738</td><td>clothing_co_parsing</td><td>CCP</td><td><a href="papers/2bf8541199728262f78d4dced6fb91479b39b738.html" target="_blank">Clothing Co-parsing by Joint Image Segmentation and Labeling</a></td><td><span class="gray">[pdf]</a></td><td>2014 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>70%</td><td>60</td><td>42</td><td>18</td><td>0</td><td>34</td><td>28</td></tr><tr><td>4793f11fbca4a7dba898b9fff68f70d868e2497c</td><td>kin_face</td><td>UB KinFace</td><td><a href="papers/4793f11fbca4a7dba898b9fff68f70d868e2497c.html" target="_blank">Kinship Verification through Transfer Learning</a></td><td><a href="https://pdfs.semanticscholar.org/4793/f11fbca4a7dba898b9fff68f70d868e2497c.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>58%</td><td>71</td><td>41</td><td>30</td><td>2</td><td>29</td><td>42</td></tr><tr><td>f41c7bb02fc97d5fb9cadd7a49c3e558a1c58a44</td><td>pa_100k</td><td>PA-100K</td><td><a href="papers/f41c7bb02fc97d5fb9cadd7a49c3e558a1c58a44.html" target="_blank">HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>75%</td><td>55</td><td>41</td><td>14</td><td>0</td><td>36</td><td>17</td></tr><tr><td>faf40ce28857aedf183e193486f5b4b0a8c478a2</td><td>iit_dehli_ear</td><td>IIT Dehli Ear</td><td><a href="papers/faf40ce28857aedf183e193486f5b4b0a8c478a2.html" target="_blank">Automated Human Identification Using Ear Imaging</a></td><td><a href="https://pdfs.semanticscholar.org/faf4/0ce28857aedf183e193486f5b4b0a8c478a2.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>80</td><td>40</td><td>40</td><td>6</td><td>35</td><td>44</td></tr><tr><td>4d58f886f5150b2d5e48fd1b5a49e09799bf895d</td><td>texas_3dfrd</td><td>Texas 3DFRD</td><td><a href="papers/4d58f886f5150b2d5e48fd1b5a49e09799bf895d.html" target="_blank">Texas 3D Face Recognition Database</a></td><td><span class="gray">[pdf]</a></td><td>2010 IEEE Southwest Symposium on Image Analysis & Interpretation (SSIAI)</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>66</td><td>40</td><td>26</td><td>3</td><td>40</td><td>27</td></tr><tr><td>31de9b3dd6106ce6eec9a35991b2b9083395fd0b</td><td>feret</td><td>FERET</td><td><a href="papers/31de9b3dd6106ce6eec9a35991b2b9083395fd0b.html" target="_blank">FERET ( Face Recognition Technology ) Recognition Algorithm Development and Test Results</a></td><td><a href="https://pdfs.semanticscholar.org/31de/9b3dd6106ce6eec9a35991b2b9083395fd0b.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>75</td><td>39</td><td>36</td><td>5</td><td>54</td><td>20</td></tr><tr><td>47aeb3b82f54b5ae8142b4bdda7b614433e69b9a</td><td>am_fed</td><td>AM-FED</td><td><a href="papers/47aeb3b82f54b5ae8142b4bdda7b614433e69b9a.html" target="_blank">Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops</td><td></td><td></td><td></td><td></td><td></td><td>46%</td><td>83</td><td>38</td><td>45</td><td>6</td><td>43</td><td>39</td></tr><tr><td>22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b</td><td>saivt</td><td>SAIVT SoftBio</td><td><a href="papers/22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b.html" target="_blank">A Database for Person Re-Identification in Multi-Camera Surveillance Networks</a></td><td><span class="gray">[pdf]</a></td><td>2012 International Conference on Digital Image Computing Techniques and Applications (DICTA)</td><td></td><td></td><td></td><td></td><td></td><td>58%</td><td>65</td><td>38</td><td>27</td><td>6</td><td>45</td><td>20</td></tr><tr><td>79828e6e9f137a583082b8b5a9dfce0c301989b8</td><td>mapillary</td><td>Mapillary</td><td><a href="papers/79828e6e9f137a583082b8b5a9dfce0c301989b8.html" target="_blank">The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>61</td><td>37</td><td>24</td><td>0</td><td>43</td><td>16</td></tr><tr><td>3dc3f0b64ef80f573e3a5f96e456e52ee980b877</td><td>georgia_tech_face_database</td><td>Georgia Tech Face</td><td><a href="papers/3dc3f0b64ef80f573e3a5f96e456e52ee980b877.html" target="_blank">Maximum Likelihood Training of the Embedded HMM for Face Detection and Recognition</a></td><td><a href="http://www.researchgate.net/profile/Monson_Hayes/publication/221124512_Maximum_Likelihood_Training_of_the_Embedded_HMM_for_Face_Detection_and_Recognition/links/0deec53509be9d6f55000000.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>67</td><td>36</td><td>31</td><td>4</td><td>29</td><td>28</td></tr><tr><td>6f3c76b7c0bd8e1d122c6ea808a271fd4749c951</td><td>ward</td><td>WARD</td><td><a href="papers/6f3c76b7c0bd8e1d122c6ea808a271fd4749c951.html" target="_blank">Re-identify people in wide area camera network</a></td><td><span class="gray">[pdf]</a></td><td>2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops</td><td>edu</td><td>University of Udine</td><td>Italy</td><td>46.08107230</td><td>13.21194740</td><td>60%</td><td>60</td><td>36</td><td>24</td><td>1</td><td>38</td><td>21</td></tr><tr><td>fcc6fe6007c322641796cb8792718641856a22a7</td><td>miw</td><td>MIW</td><td><a href="papers/fcc6fe6007c322641796cb8792718641856a22a7.html" target="_blank">Automatic facial makeup detection with application in face recognition</a></td><td><span class="gray">[pdf]</a></td><td>2013 International Conference on Biometrics (ICB)</td><td>edu</td><td>West Virginia University</td><td>United States</td><td>39.65404635</td><td>-79.96475355</td><td>71%</td><td>49</td><td>35</td><td>14</td><td>1</td><td>19</td><td>29</td></tr><tr><td>09d78009687bec46e70efcf39d4612822e61cb8c</td><td>raid</td><td>RAiD</td><td><a href="papers/09d78009687bec46e70efcf39d4612822e61cb8c.html" target="_blank">Consistent Re-identification in a Camera Network</a></td><td><a href="https://pdfs.semanticscholar.org/c27f/099e6e7e3f7f9979cbe9e0a5175fc5848ea0.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>49</td><td>35</td><td>14</td><td>3</td><td>34</td><td>13</td></tr><tr><td>fcc6fe6007c322641796cb8792718641856a22a7</td><td>youtube_makeup</td><td>YMU</td><td><a href="papers/fcc6fe6007c322641796cb8792718641856a22a7.html" target="_blank">Automatic facial makeup detection with application in face recognition</a></td><td><span class="gray">[pdf]</a></td><td>2013 International Conference on Biometrics (ICB)</td><td>edu</td><td>West Virginia University</td><td>United States</td><td>39.65404635</td><td>-79.96475355</td><td>71%</td><td>49</td><td>35</td><td>14</td><td>1</td><td>19</td><td>29</td></tr><tr><td>51eba481dac6b229a7490f650dff7b17ce05df73</td><td>imsitu</td><td>imSitu</td><td><a href="papers/51eba481dac6b229a7490f650dff7b17ce05df73.html" target="_blank">Situation Recognition: Visual Semantic Role Labeling for Image Understanding</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>52</td><td>34</td><td>18</td><td>1</td><td>46</td><td>6</td></tr><tr><td>8be57cdad86fdf8c8290df4ca3149592f3c46dd3</td><td>m2vts</td><td>m2vts</td><td><a href="papers/8be57cdad86fdf8c8290df4ca3149592f3c46dd3.html" target="_blank">The M2VTS Multimodal Face Database (Release 1.00)</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>45%</td><td>73</td><td>33</td><td>40</td><td>2</td><td>39</td><td>33</td></tr><tr><td>31b05f65405534a696a847dd19c621b7b8588263</td><td>umd_faces</td><td>UMD</td><td><a href="papers/31b05f65405534a696a847dd19c621b7b8588263.html" target="_blank">UMDFaces: An annotated face dataset for training deep networks</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE International Joint Conference on Biometrics (IJCB)</td><td>edu</td><td>University of Maryland</td><td>United States</td><td>39.28996850</td><td>-76.62196103</td><td>79%</td><td>42</td><td>33</td><td>9</td><td>2</td><td>30</td><td>11</td></tr><tr><td>37d6f0eb074d207b53885bd2eb78ccc8a04be597</td><td>vmu</td><td>VMU</td><td><a href="papers/37d6f0eb074d207b53885bd2eb78ccc8a04be597.html" target="_blank">Can facial cosmetics affect the matching accuracy of face recognition systems?</a></td><td><span class="gray">[pdf]</a></td><td>2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td>edu</td><td>West Virginia University</td><td>United States</td><td>39.65404635</td><td>-79.96475355</td><td>62%</td><td>53</td><td>33</td><td>20</td><td>0</td><td>19</td><td>31</td></tr><tr><td>37d6f0eb074d207b53885bd2eb78ccc8a04be597</td><td>youtube_makeup</td><td>YMU</td><td><a href="papers/37d6f0eb074d207b53885bd2eb78ccc8a04be597.html" target="_blank">Can facial cosmetics affect the matching accuracy of face recognition systems?</a></td><td><span class="gray">[pdf]</a></td><td>2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td>edu</td><td>West Virginia University</td><td>United States</td><td>39.65404635</td><td>-79.96475355</td><td>62%</td><td>53</td><td>33</td><td>20</td><td>0</td><td>19</td><td>31</td></tr><tr><td>9c23859ec7313f2e756a3e85575735e0c52249f4</td><td>facebook_100</td><td>Facebook100</td><td><a href="papers/9c23859ec7313f2e756a3e85575735e0c52249f4.html" target="_blank">Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook</a></td><td><span class="gray">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>Harvard University</td><td>United States</td><td>42.36782045</td><td>-71.12666653</td><td>62%</td><td>52</td><td>32</td><td>20</td><td>3</td><td>38</td><td>13</td></tr><tr><td>2161f6b7ee3c0acc81603b01dc0df689683577b9</td><td>large_scale_person_search</td><td>Large Scale Person Search</td><td><a href="papers/2161f6b7ee3c0acc81603b01dc0df689683577b9.html" target="_blank">End-to-End Deep Learning for Person Search</a></td><td><a href="https://pdfs.semanticscholar.org/2161/f6b7ee3c0acc81603b01dc0df689683577b9.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>70%</td><td>46</td><td>32</td><td>14</td><td>0</td><td>27</td><td>16</td></tr><tr><td>9c23859ec7313f2e756a3e85575735e0c52249f4</td><td>pubfig_83</td><td>pubfig83</td><td><a href="papers/9c23859ec7313f2e756a3e85575735e0c52249f4.html" target="_blank">Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook</a></td><td><span class="gray">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>Harvard University</td><td>United States</td><td>42.36782045</td><td>-71.12666653</td><td>62%</td><td>52</td><td>32</td><td>20</td><td>3</td><td>38</td><td>13</td></tr><tr><td>18858cc936947fc96b5c06bbe3c6c2faa5614540</td><td>pilot_parliament</td><td>PPB</td><td><a href="papers/18858cc936947fc96b5c06bbe3c6c2faa5614540.html" target="_blank">Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification</a></td><td><a href="http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a-supp.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>53%</td><td>59</td><td>31</td><td>28</td><td>0</td><td>47</td><td>10</td></tr><tr><td>16e8b0a1e8451d5f697b94c0c2b32a00abee1d52</td><td>umb</td><td>UMB</td><td><a href="papers/16e8b0a1e8451d5f697b94c0c2b32a00abee1d52.html" target="_blank">UMB-DB: A database of partially occluded 3D faces</a></td><td><span class="gray">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td></td><td></td><td></td><td></td><td></td><td>66%</td><td>47</td><td>31</td><td>16</td><td>2</td><td>22</td><td>24</td></tr><tr><td>356b431d4f7a2a0a38cf971c84568207dcdbf189</td><td>wider</td><td>WIDER</td><td><a href="papers/356b431d4f7a2a0a38cf971c84568207dcdbf189.html" target="_blank">Recognize complex events from static images by fusing deep channels</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>70%</td><td>44</td><td>31</td><td>13</td><td>1</td><td>29</td><td>15</td></tr><tr><td>8b2dd5c61b23ead5ae5508bb8ce808b5ea266730</td><td>10k_US_adult_faces</td><td>10K US Adult Faces</td><td><a href="papers/8b2dd5c61b23ead5ae5508bb8ce808b5ea266730.html" target="_blank">The intrinsic memorability of face photographs.</a></td><td><a href="https://pdfs.semanticscholar.org/8b2d/d5c61b23ead5ae5508bb8ce808b5ea266730.pdf" target="_blank">[pdf]</a></td><td>Journal of experimental psychology. General</td><td></td><td></td><td></td><td></td><td></td><td>56%</td><td>52</td><td>29</td><td>23</td><td>2</td><td>36</td><td>14</td></tr><tr><td>28d4e027c7e90b51b7d8908fce68128d1964668a</td><td>megaface</td><td>MegaFace</td><td><a href="papers/28d4e027c7e90b51b7d8908fce68128d1964668a.html" target="_blank">Level Playing Field for Million Scale Face Recognition</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>University of Washington</td><td>United States</td><td>47.65432380</td><td>-122.30800894</td><td>72%</td><td>39</td><td>28</td><td>11</td><td>2</td><td>29</td><td>9</td></tr><tr><td>070de852bc6eb275d7ca3a9cdde8f6be8795d1a3</td><td>d3dfacs</td><td>D3DFACS</td><td><a href="papers/070de852bc6eb275d7ca3a9cdde8f6be8795d1a3.html" target="_blank">A FACS valid 3D dynamic action unit database with applications to 3D dynamic morphable facial modeling</a></td><td><span class="gray">[pdf]</a></td><td>2011 International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>50</td><td>26</td><td>24</td><td>5</td><td>31</td><td>18</td></tr><tr><td>47662d1a368daf70ba70ef2d59eb6209f98b675d</td><td>fia</td><td>CMU FiA</td><td><a href="papers/47662d1a368daf70ba70ef2d59eb6209f98b675d.html" target="_blank">The CMU Face In Action (FIA) Database</a></td><td><a href="https://pdfs.semanticscholar.org/4766/2d1a368daf70ba70ef2d59eb6209f98b675d.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>48%</td><td>54</td><td>26</td><td>28</td><td>5</td><td>40</td><td>16</td></tr><tr><td>298cbc3dfbbb3a20af4eed97906650a4ea1c29e0</td><td>ferplus</td><td>FER+</td><td><a href="papers/298cbc3dfbbb3a20af4eed97906650a4ea1c29e0.html" target="_blank">Training deep networks for facial expression recognition with crowd-sourced label distribution</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>74%</td><td>34</td><td>25</td><td>9</td><td>0</td><td>18</td><td>16</td></tr><tr><td>e27ef52c641c2b5100a1b34fd0b819e84a31b4df</td><td>sarc3d</td><td>Sarc3D</td><td><a href="papers/e27ef52c641c2b5100a1b34fd0b819e84a31b4df.html" target="_blank">SARC3D: A New 3D Body Model for People Tracking and Re-identification</a></td><td><a href="https://pdfs.semanticscholar.org/e27e/f52c641c2b5100a1b34fd0b819e84a31b4df.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>74%</td><td>34</td><td>25</td><td>9</td><td>2</td><td>21</td><td>12</td></tr><tr><td>57fe081950f21ca03b5b375ae3e84b399c015861</td><td>cvc_01_barcelona</td><td>CVC-01</td><td><a href="papers/57fe081950f21ca03b5b375ae3e84b399c015861.html" target="_blank">Adaptive Image Sampling and Windows Classification for On-board Pedestrian Detection</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>51%</td><td>47</td><td>24</td><td>23</td><td>1</td><td>23</td><td>24</td></tr><tr><td>758d7e1be64cc668c59ef33ba8882c8597406e53</td><td>affectnet</td><td>AffectNet</td><td><a href="papers/758d7e1be64cc668c59ef33ba8882c8597406e53.html" target="_blank">AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild</a></td><td><a href="https://arxiv.org/pdf/1708.03985.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>37</td><td>23</td><td>14</td><td>0</td><td>25</td><td>11</td></tr><tr><td>d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae</td><td>b3d_ac</td><td>B3D(AC)</td><td><a href="papers/d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae.html" target="_blank">A 3-D Audio-Visual Corpus of Affective Communication</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Multimedia</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>42</td><td>23</td><td>19</td><td>2</td><td>26</td><td>15</td></tr><tr><td>53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4</td><td>bp4d_plus</td><td>BP4D+</td><td><a href="papers/53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4.html" target="_blank">Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>42</td><td>23</td><td>19</td><td>0</td><td>17</td><td>26</td></tr><tr><td>1bd1645a629f1b612960ab9bba276afd4cf7c666</td><td>brainwash</td><td>Brainwash</td><td><a href="papers/1bd1645a629f1b612960ab9bba276afd4cf7c666.html" target="_blank">End-to-End People Detection in Crowded Scenes</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>Stanford University</td><td>United States</td><td>37.43131385</td><td>-122.16936535</td><td>55%</td><td>42</td><td>23</td><td>19</td><td>1</td><td>19</td><td>19</td></tr><tr><td>1c2802c2199b6d15ecefe7ba0c39bfe44363de38</td><td>youtube_poses</td><td>YouTube Pose</td><td><a href="papers/1c2802c2199b6d15ecefe7ba0c39bfe44363de38.html" target="_blank">Personalizing Human Video Pose Estimation</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>Oxford University</td><td>United Kingdom</td><td>51.75208490</td><td>-1.25166460</td><td>64%</td><td>36</td><td>23</td><td>13</td><td>2</td><td>30</td><td>8</td></tr><tr><td>0cb2dd5f178e3a297a0c33068961018659d0f443</td><td>ijb_b</td><td>IJB-B</td><td><a href="papers/0cb2dd5f178e3a297a0c33068961018659d0f443.html" target="_blank">IARPA Janus Benchmark-B Face Dataset</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</td><td>edu</td><td>Michigan State University</td><td>United States</td><td>42.71856800</td><td>-84.47791571</td><td>63%</td><td>35</td><td>22</td><td>13</td><td>3</td><td>25</td><td>8</td></tr><tr><td>41976ebc8ab76d9a6861487c97cc7fcbe3b6015f</td><td>moments_in_time</td><td>Moments in Time</td><td><a href="papers/41976ebc8ab76d9a6861487c97cc7fcbe3b6015f.html" target="_blank">Moments in Time Dataset: one million videos for event understanding</a></td><td><a href="https://arxiv.org/pdf/1801.03150.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td>edu</td><td>Columbia University</td><td>United States</td><td>40.84198360</td><td>-73.94368971</td><td>76%</td><td>29</td><td>22</td><td>7</td><td>2</td><td>27</td><td>2</td></tr><tr><td>a0cc5f73a37723a6dd465924143f1cb4976d0169</td><td>msmt_17</td><td>MSMT17</td><td><a href="papers/a0cc5f73a37723a6dd465924143f1cb4976d0169.html" target="_blank">Person Transfer GAN to Bridge Domain Gap for Person Re-identification</a></td><td><span class="gray">[pdf]</a></td><td>2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>92%</td><td>24</td><td>22</td><td>2</td><td>1</td><td>20</td><td>4</td></tr><tr><td>fd8168f1c50de85bac58a8d328df0a50248b16ae</td><td>nd_2006</td><td>ND-2006</td><td><a href="papers/fd8168f1c50de85bac58a8d328df0a50248b16ae.html" target="_blank">Using a Multi-Instance Enrollment Representation to Improve 3D Face Recognition</a></td><td><span class="gray">[pdf]</a></td><td>2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems</td><td>edu</td><td>University of Notre Dame</td><td>United States</td><td>41.70456775</td><td>-86.23822026</td><td>63%</td><td>35</td><td>22</td><td>13</td><td>3</td><td>18</td><td>15</td></tr><tr><td>488e475eeb3bb39a145f23ede197cd3620f1d98a</td><td>apis</td><td>APiS1.0</td><td><a href="papers/488e475eeb3bb39a145f23ede197cd3620f1d98a.html" target="_blank">Pedestrian Attribute Classification in Surveillance: Database and Evaluation</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision Workshops</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>28</td><td>20</td><td>8</td><td>0</td><td>13</td><td>15</td></tr><tr><td>84fe5b4ac805af63206012d29523a1e033bc827e</td><td>awe_ears</td><td>AWE Ears</td><td><a href="papers/84fe5b4ac805af63206012d29523a1e033bc827e.html" target="_blank">Ear Recognition: More Than a Survey</a></td><td><a href="https://arxiv.org/pdf/1611.06203.pdf" target="_blank">[pdf]</a></td><td>Neurocomputing</td><td></td><td></td><td></td><td></td><td></td><td>77%</td><td>26</td><td>20</td><td>6</td><td>0</td><td>10</td><td>16</td></tr><tr><td>20388099cc415c772926e47bcbbe554e133343d1</td><td>cafe</td><td>#N/A</td><td><a href="papers/20388099cc415c772926e47bcbbe554e133343d1.html" target="_blank">The Child Affective Facial Expression (CAFE) set: validity and reliability from untrained adults</a></td><td><a href="https://pdfs.semanticscholar.org/2038/8099cc415c772926e47bcbbe554e133343d1.pdf" target="_blank">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>37</td><td>20</td><td>17</td><td>3</td><td>30</td><td>7</td></tr><tr><td>0ceda9dae8b9f322df65ca2ef02caca9758aec6f</td><td>casablanca</td><td>Casablanca</td><td><a href="papers/0ceda9dae8b9f322df65ca2ef02caca9758aec6f.html" target="_blank">Context-Aware CNNs for Person Head Detection</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>33</td><td>20</td><td>13</td><td>1</td><td>23</td><td>11</td></tr><tr><td>0ceda9dae8b9f322df65ca2ef02caca9758aec6f</td><td>hollywood_headset</td><td>HollywoodHeads</td><td><a href="papers/0ceda9dae8b9f322df65ca2ef02caca9758aec6f.html" target="_blank">Context-Aware CNNs for Person Head Detection</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>33</td><td>20</td><td>13</td><td>1</td><td>23</td><td>11</td></tr><tr><td>0297448f3ed948e136bb06ceff10eccb34e5bb77</td><td>ilids_mcts</td><td>i-LIDS Multiple-Camera</td><td><a href="papers/0297448f3ed948e136bb06ceff10eccb34e5bb77.html" target="_blank">Imagery Library for Intelligent Detection Systems (i-LIDS); A Standard for Testing Video Based Detection Systems</a></td><td><span class="gray">[pdf]</a></td><td>Proceedings 40th Annual 2006 International Carnahan Conference on Security Technology</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>35</td><td>20</td><td>15</td><td>2</td><td>21</td><td>14</td></tr><tr><td>488e475eeb3bb39a145f23ede197cd3620f1d98a</td><td>svs</td><td>SVS</td><td><a href="papers/488e475eeb3bb39a145f23ede197cd3620f1d98a.html" target="_blank">Pedestrian Attribute Classification in Surveillance: Database and Evaluation</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision Workshops</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>28</td><td>20</td><td>8</td><td>0</td><td>13</td><td>15</td></tr><tr><td>4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7</td><td>deep_fashion</td><td>DeepFashion</td><td><a href="papers/4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7.html" target="_blank">Fashion Landmark Detection in the Wild</a></td><td><a href="https://arxiv.org/pdf/1608.03049.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>73%</td><td>26</td><td>19</td><td>7</td><td>1</td><td>16</td><td>10</td></tr><tr><td>64e0690dd176a93de9d4328f6e31fc4afe1e7536</td><td>duke_mtmc</td><td>Duke MTMC</td><td><a href="papers/64e0690dd176a93de9d4328f6e31fc4afe1e7536.html" target="_blank">Tracking Multiple People Online and in Real Time</a></td><td><a href="https://pdfs.semanticscholar.org/64e0/690dd176a93de9d4328f6e31fc4afe1e7536.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>78%</td><td>23</td><td>18</td><td>5</td><td>1</td><td>12</td><td>10</td></tr><tr><td>221c18238b829c12b911706947ab38fd017acef7</td><td>rap_pedestrian</td><td>RAP</td><td><a href="papers/221c18238b829c12b911706947ab38fd017acef7.html" target="_blank">A Richly Annotated Dataset for Pedestrian Attribute Recognition</a></td><td><a href="https://arxiv.org/pdf/1603.07054.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>69%</td><td>26</td><td>18</td><td>8</td><td>0</td><td>16</td><td>10</td></tr><tr><td>d818568838433a6d6831adde49a58cef05e0c89f</td><td>agedb</td><td>AgeDB</td><td><a href="papers/d818568838433a6d6831adde49a58cef05e0c89f.html" target="_blank">AgeDB: The First Manually Collected, In-the-Wild Age Database</a></td><td><a href="http://eprints.mdx.ac.uk/22044/1/agedb_kotsia.pdf" target="_blank">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</td><td>edu</td><td>Imperial College London</td><td>United Kingdom</td><td>51.49887085</td><td>-0.17560797</td><td>94%</td><td>18</td><td>17</td><td>1</td><td>0</td><td>14</td><td>3</td></tr><tr><td>213a579af9e4f57f071b884aa872651372b661fd</td><td>bbc_pose</td><td>BBC Pose</td><td><a href="papers/213a579af9e4f57f071b884aa872651372b661fd.html" target="_blank">Automatic and Efficient Human Pose Estimation for Sign Language Videos</a></td><td><a href="http://tomas.pfister.fi/files/charles13ijcv.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>26</td><td>17</td><td>9</td><td>1</td><td>16</td><td>11</td></tr><tr><td>4946ba10a4d5a7d0a38372f23e6622bd347ae273</td><td>coco_action</td><td>COCO-a</td><td><a href="papers/4946ba10a4d5a7d0a38372f23e6622bd347ae273.html" target="_blank">Describing Common Human Visual Actions in Images</a></td><td><a href="https://arxiv.org/pdf/1506.02203.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>68%</td><td>25</td><td>17</td><td>8</td><td>0</td><td>23</td><td>2</td></tr><tr><td>d178cde92ab3dc0dd2ebee5a76a33d556c39448b</td><td>jiku_mobile</td><td>Jiku Mobile Video Dataset</td><td><a href="papers/d178cde92ab3dc0dd2ebee5a76a33d556c39448b.html" target="_blank">The jiku mobile video dataset</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td>edu</td><td>National University of Singapore</td><td>Singapore</td><td>1.29620180</td><td>103.77689944</td><td>71%</td><td>24</td><td>17</td><td>7</td><td>0</td><td>6</td><td>19</td></tr><tr><td>2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d</td><td>kin_face</td><td>UB KinFace</td><td><a href="papers/2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d.html" target="_blank">Genealogical face recognition based on UB KinFace database</a></td><td><span class="gray">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>SUNY Buffalo</td><td>United States</td><td>42.93362780</td><td>-78.88394479</td><td>55%</td><td>31</td><td>17</td><td>14</td><td>0</td><td>11</td><td>21</td></tr><tr><td>ea050801199f98a1c7c1df6769f23f658299a3ae</td><td>mpi_large</td><td>Large MPI Facial Expression</td><td><a href="papers/ea050801199f98a1c7c1df6769f23f658299a3ae.html" target="_blank">The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</a></td><td><a href="https://pdfs.semanticscholar.org/ea05/0801199f98a1c7c1df6769f23f658299a3ae.pdf" target="_blank">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>33</td><td>17</td><td>16</td><td>4</td><td>29</td><td>4</td></tr><tr><td>ea050801199f98a1c7c1df6769f23f658299a3ae</td><td>mpi_small</td><td>Small MPI Facial Expression</td><td><a href="papers/ea050801199f98a1c7c1df6769f23f658299a3ae.html" target="_blank">The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</a></td><td><a href="https://pdfs.semanticscholar.org/ea05/0801199f98a1c7c1df6769f23f658299a3ae.pdf" target="_blank">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>33</td><td>17</td><td>16</td><td>4</td><td>29</td><td>4</td></tr><tr><td>1e3df3ca8feab0b36fd293fe689f93bb2aaac591</td><td>immediacy</td><td>Immediacy</td><td><a href="papers/1e3df3ca8feab0b36fd293fe689f93bb2aaac591.html" target="_blank">Multi-task Recurrent Neural Network for Immediacy Prediction</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>26</td><td>16</td><td>10</td><td>2</td><td>21</td><td>6</td></tr><tr><td>71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6</td><td>umd_faces</td><td>UMD</td><td><a href="papers/71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6.html" target="_blank">The Do’s and Don’ts for CNN-Based Face Verification</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE International Conference on Computer Vision Workshops (ICCVW)</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>26</td><td>16</td><td>10</td><td>2</td><td>16</td><td>8</td></tr><tr><td>19d1b811df60f86cbd5e04a094b07f32fff7a32a</td><td>york_3d</td><td>UOY 3D Face Database</td><td><a href="papers/19d1b811df60f86cbd5e04a094b07f32fff7a32a.html" target="_blank">Three-dimensional face recognition: an eigensurface approach</a></td><td><span class="gray">[pdf]</a></td><td>2004 International Conference on Image Processing, 2004. ICIP '04.</td><td></td><td></td><td></td><td></td><td></td><td>42%</td><td>38</td><td>16</td><td>22</td><td>4</td><td>24</td><td>13</td></tr><tr><td>b6c293f0420f7e945b5916ae44269fb53e139275</td><td>erce</td><td>ERCe</td><td><a href="papers/b6c293f0420f7e945b5916ae44269fb53e139275.html" target="_blank">Video Synopsis by Heterogeneous Multi-source Correlation</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>29</td><td>15</td><td>14</td><td>2</td><td>14</td><td>13</td></tr><tr><td>b6c293f0420f7e945b5916ae44269fb53e139275</td><td>tisi</td><td>Times Square Intersection</td><td><a href="papers/b6c293f0420f7e945b5916ae44269fb53e139275.html" target="_blank">Video Synopsis by Heterogeneous Multi-source Correlation</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>29</td><td>15</td><td>14</td><td>2</td><td>14</td><td>13</td></tr><tr><td>2a171f8d14b6b8735001a11c217af9587d095848</td><td>social_relation</td><td>Social Relation</td><td><a href="papers/2a171f8d14b6b8735001a11c217af9587d095848.html" target="_blank">Learning Social Relation Traits from Face Images</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>23</td><td>14</td><td>9</td><td>4</td><td>16</td><td>7</td></tr><tr><td>8875ae233bc074f5cd6c4ebba447b536a7e847a5</td><td>voxceleb2</td><td>VoxCeleb2</td><td><a href="papers/8875ae233bc074f5cd6c4ebba447b536a7e847a5.html" target="_blank">VoxCeleb2: Deep Speaker Recognition.</a></td><td><a href="https://pdfs.semanticscholar.org/8875/ae233bc074f5cd6c4ebba447b536a7e847a5.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>41%</td><td>34</td><td>14</td><td>19</td><td>2</td><td>17</td><td>2</td></tr><tr><td>5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725</td><td>fpoq</td><td>50 People One Question</td><td><a href="papers/5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725.html" target="_blank">Merging Pose Estimates Across Space and Time</a></td><td><a href="http://authors.library.caltech.edu/41565/1/tracking_bmvc.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>81%</td><td>16</td><td>13</td><td>3</td><td>0</td><td>13</td><td>4</td></tr><tr><td>44d23df380af207f5ac5b41459c722c87283e1eb</td><td>wider_attribute</td><td>WIDER Attribute</td><td><a href="papers/44d23df380af207f5ac5b41459c722c87283e1eb.html" target="_blank">Human Attribute Recognition by Deep Hierarchical Contexts</a></td><td><a href="https://pdfs.semanticscholar.org/8e28/07f2dd53b03a759e372e07f7191cae65c9fd.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>72%</td><td>18</td><td>13</td><td>5</td><td>0</td><td>14</td><td>4</td></tr><tr><td>45e616093a92e5f1e61a7c6037d5f637aa8964af</td><td>malf</td><td>MALF</td><td><a href="papers/45e616093a92e5f1e61a7c6037d5f637aa8964af.html" target="_blank">Fine-grained evaluation on face detection in the wild</a></td><td><span class="gray">[pdf]</a></td><td>2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>17</td><td>12</td><td>5</td><td>0</td><td>12</td><td>5</td></tr><tr><td>774cbb45968607a027ae4729077734db000a1ec5</td><td>urban_tribes</td><td>Urban Tribes</td><td><a href="papers/774cbb45968607a027ae4729077734db000a1ec5.html" target="_blank">From Bikers to Surfers: Visual Recognition of Urban Tribes</a></td><td><a href="http://vision.cornell.edu/se3/wp-content/uploads/2014/09/utribes_bmvc13_final.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>67%</td><td>18</td><td>12</td><td>6</td><td>1</td><td>12</td><td>6</td></tr><tr><td>a94cae786d515d3450d48267e12ca954aab791c4</td><td>yawdd</td><td>YawDD</td><td><a href="papers/a94cae786d515d3450d48267e12ca954aab791c4.html" target="_blank">YawDD: a yawning detection dataset</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>80%</td><td>15</td><td>12</td><td>3</td><td>1</td><td>2</td><td>13</td></tr><tr><td>4e6ee936eb50dd032f7138702fa39b7c18ee8907</td><td>dartmouth_children</td><td>Dartmouth Children</td><td><a href="papers/4e6ee936eb50dd032f7138702fa39b7c18ee8907.html" target="_blank">The Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set</a></td><td><a href="https://pdfs.semanticscholar.org/4e6e/e936eb50dd032f7138702fa39b7c18ee8907.pdf" target="_blank">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>21</td><td>11</td><td>10</td><td>2</td><td>18</td><td>3</td></tr><tr><td>4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06</td><td>distance_nighttime</td><td>Long Distance Heterogeneous Face</td><td><a href="papers/4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06.html" target="_blank">Nighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching</a></td><td><a href="https://pdfs.semanticscholar.org/4156/b7e88f2e0ab0a7c095b9bab199ae2b23bd06.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>22</td><td>11</td><td>11</td><td>3</td><td>11</td><td>10</td></tr><tr><td>b71d1aa90dcbe3638888725314c0d56640c1fef1</td><td>ifdb</td><td>IFDB</td><td><a href="papers/b71d1aa90dcbe3638888725314c0d56640c1fef1.html" target="_blank">Iranian Face Database with age, pose and expression</a></td><td><span class="gray">[pdf]</a></td><td>2007 International Conference on Machine Vision</td><td>edu</td><td>Islamic Azad University</td><td>Iran</td><td>34.84529990</td><td>48.55962120</td><td>48%</td><td>23</td><td>11</td><td>12</td><td>2</td><td>14</td><td>9</td></tr><tr><td>57178b36c21fd7f4529ac6748614bb3374714e91</td><td>ijb_c</td><td>IJB-C</td><td><a href="papers/57178b36c21fd7f4529ac6748614bb3374714e91.html" target="_blank">IARPA Janus Benchmark - C: Face Dataset and Protocol</a></td><td><span class="gray">[pdf]</a></td><td>2018 International Conference on Biometrics (ICB)</td><td></td><td></td><td></td><td></td><td></td><td>79%</td><td>14</td><td>11</td><td>3</td><td>0</td><td>12</td><td>1</td></tr><tr><td>ca3e88d87e1344d076c964ea89d91a75c417f5ee</td><td>imfdb</td><td>IMFDB</td><td><a href="papers/ca3e88d87e1344d076c964ea89d91a75c417f5ee.html" target="_blank">Indian Movie Face Database: A benchmark for face recognition under wide variations</a></td><td><span class="gray">[pdf]</a></td><td>2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)</td><td>edu</td><td>BVBCET, Hubli, India</td><td>India</td><td>15.36883320</td><td>75.12137960</td><td>65%</td><td>17</td><td>11</td><td>6</td><td>0</td><td>11</td><td>5</td></tr><tr><td>25474c21613607f6bb7687a281d5f9d4ffa1f9f3</td><td>faceplace</td><td>Face Place</td><td><a href="papers/25474c21613607f6bb7687a281d5f9d4ffa1f9f3.html" target="_blank">Recognizing disguised faces</a></td><td><a href="https://pdfs.semanticscholar.org/d936/7ceb0be378c3a9ddf7cb741c678c1a3c574c.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>34%</td><td>29</td><td>10</td><td>19</td><td>0</td><td>18</td><td>10</td></tr><tr><td>0f0fcf041559703998abf310e56f8a2f90ee6f21</td><td>feret</td><td>FERET</td><td><a href="papers/0f0fcf041559703998abf310e56f8a2f90ee6f21.html" target="_blank">The FERET Evaluation Methodology for Face-Recognition Algorithms</a></td><td><a href="https://pdfs.semanticscholar.org/5099/7a5605c1f61e09e9a96789ed7495be6625aa.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>34%</td><td>29</td><td>10</td><td>19</td><td>3</td><td>18</td><td>9</td></tr><tr><td>2f43b614607163abf41dfe5d17ef6749a1b61304</td><td>hrt_transgender</td><td>HRT Transgender</td><td><a href="papers/2f43b614607163abf41dfe5d17ef6749a1b61304.html" target="_blank">Investigating the Periocular-Based Face Recognition Across Gender Transformation</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Information Forensics and Security</td><td>edu</td><td>University of North Carolina at Wilmington</td><td>United States</td><td>34.22498270</td><td>-77.86907744</td><td>77%</td><td>13</td><td>10</td><td>3</td><td>0</td><td>6</td><td>8</td></tr><tr><td>4563b46d42079242f06567b3f2e2f7a80cb3befe</td><td>vadana</td><td>VADANA</td><td><a href="papers/4563b46d42079242f06567b3f2e2f7a80cb3befe.html" target="_blank">VADANA: A dense dataset for facial image analysis</a></td><td><span class="gray">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td>edu</td><td>University of Delaware</td><td>United States</td><td>39.68103280</td><td>-75.75401840</td><td>67%</td><td>15</td><td>10</td><td>5</td><td>0</td><td>5</td><td>10</td></tr><tr><td>2624d84503bc2f8e190e061c5480b6aa4d89277a</td><td>afew_va</td><td>AFEW-VA</td><td><a href="papers/2624d84503bc2f8e190e061c5480b6aa4d89277a.html" target="_blank">AFEW-VA database for valence and arousal estimation in-the-wild</a></td><td><a href="https://ibug.doc.ic.ac.uk/media/uploads/documents/afew-va.pdf" target="_blank">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>18</td><td>9</td><td>9</td><td>0</td><td>12</td><td>5</td></tr><tr><td>6403117f9c005ae81f1e8e6d1302f4a045e3d99d</td><td>alert_airport</td><td>ALERT Airport</td><td><a href="papers/6403117f9c005ae81f1e8e6d1302f4a045e3d99d.html" target="_blank">A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>45%</td><td>20</td><td>9</td><td>11</td><td>0</td><td>9</td><td>11</td></tr><tr><td>8d5998cd984e7cce307da7d46f155f9db99c6590</td><td>chalearn</td><td>ChaLearn</td><td><a href="papers/8d5998cd984e7cce307da7d46f155f9db99c6590.html" target="_blank">ChaLearn looking at people: A review of events and resources</a></td><td><span class="gray">[pdf]</a></td><td>2017 International Joint Conference on Neural Networks (IJCNN)</td><td></td><td></td><td></td><td></td><td></td><td>69%</td><td>13</td><td>9</td><td>4</td><td>1</td><td>8</td><td>4</td></tr><tr><td>a8d0b149c2eadaa02204d3e4356fbc8eccf3b315</td><td>hi4d_adsip</td><td>Hi4D-ADSIP</td><td><a href="papers/a8d0b149c2eadaa02204d3e4356fbc8eccf3b315.html" target="_blank">Hi4D-ADSIP 3-D dynamic facial articulation database</a></td><td><span class="gray">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>15</td><td>9</td><td>6</td><td>1</td><td>4</td><td>11</td></tr><tr><td>bd26dabab576adb6af30484183c9c9c8379bf2e0</td><td>scut_fbp</td><td>SCUT-FBP</td><td><a href="papers/bd26dabab576adb6af30484183c9c9c8379bf2e0.html" target="_blank">SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Systems, Man, and Cybernetics</td><td></td><td></td><td></td><td></td><td></td><td>47%</td><td>19</td><td>9</td><td>10</td><td>2</td><td>6</td><td>13</td></tr><tr><td>060820f110a72cbf02c14a6d1085bd6e1d994f6a</td><td>caltech_crp</td><td>Caltech CRP</td><td><a href="papers/060820f110a72cbf02c14a6d1085bd6e1d994f6a.html" target="_blank">Fine-grained classification of pedestrians in video: Benchmark and state of the art</a></td><td><a href="https://arxiv.org/pdf/1605.06177.pdf" target="_blank">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>47%</td><td>17</td><td>8</td><td>9</td><td>0</td><td>9</td><td>8</td></tr><tr><td>2b926b3586399d028b46315d7d9fb9d879e4f79c</td><td>frav3d</td><td>FRAV3D</td><td><a href="papers/2b926b3586399d028b46315d7d9fb9d879e4f79c.html" target="_blank">Multimodal 2D, 2.5D & 3D Face Verification</a></td><td><span class="gray">[pdf]</a></td><td>2006 International Conference on Image Processing</td><td>edu</td><td>Universidad Rey Juan Carlos, Spain</td><td>Spain</td><td>40.33586610</td><td>-3.87694320</td><td>57%</td><td>14</td><td>8</td><td>6</td><td>0</td><td>2</td><td>12</td></tr><tr><td>8f02ec0be21461fbcedf51d864f944cfc42c875f</td><td>hda_plus</td><td>HDA+</td><td><a href="papers/8f02ec0be21461fbcedf51d864f944cfc42c875f.html" target="_blank">The HDA+ Data Set for Research on Fully Automated Re-identification Systems</a></td><td><a href="https://pdfs.semanticscholar.org/8f02/ec0be21461fbcedf51d864f944cfc42c875f.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>16</td><td>8</td><td>8</td><td>1</td><td>10</td><td>6</td></tr><tr><td>c570d1247e337f91e555c3be0e8c8a5aba539d9f</td><td>mcgill</td><td>McGill Real World</td><td><a href="papers/c570d1247e337f91e555c3be0e8c8a5aba539d9f.html" target="_blank">Robust semi-automatic head pose labeling for real-world face video sequences</a></td><td><span class="gray">[pdf]</a></td><td>Multimedia Tools and Applications</td><td>edu</td><td>McGill University</td><td>Canada</td><td>45.50397610</td><td>-73.57496870</td><td>44%</td><td>18</td><td>8</td><td>10</td><td>0</td><td>13</td><td>7</td></tr><tr><td>041d3eedf5e45ce5c5229f0181c5c576ed1fafd6</td><td>ucf_selfie</td><td>UCF Selfie</td><td><a href="papers/041d3eedf5e45ce5c5229f0181c5c576ed1fafd6.html" target="_blank">How to Take a Good Selfie?</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>73%</td><td>11</td><td>8</td><td>3</td><td>0</td><td>7</td><td>5</td></tr><tr><td>633c851ebf625ad7abdda2324e9de093cf623141</td><td>appa_real</td><td>APPA-REAL</td><td><a href="papers/633c851ebf625ad7abdda2324e9de093cf623141.html" target="_blank">Apparent and Real Age Estimation in Still Images with Deep Residual Regressors on Appa-Real Database</a></td><td><span class="gray">[pdf]</a></td><td>2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017)</td><td></td><td></td><td></td><td></td><td></td><td>70%</td><td>10</td><td>7</td><td>3</td><td>0</td><td>8</td><td>3</td></tr><tr><td>2cd7821fcf5fae53a185624f7eeda007434ae037</td><td>geofaces</td><td>GeoFaces</td><td><a href="papers/2cd7821fcf5fae53a185624f7eeda007434ae037.html" target="_blank">Exploring the geo-dependence of human face appearance</a></td><td><a href="http://cs.uky.edu/~jacobs/papers/islam2014faces.pdf" target="_blank">[pdf]</a></td><td>IEEE Winter Conference on Applications of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>88%</td><td>8</td><td>7</td><td>1</td><td>0</td><td>5</td><td>3</td></tr><tr><td>2cd7821fcf5fae53a185624f7eeda007434ae037</td><td>geofaces</td><td>GeoFaces</td><td><a href="papers/2cd7821fcf5fae53a185624f7eeda007434ae037.html" target="_blank">Exploring the geo-dependence of human face appearance</a></td><td><a href="http://cs.uky.edu/~jacobs/papers/islam2014faces.pdf" target="_blank">[pdf]</a></td><td>IEEE Winter Conference on Applications of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>88%</td><td>8</td><td>7</td><td>1</td><td>0</td><td>5</td><td>3</td></tr><tr><td>7f4040b482d16354d5938c1d1b926b544652bf5b</td><td>nova_emotions</td><td>Novaemötions Dataset</td><td><a href="papers/7f4040b482d16354d5938c1d1b926b544652bf5b.html" target="_blank">Competitive affective gaming: winning with a smile</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Universidade NOVA de Lisboa, Caparica, Portugal</td><td>Portugal</td><td>38.66096400</td><td>-9.20581300</td><td>78%</td><td>9</td><td>7</td><td>2</td><td>0</td><td>5</td><td>4</td></tr><tr><td>4b4106614c1d553365bad75d7866bff0de6056ed</td><td>ufi</td><td>UFI</td><td><a href="papers/4b4106614c1d553365bad75d7866bff0de6056ed.html" target="_blank">Unconstrained Facial Images: Database for Face Recognition Under Real-World Conditions</a></td><td><a href="http://home.zcu.cz/~pkral/papers/kral_micai15.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>12</td><td>6</td><td>6</td><td>0</td><td>4</td><td>6</td></tr><tr><td>22f656d0f8426c84a33a267977f511f127bfd7f3</td><td>expw</td><td>ExpW</td><td><a href="papers/22f656d0f8426c84a33a267977f511f127bfd7f3.html" target="_blank">From Facial Expression Recognition to Interpersonal Relation Prediction</a></td><td><a href="https://arxiv.org/pdf/1609.06426.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>11</td><td>6</td><td>5</td><td>0</td><td>5</td><td>4</td></tr><tr><td>4af89578ac237278be310f7660a408b03f12d603</td><td>geofaces</td><td>GeoFaces</td><td><a href="papers/4af89578ac237278be310f7660a408b03f12d603.html" target="_blank">Large-scale geo-facial image analysis</a></td><td><a href="https://pdfs.semanticscholar.org/3ede/3ed28329bf48fbd06438a69c4f855bef003f.pdf" target="_blank">[pdf]</a></td><td>EURASIP J. Image and Video Processing</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>6</td><td>6</td><td>0</td><td>0</td><td>4</td><td>2</td></tr><tr><td>2d45cfd838016a6e39f6b766ffe85acd649440c7</td><td>mcgill</td><td>McGill Real World</td><td><a href="papers/2d45cfd838016a6e39f6b766ffe85acd649440c7.html" target="_blank">Hierarchical temporal graphical model for head pose estimation and subsequent attribute classification in real-world videos</a></td><td><span class="gray">[pdf]</a></td><td>Computer Vision and Image Understanding</td><td></td><td></td><td></td><td></td><td></td><td>75%</td><td>8</td><td>6</td><td>2</td><td>0</td><td>5</td><td>3</td></tr><tr><td>1a40092b493c6b8840257ab7f96051d1a4dbfeb2</td><td>sports_videos_in_the_wild</td><td>SVW</td><td><a href="papers/1a40092b493c6b8840257ab7f96051d1a4dbfeb2.html" target="_blank">Sports Videos in the Wild (SVW): A video dataset for sports analysis</a></td><td><span class="gray">[pdf]</a></td><td>2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)</td><td></td><td></td><td></td><td></td><td></td><td>86%</td><td>7</td><td>6</td><td>1</td><td>1</td><td>5</td><td>2</td></tr><tr><td>8627f019882b024aef92e4eb9355c499c733e5b7</td><td>used</td><td>USED Social Event Dataset</td><td><a href="papers/8627f019882b024aef92e4eb9355c499c733e5b7.html" target="_blank">USED: a large-scale social event detection dataset</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td>edu</td><td>University of Trento</td><td>Italy</td><td>46.06588360</td><td>11.11598940</td><td>86%</td><td>7</td><td>6</td><td>1</td><td>0</td><td>3</td><td>4</td></tr><tr><td>0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e</td><td>lag</td><td>LAG</td><td><a href="papers/0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e.html" target="_blank">Large age-gap face verification by feature injection in deep networks</a></td><td><a href="https://arxiv.org/pdf/1602.06149.pdf" target="_blank">[pdf]</a></td><td>Pattern Recognition Letters</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>7</td><td>5</td><td>2</td><td>0</td><td>3</td><td>4</td></tr><tr><td>9e5378e7b336c89735d3bb15cf67eff96f86d39a</td><td>precarious</td><td>Precarious</td><td><a href="papers/9e5378e7b336c89735d3bb15cf67eff96f86d39a.html" target="_blank">Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>36%</td><td>14</td><td>5</td><td>9</td><td>0</td><td>12</td><td>1</td></tr><tr><td>07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1</td><td>uccs</td><td>UCCS</td><td><a href="papers/07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1.html" target="_blank">Large scale unconstrained open set face database</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td>company</td><td>Securics Inc., Colorado Springs, CO</td><td>United States</td><td>38.83388160</td><td>-104.82136340</td><td>83%</td><td>6</td><td>5</td><td>1</td><td>0</td><td>4</td><td>2</td></tr><tr><td>d4f1eb008eb80595bcfdac368e23ae9754e1e745</td><td>uccs</td><td>UCCS</td><td><a href="papers/d4f1eb008eb80595bcfdac368e23ae9754e1e745.html" target="_blank">Unconstrained Face Detection and Open-Set Face Recognition Challenge</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE International Joint Conference on Biometrics (IJCB)</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>5</td><td>5</td><td>0</td><td>0</td><td>4</td><td>1</td></tr><tr><td>922e0a51a3b8c67c4c6ac09a577ff674cbd28b34</td><td>v47</td><td>V47</td><td><a href="papers/922e0a51a3b8c67c4c6ac09a577ff674cbd28b34.html" target="_blank">Re-identification of pedestrians with variable occlusion and scale</a></td><td><span class="gray">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td>edu</td><td>Kingston University</td><td>United Kingdom</td><td>51.42930860</td><td>-0.26840440</td><td>56%</td><td>9</td><td>5</td><td>4</td><td>1</td><td>5</td><td>4</td></tr><tr><td>7ebb153704706e457ab57b432793d2b6e5d12592</td><td>vgg_celebs_in_places</td><td>CIP</td><td><a href="papers/7ebb153704706e457ab57b432793d2b6e5d12592.html" target="_blank">Faces in Places: compound query retrieval</a></td><td><a href="https://pdfs.semanticscholar.org/7ebb/153704706e457ab57b432793d2b6e5d12592.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>5</td><td>5</td><td>0</td><td>0</td><td>3</td><td>2</td></tr><tr><td>56ae6d94fc6097ec4ca861f0daa87941d1c10b70</td><td>cmdp</td><td>CMDP</td><td><a href="papers/56ae6d94fc6097ec4ca861f0daa87941d1c10b70.html" target="_blank">Distance Estimation of an Unknown Person from a Portrait</a></td><td><a href="http://authors.library.caltech.edu/49084/13/FaceDistanceEstimation_RONCHI.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>44%</td><td>9</td><td>4</td><td>5</td><td>0</td><td>6</td><td>3</td></tr><tr><td>563c940054e4b456661762c1ab858e6f730c3159</td><td>data_61</td><td>Data61 Pedestrian</td><td><a href="papers/563c940054e4b456661762c1ab858e6f730c3159.html" target="_blank">A Multi-modal Graphical Model for Scene Analysis</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE Winter Conference on Applications of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>8</td><td>4</td><td>4</td><td>0</td><td>5</td><td>3</td></tr><tr><td>287ddcb3db5562235d83aee318f318b8d5e43fb1</td><td>erce</td><td>ERCe</td><td><a href="papers/287ddcb3db5562235d83aee318f318b8d5e43fb1.html" target="_blank">Learning from Multiple Sources for Video Summarisation</a></td><td><a href="https://arxiv.org/pdf/1501.03069.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>7</td><td>4</td><td>3</td><td>0</td><td>4</td><td>3</td></tr><tr><td>dd65f71dac86e36eecbd3ed225d016c3336b4a13</td><td>families_in_the_wild</td><td>FIW</td><td><a href="papers/dd65f71dac86e36eecbd3ed225d016c3336b4a13.html" target="_blank">Visual Kinship Recognition of Families in the Wild</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td>edu</td><td>University of Massachusetts Dartmouth</td><td>United States</td><td>41.62772475</td><td>-71.00724501</td><td>80%</td><td>5</td><td>4</td><td>1</td><td>0</td><td>2</td><td>3</td></tr><tr><td>137aa2f891d474fce1e7a1d1e9b3aefe21e22b34</td><td>hrt_transgender</td><td>HRT Transgender</td><td><a href="papers/137aa2f891d474fce1e7a1d1e9b3aefe21e22b34.html" target="_blank">Is the eye region more reliable than the face? A preliminary study of face-based recognition on a transgender dataset</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>7</td><td>4</td><td>3</td><td>1</td><td>3</td><td>5</td></tr><tr><td>9cc8cf0c7d7fa7607659921b6ff657e17e135ecc</td><td>mafa</td><td>MAsked FAces</td><td><a href="papers/9cc8cf0c7d7fa7607659921b6ff657e17e135ecc.html" target="_blank">Detecting Masked Faces in the Wild with LLE-CNNs</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>80%</td><td>5</td><td>4</td><td>1</td><td>0</td><td>4</td><td>1</td></tr><tr><td>c72a2ea819df9b0e8cd267eebcc6528b8741e03d</td><td>megaage</td><td>MegaAge</td><td><a href="papers/c72a2ea819df9b0e8cd267eebcc6528b8741e03d.html" target="_blank">Quantifying Facial Age by Posterior of Age Comparisons</a></td><td><a href="https://arxiv.org/pdf/1708.09687.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>4</td><td>4</td><td>0</td><td>0</td><td>4</td><td>0</td></tr><tr><td>23e824d1dfc33f3780dd18076284f07bd99f1c43</td><td>mifs</td><td>MIFS</td><td><a href="papers/23e824d1dfc33f3780dd18076284f07bd99f1c43.html" target="_blank">Spoofing faces using makeup: An investigative study</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)</td><td>edu</td><td>INRIA Méditerranée</td><td>France</td><td>43.61581310</td><td>7.06838000</td><td>67%</td><td>6</td><td>4</td><td>2</td><td>0</td><td>1</td><td>5</td></tr><tr><td>22909dd19a0ec3b6065334cb5be5392cb24d839d</td><td>pets</td><td>PETS 2017</td><td><a href="papers/22909dd19a0ec3b6065334cb5be5392cb24d839d.html" target="_blank">PETS 2017: Dataset and Challenge</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</td><td></td><td></td><td></td><td></td><td></td><td>44%</td><td>9</td><td>4</td><td>5</td><td>0</td><td>1</td><td>8</td></tr><tr><td>54983972aafc8e149259d913524581357b0f91c3</td><td>reseed</td><td>ReSEED</td><td><a href="papers/54983972aafc8e149259d913524581357b0f91c3.html" target="_blank">ReSEED: social event dEtection dataset</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>67%</td><td>6</td><td>4</td><td>2</td><td>1</td><td>1</td><td>5</td></tr><tr><td>287ddcb3db5562235d83aee318f318b8d5e43fb1</td><td>tisi</td><td>Times Square Intersection</td><td><a href="papers/287ddcb3db5562235d83aee318f318b8d5e43fb1.html" target="_blank">Learning from Multiple Sources for Video Summarisation</a></td><td><a href="https://arxiv.org/pdf/1501.03069.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>7</td><td>4</td><td>3</td><td>0</td><td>4</td><td>3</td></tr><tr><td>9e31e77f9543ab42474ba4e9330676e18c242e72</td><td>imdb_face</td><td>IMDb Face</td><td><a href="papers/9e31e77f9543ab42474ba4e9330676e18c242e72.html" target="_blank">The Devil of Face Recognition is in the Noise</a></td><td><a href="https://arxiv.org/pdf/1807.11649.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Nanyang Technological University</td><td>Singapore</td><td>1.34841040</td><td>103.68297965</td><td>50%</td><td>6</td><td>3</td><td>3</td><td>0</td><td>4</td><td>1</td></tr><tr><td>a7fe834a0af614ce6b50dc093132b031dd9a856b</td><td>market_1501</td><td>Market 1501</td><td><a href="papers/a7fe834a0af614ce6b50dc093132b031dd9a856b.html" target="_blank">Orientation Driven Bag of Appearances for Person Re-identification</a></td><td><a href="https://arxiv.org/pdf/1605.02464.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>7</td><td>3</td><td>4</td><td>0</td><td>4</td><td>4</td></tr><tr><td>578d4ad74818086bb64f182f72e2c8bd31e3d426</td><td>mr2</td><td>MR2</td><td><a href="papers/578d4ad74818086bb64f182f72e2c8bd31e3d426.html" target="_blank">The MR2: A multi-racial, mega-resolution database of facial stimuli.</a></td><td><a href="http://www.mpmlab.org/The%20MR2%20face%20database.pdf" target="_blank">[pdf]</a></td><td>Behavior research methods</td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>7</td><td>3</td><td>4</td><td>0</td><td>7</td><td>0</td></tr><tr><td>ad01687649d95cd5b56d7399a9603c4b8e2217d7</td><td>mrp_drone</td><td>MRP Drone</td><td><a href="papers/ad01687649d95cd5b56d7399a9603c4b8e2217d7.html" target="_blank">Investigating Open-World Person Re-identification Using a Drone</a></td><td><a href="https://pdfs.semanticscholar.org/ad01/687649d95cd5b56d7399a9603c4b8e2217d7.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>7</td><td>3</td><td>4</td><td>1</td><td>5</td><td>2</td></tr><tr><td>a7fe834a0af614ce6b50dc093132b031dd9a856b</td><td>pku_reid</td><td>PKU-Reid</td><td><a href="papers/a7fe834a0af614ce6b50dc093132b031dd9a856b.html" target="_blank">Orientation Driven Bag of Appearances for Person Re-identification</a></td><td><a href="https://arxiv.org/pdf/1605.02464.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>7</td><td>3</td><td>4</td><td>0</td><td>4</td><td>4</td></tr><tr><td>3531332efe19be21e7401ba1f04570a142617236</td><td>ufdd</td><td>UFDD</td><td><a href="papers/3531332efe19be21e7401ba1f04570a142617236.html" target="_blank">Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results</a></td><td><a href="https://arxiv.org/pdf/1804.10275.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>75%</td><td>4</td><td>3</td><td>1</td><td>0</td><td>4</td><td>0</td></tr><tr><td>17b46e2dad927836c689d6787ddb3387c6159ece</td><td>geofaces</td><td>GeoFaces</td><td><a href="papers/17b46e2dad927836c689d6787ddb3387c6159ece.html" target="_blank">GeoFaceExplorer: exploring the geo-dependence of facial attributes</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>2</td><td>2</td><td>0</td><td>0</td><td>1</td><td>1</td></tr><tr><td>e58dd160a76349d46f881bd6ddbc2921f08d1050</td><td>gfw</td><td>Grouping Face in the Wild</td><td><a href="papers/e58dd160a76349d46f881bd6ddbc2921f08d1050.html" target="_blank">Merge or Not? Learning to Group Faces via Imitation Learning</a></td><td><a href="https://arxiv.org/pdf/1707.03986.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>2</td><td>2</td><td>0</td><td>0</td><td>2</td><td>0</td></tr><tr><td>4eab317b5ac436a949849ed286baa3de2a541eef</td><td>laofiw</td><td>LAOFIW</td><td><a href="papers/4eab317b5ac436a949849ed286baa3de2a541eef.html" target="_blank">Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings</a></td><td><a href="https://arxiv.org/pdf/1809.02169.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>2</td><td>2</td><td>0</td><td>0</td><td>2</td><td>0</td></tr><tr><td>f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f</td><td>pku_reid</td><td>PKU-Reid</td><td><a href="papers/f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f.html" target="_blank">Swiss-System Based Cascade Ranking for Gait-Based Person Re-Identification</a></td><td><a href="https://pdfs.semanticscholar.org/f6c8/d5e35d7e4d60a0104f233ac1a3ab757da53f.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>4</td><td>2</td><td>2</td><td>0</td><td>1</td><td>2</td></tr><tr><td>4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b38461</td><td>3dddb_unconstrained</td><td>3D Dynamic</td><td><a href="papers/4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b38461.html" target="_blank">A 3D Dynamic Database for Unconstrained Face Recognition</a></td><td><a href="https://pdfs.semanticscholar.org/4d4b/b462c9f1d4e4ab1e4aa6a75cc0bc71b38461.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>2</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td></tr><tr><td>a40f9bfd3c45658ee8da70e1f2dfbe1f0c744d43</td><td>4dfab</td><td>4DFAB</td><td><a href="papers/a40f9bfd3c45658ee8da70e1f2dfbe1f0c744d43.html" target="_blank">4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications</a></td><td><a href="https://arxiv.org/pdf/1712.01443.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>25%</td><td>4</td><td>1</td><td>3</td><td>0</td><td>2</td><td>2</td></tr><tr><td>65355cbb581a219bd7461d48b3afd115263ea760</td><td>complex_activities</td><td>Ongoing Complex Activities</td><td><a href="papers/65355cbb581a219bd7461d48b3afd115263ea760.html" target="_blank">Recognition of ongoing complex activities by sequence prediction over a hierarchical label space</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Winter Conference on Applications of Computer Vision (WACV)</td><td></td><td></td><td></td><td></td><td></td><td>33%</td><td>3</td><td>1</td><td>2</td><td>0</td><td>3</td><td>0</td></tr><tr><td>55c40cbcf49a0225e72d911d762c27bb1c2d14aa</td><td>ifad</td><td>IFAD</td><td><a href="papers/55c40cbcf49a0225e72d911d762c27bb1c2d14aa.html" target="_blank">Indian Face Age Database: A Database for Face Recognition with Age Variation</a></td><td><a href="https://pdfs.semanticscholar.org/025e/4cf3fd3fdeced91e9373b56ee14af7ca432c.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>2</td><td>1</td><td>1</td><td>0</td><td>2</td><td>0</td></tr><tr><td>c06b13d0ec3f5c43e2782cd22542588e233733c3</td><td>nova_emotions</td><td>Novaemötions Dataset</td><td><a href="papers/c06b13d0ec3f5c43e2782cd22542588e233733c3.html" target="_blank">Crowdsourcing facial expressions for affective-interaction</a></td><td><span class="gray">[pdf]</a></td><td>Computer Vision and Image Understanding</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><td>2306b2a8fba28539306052764a77a0d0f5d1236a</td><td>qmul_surv_face</td><td>QMUL-SurvFace</td><td><a href="papers/2306b2a8fba28539306052764a77a0d0f5d1236a.html" target="_blank">Surveillance Face Recognition Challenge</a></td><td><a href="https://arxiv.org/pdf/1804.09691.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td>edu</td><td>Queen Mary University of London</td><td>United Kingdom</td><td>51.52472720</td><td>-0.03931035</td><td>100%</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><td>d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9</td><td>stair_actions</td><td>STAIR Action</td><td><a href="papers/d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9.html" target="_blank">STAIR Actions: A Video Dataset of Everyday Home Actions</a></td><td><a href="https://arxiv.org/pdf/1804.04326.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><td>5ad4e9f947c1653c247d418f05dad758a3f9277b</td><td>wlfdb</td><td>WLFDB</td><td><a href="papers/5ad4e9f947c1653c247d418f05dad758a3f9277b.html" target="_blank">WLFDB : Weakly Labeled Face Databases</a></td><td><a href="https://pdfs.semanticscholar.org/5ad4/e9f947c1653c247d418f05dad758a3f9277b.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><td>7b92d1e53cc87f7a4256695de590098a2f30261e</td><td>appa_real</td><td>APPA-REAL</td><td><a href="papers/7b92d1e53cc87f7a4256695de590098a2f30261e.html" target="_blank">From Apparent to Real Age: Gender, Age, Ethnic, Makeup, and Expression Bias Analysis in Real Age Estimation</a></td><td><span class="gray">[pdf]</a></td><td>2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>1dc35905a1deff8bc74688f2d7e2f48fd2273275</td><td>caltech_pedestrians</td><td>Caltech Pedestrians</td><td><a href="papers/1dc35905a1deff8bc74688f2d7e2f48fd2273275.html" target="_blank">Pedestrian detection: A benchmark</a></td><td><span class="gray">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>15e1af79939dbf90790b03d8aa02477783fb1d0f</td><td>duke_mtmc</td><td>Duke MTMC</td><td><a href="papers/15e1af79939dbf90790b03d8aa02477783fb1d0f.html" target="_blank">Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro</a></td><td><a href="https://arxiv.org/pdf/1701.07717.pdf" target="_blank">[pdf]</a></td><td>2017 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>72a155c987816ae81c858fddbd6beab656d86220</td><td>europersons</td><td>EuroCity Persons</td><td><a href="papers/72a155c987816ae81c858fddbd6beab656d86220.html" target="_blank">The EuroCity Persons Dataset: A Novel Benchmark for Object Detection</a></td><td><a href="https://arxiv.org/pdf/1805.07193.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>2</td><td>0</td><td>2</td><td>0</td><td>2</td><td>0</td></tr><tr><td>670637d0303a863c1548d5b19f705860a23e285c</td><td>face_tracer</td><td>FaceTracer</td><td><a href="papers/670637d0303a863c1548d5b19f705860a23e285c.html" target="_blank">Face swapping: automatically replacing faces in photographs</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>12ad3b5bbbf407f8e54ea692c07633d1a867c566</td><td>graz</td><td>Graz Pedestrian</td><td><a href="papers/12ad3b5bbbf407f8e54ea692c07633d1a867c566.html" target="_blank">Object recognition using segmentation for feature detection</a></td><td><span class="gray">[pdf]</a></td><td>Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.</td><td>edu</td><td>Inst. of Comput. Sci., Univ. of Leoben, Austria</td><td>Austria</td><td>47.38473720</td><td>15.09302010</td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>bd88bb2e4f351352d88ee7375af834360e223498</td><td>hda_plus</td><td>HDA+</td><td><a href="papers/bd88bb2e4f351352d88ee7375af834360e223498.html" target="_blank">HDA dataset-DRAFT 1 A Multi-camera video data set for research on High-Definition surveillance</a></td><td><a href="https://pdfs.semanticscholar.org/bd88/bb2e4f351352d88ee7375af834360e223498.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>2</td><td>0</td><td>2</td><td>0</td><td>1</td><td>2</td></tr><tr><td>0ab7cff2ccda7269b73ff6efd9d37e1318f7db25</td><td>ibm_dif</td><td>IBM Diversity in Faces</td><td><a href="papers/0ab7cff2ccda7269b73ff6efd9d37e1318f7db25.html" target="_blank">Facial Coding Scheme Reference 1 Craniofacial Distances</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>066d71fcd997033dce4ca58df924397dfe0b5fd1</td><td>ifdb</td><td>IFDB</td><td><a href="papers/066d71fcd997033dce4ca58df924397dfe0b5fd1.html" target="_blank">Iranian Face Database and Evaluation with a New Detection Algorithm</a></td><td><a href="https://pdfs.semanticscholar.org/066d/71fcd997033dce4ca58df924397dfe0b5fd1.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>21d9d0deed16f0ad62a4865e9acf0686f4f15492</td><td>images_of_groups</td><td>Images of Groups</td><td><a href="papers/21d9d0deed16f0ad62a4865e9acf0686f4f15492.html" target="_blank">Understanding images of groups of people</a></td><td><span class="gray">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>Carnegie Mellon University</td><td>United States</td><td>40.44416190</td><td>-79.94272826</td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>140438a77a771a8fb656b39a78ff488066eb6b50</td><td>lfpw</td><td>LFPW</td><td><a href="papers/140438a77a771a8fb656b39a78ff488066eb6b50.html" target="_blank">Localizing Parts of Faces Using a Consensus of Exemplars</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>079a0a3bf5200994e1f972b1b9197bf2f90e87d4</td><td>mit_cbcl</td><td>MIT CBCL</td><td><a href="papers/079a0a3bf5200994e1f972b1b9197bf2f90e87d4.html" target="_blank">Component-Based Face Recognition with 3D Morphable Models</a></td><td><span class="gray">[pdf]</a></td><td>2004 Conference on Computer Vision and Pattern Recognition Workshop</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>2fda164863a06a92d3a910b96eef927269aeb730</td><td>names_and_faces</td><td>News Dataset</td><td><a href="papers/2fda164863a06a92d3a910b96eef927269aeb730.html" target="_blank">Names and faces in the news</a></td><td><span class="gray">[pdf]</a></td><td>Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>d3200d49a19a4a4e4e9745ee39649b65d80c834b</td><td>scut_head</td><td>SCUT HEAD</td><td><a href="papers/d3200d49a19a4a4e4e9745ee39649b65d80c834b.html" target="_blank">Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture</a></td><td><a href="https://arxiv.org/pdf/1803.09256.pdf" target="_blank">[pdf]</a></td><td>2018 24th International Conference on Pattern Recognition (ICPR)</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>8990cdce3f917dad622e43e033db686b354d057c</td><td>tiny_faces</td><td>TinyFace</td><td><a href="papers/8990cdce3f917dad622e43e033db686b354d057c.html" target="_blank">Low-Resolution Face Recognition</a></td><td><a href="https://arxiv.org/pdf/1811.08965.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>6ad5a38df8dd4cdddd74f31996ce096d41219f72</td><td>tud_brussels</td><td>TUD-Brussels</td><td><a href="papers/6ad5a38df8dd4cdddd74f31996ce096d41219f72.html" target="_blank">Multi-cue onboard pedestrian detection</a></td><td><span class="gray">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>6ad5a38df8dd4cdddd74f31996ce096d41219f72</td><td>tud_motionpairs</td><td>TUD-Motionparis</td><td><a href="papers/6ad5a38df8dd4cdddd74f31996ce096d41219f72.html" target="_blank">Multi-cue onboard pedestrian detection</a></td><td><span class="gray">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>01959ef569f74c286956024866c1d107099199f7</td><td>vqa</td><td>VQA</td><td><a href="papers/01959ef569f74c286956024866c1d107099199f7.html" target="_blank">VQA: Visual Question Answering</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>9b9bf5e623cb8af7407d2d2d857bc3f1b531c182</td><td>who_goes_there</td><td>WGT</td><td><a href="papers/9b9bf5e623cb8af7407d2d2d857bc3f1b531c182.html" target="_blank">Who goes there?: approaches to mapping facial appearance diversity</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td>edu</td><td>University of Kentucky</td><td>United States</td><td>38.03337420</td><td>-84.50177580</td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>36bccfb2ad847096bc76777e544f305813cd8f5b</td><td>wildtrack</td><td>WildTrack</td><td><a href="papers/36bccfb2ad847096bc76777e544f305813cd8f5b.html" target="_blank">WILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection</a></td><td><a href="http://openaccess.thecvf.com/content_cvpr_2018/Supplemental/1562-supp.pdf" target="_blank">[pdf]</a></td><td>2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr></table></body></html>
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-<!doctype html><html><head><meta charset='utf-8'><title>All Papers</title><link rel='stylesheet' href='reports.css'></head><body><h2>All Papers</h2><table border='1' cellpadding='3' cellspacing='3'><th>Paper ID</th><th>Megapixels Key</th><th>Megapixels Name</th><th>Report Link</th><th>PDF Link</th><th>Journal</th><th>Type</th><th>Address</th><th>Country</th><th>Lat</th><th>Lng</th><th>Coverage</th><th>Total Citations</th><th>Geocoded Citations</th><th>Unknown Citations</th><th>Empty Citations</th><th>With PDF</th><th>With DOI</th><tr><td>3325860c0c82a93b2eac654f5324dd6a776f609e</td><td>mpii_human_pose</td><td>MPII Human Pose</td><td><a href="papers/3325860c0c82a93b2eac654f5324dd6a776f609e.html" target="_blank">2D Human Pose Estimation: New Benchmark and State of the Art Analysis</a></td><td><span class="gray">[pdf]</a></td><td>2014 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>387</td><td>251</td><td>136</td><td>21</td><td>291</td><td>96</td></tr><tr><td>e4754afaa15b1b53e70743880484b8d0736990ff</td><td>fiw_300</td><td>300-W</td><td><a href="papers/e4754afaa15b1b53e70743880484b8d0736990ff.html" target="_blank">300 Faces In-The-Wild Challenge: database and results</a></td><td><a href="http://ibug.doc.ic.ac.uk/media/uploads/documents/1-s2.0-s0262885616000147-main.pdf" target="_blank">[pdf]</a></td><td>Image Vision Comput.</td><td>edu</td><td>Imperial College London</td><td>United Kingdom</td><td>51.49887085</td><td>-0.17560797</td><td>71%</td><td>129</td><td>92</td><td>37</td><td>6</td><td>74</td><td>55</td></tr><tr><td>044d9a8c61383312cdafbcc44b9d00d650b21c70</td><td>fiw_300</td><td>300-W</td><td><a href="papers/044d9a8c61383312cdafbcc44b9d00d650b21c70.html" target="_blank">300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision Workshops</td><td></td><td></td><td></td><td></td><td></td><td>79%</td><td>323</td><td>255</td><td>68</td><td>15</td><td>208</td><td>120</td></tr><tr><td>2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e</td><td>3dpes</td><td>3DPeS</td><td><a href="papers/2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e.html" target="_blank">3DPeS: 3D people dataset for surveillance and forensics</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>133</td><td>82</td><td>51</td><td>9</td><td>73</td><td>58</td></tr><tr><td>a40f9bfd3c45658ee8da70e1f2dfbe1f0c744d43</td><td>4dfab</td><td>4DFAB</td><td><a href="papers/a40f9bfd3c45658ee8da70e1f2dfbe1f0c744d43.html" target="_blank">4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications</a></td><td><a href="https://arxiv.org/pdf/1712.01443.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>25%</td><td>4</td><td>1</td><td>3</td><td>0</td><td>2</td><td>2</td></tr><tr><td>31b58ced31f22eab10bd3ee2d9174e7c14c27c01</td><td>tiny_images</td><td>Tiny Images</td><td><a href="papers/31b58ced31f22eab10bd3ee2d9174e7c14c27c01.html" target="_blank">80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>999</td><td>574</td><td>425</td><td>89</td><td>644</td><td>337</td></tr><tr><td>d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae</td><td>b3d_ac</td><td>B3D(AC)</td><td><a href="papers/d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae.html" target="_blank">A 3-D Audio-Visual Corpus of Affective Communication</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Multimedia</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>42</td><td>23</td><td>19</td><td>2</td><td>26</td><td>15</td></tr><tr><td>4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b38461</td><td>3dddb_unconstrained</td><td>3D Dynamic</td><td><a href="papers/4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b38461.html" target="_blank">A 3D Dynamic Database for Unconstrained Face Recognition</a></td><td><a href="https://pdfs.semanticscholar.org/4d4b/b462c9f1d4e4ab1e4aa6a75cc0bc71b38461.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>2</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td></tr><tr><td>639937b3a1b8bded3f7e9a40e85bd3770016cf3c</td><td>bfm</td><td>BFM</td><td><a href="papers/639937b3a1b8bded3f7e9a40e85bd3770016cf3c.html" target="_blank">A 3D Face Model for Pose and Illumination Invariant Face Recognition</a></td><td><span class="gray">[pdf]</a></td><td>2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>343</td><td>194</td><td>149</td><td>23</td><td>223</td><td>114</td></tr><tr><td>cc589c499dcf323fe4a143bbef0074c3e31f9b60</td><td>bu_3dfe</td><td>BU-3DFE</td><td><a href="papers/cc589c499dcf323fe4a143bbef0074c3e31f9b60.html" target="_blank">A 3D facial expression database for facial behavior research</a></td><td><span class="gray">[pdf]</a></td><td>7th International Conference on Automatic Face and Gesture Recognition (FGR06)</td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>588</td><td>316</td><td>271</td><td>44</td><td>306</td><td>282</td></tr><tr><td>22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b</td><td>saivt</td><td>SAIVT SoftBio</td><td><a href="papers/22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b.html" target="_blank">A Database for Person Re-Identification in Multi-Camera Surveillance Networks</a></td><td><span class="gray">[pdf]</a></td><td>2012 International Conference on Digital Image Computing Techniques and Applications (DICTA)</td><td></td><td></td><td></td><td></td><td></td><td>58%</td><td>65</td><td>38</td><td>27</td><td>6</td><td>45</td><td>20</td></tr><tr><td>070de852bc6eb275d7ca3a9cdde8f6be8795d1a3</td><td>d3dfacs</td><td>D3DFACS</td><td><a href="papers/070de852bc6eb275d7ca3a9cdde8f6be8795d1a3.html" target="_blank">A FACS valid 3D dynamic action unit database with applications to 3D dynamic morphable facial modeling</a></td><td><span class="gray">[pdf]</a></td><td>2011 International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>50</td><td>26</td><td>24</td><td>5</td><td>31</td><td>18</td></tr><tr><td>563c940054e4b456661762c1ab858e6f730c3159</td><td>data_61</td><td>Data61 Pedestrian</td><td><a href="papers/563c940054e4b456661762c1ab858e6f730c3159.html" target="_blank">A Multi-modal Graphical Model for Scene Analysis</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE Winter Conference on Applications of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>8</td><td>4</td><td>4</td><td>0</td><td>5</td><td>3</td></tr><tr><td>221c18238b829c12b911706947ab38fd017acef7</td><td>rap_pedestrian</td><td>RAP</td><td><a href="papers/221c18238b829c12b911706947ab38fd017acef7.html" target="_blank">A Richly Annotated Dataset for Pedestrian Attribute Recognition</a></td><td><a href="https://arxiv.org/pdf/1603.07054.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>69%</td><td>26</td><td>18</td><td>8</td><td>0</td><td>16</td><td>10</td></tr><tr><td>013909077ad843eb6df7a3e8e290cfd5575999d2</td><td>fiw_300</td><td>300-W</td><td><a href="papers/013909077ad843eb6df7a3e8e290cfd5575999d2.html" target="_blank">A Semi-automatic Methodology for Facial Landmark Annotation</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops</td><td></td><td></td><td></td><td></td><td></td><td>80%</td><td>184</td><td>148</td><td>36</td><td>8</td><td>120</td><td>67</td></tr><tr><td>3b4ec8af470948a72a6ed37a9fd226719a874ebc</td><td>sdu_vid</td><td>SDU-VID</td><td><a href="papers/3b4ec8af470948a72a6ed37a9fd226719a874ebc.html" target="_blank">A Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>66%</td><td>95</td><td>63</td><td>32</td><td>6</td><td>50</td><td>45</td></tr><tr><td>6403117f9c005ae81f1e8e6d1302f4a045e3d99d</td><td>alert_airport</td><td>ALERT Airport</td><td><a href="papers/6403117f9c005ae81f1e8e6d1302f4a045e3d99d.html" target="_blank">A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>45%</td><td>20</td><td>9</td><td>11</td><td>0</td><td>9</td><td>11</td></tr><tr><td>0d3bb75852098b25d90f31d2f48fd0cb4944702b</td><td>face_scrub</td><td>FaceScrub</td><td><a href="papers/0d3bb75852098b25d90f31d2f48fd0cb4944702b.html" target="_blank">A data-driven approach to cleaning large face datasets</a></td><td><span class="gray">[pdf]</a></td><td>2014 IEEE International Conference on Image Processing (ICIP)</td><td></td><td></td><td></td><td></td><td></td><td>64%</td><td>138</td><td>89</td><td>49</td><td>1</td><td>95</td><td>41</td></tr><tr><td>b91f54e1581fbbf60392364323d00a0cd43e493c</td><td>bp4d_spontanous</td><td>BP4D-Spontanous</td><td><a href="papers/b91f54e1581fbbf60392364323d00a0cd43e493c.html" target="_blank">A high-resolution spontaneous 3D dynamic facial expression database</a></td><td><span class="gray">[pdf]</a></td><td>2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)</td><td>edu</td><td>SUNY Binghamton</td><td>United States</td><td>42.08779975</td><td>-75.97066066</td><td>52%</td><td>154</td><td>80</td><td>74</td><td>6</td><td>80</td><td>75</td></tr><tr><td>8b56e33f33e582f3e473dba573a16b598ed9bcdc</td><td>fei</td><td>FEI</td><td><a href="papers/8b56e33f33e582f3e473dba573a16b598ed9bcdc.html" target="_blank">A new ranking method for principal components analysis and its application to face image analysis</a></td><td><span class="gray">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>169</td><td>93</td><td>76</td><td>6</td><td>69</td><td>102</td></tr><tr><td>2624d84503bc2f8e190e061c5480b6aa4d89277a</td><td>afew_va</td><td>AFEW-VA</td><td><a href="papers/2624d84503bc2f8e190e061c5480b6aa4d89277a.html" target="_blank">AFEW-VA database for valence and arousal estimation in-the-wild</a></td><td><a href="https://ibug.doc.ic.ac.uk/media/uploads/documents/afew-va.pdf" target="_blank">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>18</td><td>9</td><td>9</td><td>0</td><td>12</td><td>5</td></tr><tr><td>2ad0ee93d029e790ebb50574f403a09854b65b7e</td><td>yale_faces</td><td>YaleFaces</td><td><a href="papers/2ad0ee93d029e790ebb50574f403a09854b65b7e.html" target="_blank">Acquiring linear subspaces for face recognition under variable lighting</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>999</td><td>554</td><td>445</td><td>94</td><td>495</td><td>491</td></tr><tr><td>57fe081950f21ca03b5b375ae3e84b399c015861</td><td>cvc_01_barcelona</td><td>CVC-01</td><td><a href="papers/57fe081950f21ca03b5b375ae3e84b399c015861.html" target="_blank">Adaptive Image Sampling and Windows Classification for On-board Pedestrian Detection</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>51%</td><td>47</td><td>24</td><td>23</td><td>1</td><td>23</td><td>24</td></tr><tr><td>758d7e1be64cc668c59ef33ba8882c8597406e53</td><td>affectnet</td><td>AffectNet</td><td><a href="papers/758d7e1be64cc668c59ef33ba8882c8597406e53.html" target="_blank">AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild</a></td><td><a href="https://arxiv.org/pdf/1708.03985.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>37</td><td>23</td><td>14</td><td>0</td><td>25</td><td>11</td></tr><tr><td>47aeb3b82f54b5ae8142b4bdda7b614433e69b9a</td><td>am_fed</td><td>AM-FED</td><td><a href="papers/47aeb3b82f54b5ae8142b4bdda7b614433e69b9a.html" target="_blank">Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops</td><td></td><td></td><td></td><td></td><td></td><td>46%</td><td>83</td><td>38</td><td>45</td><td>6</td><td>43</td><td>39</td></tr><tr><td>1be498d4bbc30c3bfd0029114c784bc2114d67c0</td><td>adience</td><td>Adience</td><td><a href="papers/1be498d4bbc30c3bfd0029114c784bc2114d67c0.html" target="_blank">Age and Gender Estimation of Unfiltered Faces</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Information Forensics and Security</td><td>edu</td><td>Open University of Israel</td><td>Israel</td><td>32.77824165</td><td>34.99565673</td><td>87%</td><td>179</td><td>156</td><td>23</td><td>1</td><td>98</td><td>80</td></tr><tr><td>d818568838433a6d6831adde49a58cef05e0c89f</td><td>agedb</td><td>AgeDB</td><td><a href="papers/d818568838433a6d6831adde49a58cef05e0c89f.html" target="_blank">AgeDB: The First Manually Collected, In-the-Wild Age Database</a></td><td><a href="http://eprints.mdx.ac.uk/22044/1/agedb_kotsia.pdf" target="_blank">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</td><td>edu</td><td>Imperial College London</td><td>United Kingdom</td><td>51.49887085</td><td>-0.17560797</td><td>94%</td><td>18</td><td>17</td><td>1</td><td>0</td><td>14</td><td>3</td></tr><tr><td>a74251efa970b92925b89eeef50a5e37d9281ad0</td><td>aflw</td><td>AFLW</td><td><a href="papers/a74251efa970b92925b89eeef50a5e37d9281ad0.html" target="_blank">Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization</a></td><td><span class="gray">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td>edu</td><td>TU Graz</td><td>Austria</td><td>47.07071400</td><td>15.43950400</td><td>69%</td><td>318</td><td>218</td><td>100</td><td>27</td><td>211</td><td>107</td></tr><tr><td>2ce2560cf59db59ce313bbeb004e8ce55c5ce928</td><td>texas_3dfrd</td><td>Texas 3DFRD</td><td><a href="papers/2ce2560cf59db59ce313bbeb004e8ce55c5ce928.html" target="_blank">Anthropometric 3D Face Recognition</a></td><td><a href="http://live.ece.utexas.edu/publications/2010/sg_ijcv_june10.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>63%</td><td>91</td><td>57</td><td>34</td><td>5</td><td>60</td><td>31</td></tr><tr><td>633c851ebf625ad7abdda2324e9de093cf623141</td><td>appa_real</td><td>APPA-REAL</td><td><a href="papers/633c851ebf625ad7abdda2324e9de093cf623141.html" target="_blank">Apparent and Real Age Estimation in Still Images with Deep Residual Regressors on Appa-Real Database</a></td><td><span class="gray">[pdf]</a></td><td>2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017)</td><td></td><td></td><td></td><td></td><td></td><td>70%</td><td>10</td><td>7</td><td>3</td><td>0</td><td>8</td><td>3</td></tr><tr><td>0df0d1adea39a5bef318b74faa37de7f3e00b452</td><td>mpii_gaze</td><td>MPIIGaze</td><td><a href="papers/0df0d1adea39a5bef318b74faa37de7f3e00b452.html" target="_blank">Appearance-based gaze estimation in the wild</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>73%</td><td>149</td><td>109</td><td>40</td><td>3</td><td>94</td><td>54</td></tr><tr><td>759a3b3821d9f0e08e0b0a62c8b693230afc3f8d</td><td>pubfig</td><td>PubFig</td><td><a href="papers/759a3b3821d9f0e08e0b0a62c8b693230afc3f8d.html" target="_blank">Attribute and simile classifiers for face verification</a></td><td><span class="gray">[pdf]</a></td><td>2009 IEEE 12th International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>64%</td><td>914</td><td>583</td><td>331</td><td>47</td><td>586</td><td>316</td></tr><tr><td>faf40ce28857aedf183e193486f5b4b0a8c478a2</td><td>iit_dehli_ear</td><td>IIT Dehli Ear</td><td><a href="papers/faf40ce28857aedf183e193486f5b4b0a8c478a2.html" target="_blank">Automated Human Identification Using Ear Imaging</a></td><td><a href="https://pdfs.semanticscholar.org/faf4/0ce28857aedf183e193486f5b4b0a8c478a2.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>80</td><td>40</td><td>40</td><td>6</td><td>35</td><td>44</td></tr><tr><td>2160788824c4c29ffe213b2cbeb3f52972d73f37</td><td>3d_rma</td><td>3D-RMA</td><td><a href="papers/2160788824c4c29ffe213b2cbeb3f52972d73f37.html" target="_blank">Automatic 3D face authentication</a></td><td><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.31.9190&rep=rep1&type=pdf" target="_blank">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>100</td><td>54</td><td>46</td><td>8</td><td>63</td><td>36</td></tr><tr><td>213a579af9e4f57f071b884aa872651372b661fd</td><td>bbc_pose</td><td>BBC Pose</td><td><a href="papers/213a579af9e4f57f071b884aa872651372b661fd.html" target="_blank">Automatic and Efficient Human Pose Estimation for Sign Language Videos</a></td><td><a href="http://tomas.pfister.fi/files/charles13ijcv.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>26</td><td>17</td><td>9</td><td>1</td><td>16</td><td>11</td></tr><tr><td>fcc6fe6007c322641796cb8792718641856a22a7</td><td>miw</td><td>MIW</td><td><a href="papers/fcc6fe6007c322641796cb8792718641856a22a7.html" target="_blank">Automatic facial makeup detection with application in face recognition</a></td><td><span class="gray">[pdf]</a></td><td>2013 International Conference on Biometrics (ICB)</td><td>edu</td><td>West Virginia University</td><td>United States</td><td>39.65404635</td><td>-79.96475355</td><td>71%</td><td>49</td><td>35</td><td>14</td><td>1</td><td>19</td><td>29</td></tr><tr><td>fcc6fe6007c322641796cb8792718641856a22a7</td><td>youtube_makeup</td><td>YMU</td><td><a href="papers/fcc6fe6007c322641796cb8792718641856a22a7.html" target="_blank">Automatic facial makeup detection with application in face recognition</a></td><td><span class="gray">[pdf]</a></td><td>2013 International Conference on Biometrics (ICB)</td><td>edu</td><td>West Virginia University</td><td>United States</td><td>39.65404635</td><td>-79.96475355</td><td>71%</td><td>49</td><td>35</td><td>14</td><td>1</td><td>19</td><td>29</td></tr><tr><td>0a85bdff552615643dd74646ac881862a7c7072d</td><td>pipa</td><td>PIPA</td><td><a href="papers/0a85bdff552615643dd74646ac881862a7c7072d.html" target="_blank">Beyond frontal faces: Improving Person Recognition using multiple cues</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>company</td><td>Facebook</td><td>United States</td><td>37.39367170</td><td>-122.08072620</td><td>91%</td><td>54</td><td>49</td><td>4</td><td>1</td><td>41</td><td>12</td></tr><tr><td>2acf7e58f0a526b957be2099c10aab693f795973</td><td>bosphorus</td><td>The Bosphorus</td><td><a href="papers/2acf7e58f0a526b957be2099c10aab693f795973.html" target="_blank">Bosphorus Database for 3D Face Analysis</a></td><td><a href="https://pdfs.semanticscholar.org/4254/fbba3846008f50671edc9cf70b99d7304543.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>56%</td><td>352</td><td>198</td><td>154</td><td>17</td><td>162</td><td>188</td></tr><tr><td>37d6f0eb074d207b53885bd2eb78ccc8a04be597</td><td>vmu</td><td>VMU</td><td><a href="papers/37d6f0eb074d207b53885bd2eb78ccc8a04be597.html" target="_blank">Can facial cosmetics affect the matching accuracy of face recognition systems?</a></td><td><span class="gray">[pdf]</a></td><td>2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td>edu</td><td>West Virginia University</td><td>United States</td><td>39.65404635</td><td>-79.96475355</td><td>62%</td><td>53</td><td>33</td><td>20</td><td>0</td><td>19</td><td>31</td></tr><tr><td>37d6f0eb074d207b53885bd2eb78ccc8a04be597</td><td>youtube_makeup</td><td>YMU</td><td><a href="papers/37d6f0eb074d207b53885bd2eb78ccc8a04be597.html" target="_blank">Can facial cosmetics affect the matching accuracy of face recognition systems?</a></td><td><span class="gray">[pdf]</a></td><td>2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td>edu</td><td>West Virginia University</td><td>United States</td><td>39.65404635</td><td>-79.96475355</td><td>62%</td><td>53</td><td>33</td><td>20</td><td>0</td><td>19</td><td>31</td></tr><tr><td>8d5998cd984e7cce307da7d46f155f9db99c6590</td><td>chalearn</td><td>ChaLearn</td><td><a href="papers/8d5998cd984e7cce307da7d46f155f9db99c6590.html" target="_blank">ChaLearn looking at people: A review of events and resources</a></td><td><span class="gray">[pdf]</a></td><td>2017 International Joint Conference on Neural Networks (IJCNN)</td><td></td><td></td><td></td><td></td><td></td><td>69%</td><td>13</td><td>9</td><td>4</td><td>1</td><td>8</td><td>4</td></tr><tr><td>2bf8541199728262f78d4dced6fb91479b39b738</td><td>clothing_co_parsing</td><td>CCP</td><td><a href="papers/2bf8541199728262f78d4dced6fb91479b39b738.html" target="_blank">Clothing Co-parsing by Joint Image Segmentation and Labeling</a></td><td><span class="gray">[pdf]</a></td><td>2014 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>70%</td><td>60</td><td>42</td><td>18</td><td>0</td><td>34</td><td>28</td></tr><tr><td>22ad2c8c0f4d6aa4328b38d894b814ec22579761</td><td>gallagher</td><td>Gallagher</td><td><a href="papers/22ad2c8c0f4d6aa4328b38d894b814ec22579761.html" target="_blank">Clothing cosegmentation for recognizing people</a></td><td><span class="gray">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>Carnegie Mellon University</td><td>United States</td><td>40.44416190</td><td>-79.94272826</td><td>65%</td><td>178</td><td>116</td><td>62</td><td>7</td><td>100</td><td>86</td></tr><tr><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td><td>leeds_sports_pose</td><td>Leeds Sports Pose</td><td><a href="papers/4b1d23d17476fcf78f4cbadf69fb130b1aa627c0.html" target="_blank">Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</a></td><td><a href="http://www.bmva.org/bmvc/2010/conference/paper12/abstract12.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>285</td><td>185</td><td>100</td><td>13</td><td>197</td><td>93</td></tr><tr><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td><td>stickmen_buffy</td><td>Buffy Stickmen</td><td><a href="papers/4b1d23d17476fcf78f4cbadf69fb130b1aa627c0.html" target="_blank">Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</a></td><td><a href="http://www.bmva.org/bmvc/2010/conference/paper12/abstract12.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>285</td><td>185</td><td>100</td><td>13</td><td>197</td><td>93</td></tr><tr><td>45c31cde87258414f33412b3b12fc5bec7cb3ba9</td><td>jaffe</td><td>JAFFE</td><td><a href="papers/45c31cde87258414f33412b3b12fc5bec7cb3ba9.html" target="_blank">Coding Facial Expressions with Gabor Wavelets</a></td><td><a href="http://physics.lbl.gov/patrecog/images/Facerecog_gabor.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>899</td><td>508</td><td>391</td><td>51</td><td>431</td><td>451</td></tr><tr><td>b1f4423c227fa37b9680787be38857069247a307</td><td>afew_va</td><td>AFEW-VA</td><td><a href="papers/b1f4423c227fa37b9680787be38857069247a307.html" target="_blank">Collecting Large, Richly Annotated Facial-Expression Databases from Movies</a></td><td><span class="gray">[pdf]</a></td><td>IEEE MultiMedia</td><td>edu</td><td>Australian National University</td><td>Australia</td><td>-35.27769990</td><td>149.11852700</td><td>64%</td><td>181</td><td>115</td><td>66</td><td>8</td><td>87</td><td>97</td></tr><tr><td>7f4040b482d16354d5938c1d1b926b544652bf5b</td><td>nova_emotions</td><td>Novaemötions Dataset</td><td><a href="papers/7f4040b482d16354d5938c1d1b926b544652bf5b.html" target="_blank">Competitive affective gaming: winning with a smile</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Universidade NOVA de Lisboa, Caparica, Portugal</td><td>Portugal</td><td>38.66096400</td><td>-9.20581300</td><td>78%</td><td>9</td><td>7</td><td>2</td><td>0</td><td>5</td><td>4</td></tr><tr><td>079a0a3bf5200994e1f972b1b9197bf2f90e87d4</td><td>mit_cbcl</td><td>MIT CBCL</td><td><a href="papers/079a0a3bf5200994e1f972b1b9197bf2f90e87d4.html" target="_blank">Component-Based Face Recognition with 3D Morphable Models</a></td><td><span class="gray">[pdf]</a></td><td>2004 Conference on Computer Vision and Pattern Recognition Workshop</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>23fc83c8cfff14a16df7ca497661264fc54ed746</td><td>cohn_kanade</td><td>CK</td><td><a href="papers/23fc83c8cfff14a16df7ca497661264fc54ed746.html" target="_blank">Comprehensive Database for Facial Expression Analysis</a></td><td><a href="https://pdfs.semanticscholar.org/23fc/83c8cfff14a16df7ca497661264fc54ed746.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>999</td><td>553</td><td>446</td><td>69</td><td>540</td><td>439</td></tr><tr><td>09d78009687bec46e70efcf39d4612822e61cb8c</td><td>raid</td><td>RAiD</td><td><a href="papers/09d78009687bec46e70efcf39d4612822e61cb8c.html" target="_blank">Consistent Re-identification in a Camera Network</a></td><td><a href="https://pdfs.semanticscholar.org/c27f/099e6e7e3f7f9979cbe9e0a5175fc5848ea0.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>49</td><td>35</td><td>14</td><td>3</td><td>34</td><td>13</td></tr><tr><td>0ceda9dae8b9f322df65ca2ef02caca9758aec6f</td><td>casablanca</td><td>Casablanca</td><td><a href="papers/0ceda9dae8b9f322df65ca2ef02caca9758aec6f.html" target="_blank">Context-Aware CNNs for Person Head Detection</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>33</td><td>20</td><td>13</td><td>1</td><td>23</td><td>11</td></tr><tr><td>0ceda9dae8b9f322df65ca2ef02caca9758aec6f</td><td>hollywood_headset</td><td>HollywoodHeads</td><td><a href="papers/0ceda9dae8b9f322df65ca2ef02caca9758aec6f.html" target="_blank">Context-Aware CNNs for Person Head Detection</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>33</td><td>20</td><td>13</td><td>1</td><td>23</td><td>11</td></tr><tr><td>c06b13d0ec3f5c43e2782cd22542588e233733c3</td><td>nova_emotions</td><td>Novaemötions Dataset</td><td><a href="papers/c06b13d0ec3f5c43e2782cd22542588e233733c3.html" target="_blank">Crowdsourcing facial expressions for affective-interaction</a></td><td><span class="gray">[pdf]</a></td><td>Computer Vision and Image Understanding</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><td>8355d095d3534ef511a9af68a3b2893339e3f96b</td><td>imdb_wiki</td><td>IMDB-Wiki</td><td><a href="papers/8355d095d3534ef511a9af68a3b2893339e3f96b.html" target="_blank">DEX: Deep EXpectation of Apparent Age from a Single Image</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision Workshop (ICCVW)</td><td></td><td></td><td></td><td></td><td></td><td>77%</td><td>122</td><td>94</td><td>28</td><td>4</td><td>75</td><td>48</td></tr><tr><td>5a5f0287484f0d480fed1ce585dbf729586f0edc</td><td>disfa</td><td>DISFA</td><td><a href="papers/5a5f0287484f0d480fed1ce585dbf729586f0edc.html" target="_blank">DISFA: A Spontaneous Facial Action Intensity Database</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Affective Computing</td><td>edu</td><td>University of Denver</td><td>United States</td><td>39.67665410</td><td>-104.96220300</td><td>54%</td><td>184</td><td>100</td><td>84</td><td>17</td><td>96</td><td>89</td></tr><tr><td>10195a163ab6348eef37213a46f60a3d87f289c5</td><td>imdb_wiki</td><td>IMDB-Wiki</td><td><a href="papers/10195a163ab6348eef37213a46f60a3d87f289c5.html" target="_blank">Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks</a></td><td><a href="http://www.vision.ee.ethz.ch/en/publications/papers/articles/eth_biwi_01299.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td>edu</td><td>ETH Zurich</td><td>Switzerland</td><td>47.37631300</td><td>8.54766990</td><td>72%</td><td>145</td><td>104</td><td>41</td><td>9</td><td>93</td><td>51</td></tr><tr><td>162ea969d1929ed180cc6de9f0bf116993ff6e06</td><td>vgg_faces</td><td>VGG Face</td><td><a href="papers/162ea969d1929ed180cc6de9f0bf116993ff6e06.html" target="_blank">Deep Face Recognition</a></td><td><a href="https://pdfs.semanticscholar.org/f372/ab9b3270d4e4f6a0258c83c2736c3a5c0454.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>999</td><td>646</td><td>353</td><td>48</td><td>558</td><td>429</td></tr><tr><td>6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4</td><td>celeba</td><td>CelebA</td><td><a href="papers/6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4.html" target="_blank">Deep Learning Face Attributes in the Wild</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td>edu</td><td>Chinese University of Hong Kong</td><td>China</td><td>22.41626320</td><td>114.21093180</td><td>57%</td><td>919</td><td>526</td><td>392</td><td>61</td><td>694</td><td>201</td></tr><tr><td>18010284894ed0edcca74e5bf768ee2e15ef7841</td><td>deep_fashion</td><td>DeepFashion</td><td><a href="papers/18010284894ed0edcca74e5bf768ee2e15ef7841.html" target="_blank">DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>64%</td><td>176</td><td>112</td><td>64</td><td>2</td><td>113</td><td>62</td></tr><tr><td>6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3</td><td>cuhk_campus_03</td><td>CUHK03 Campus</td><td><a href="papers/6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3.html" target="_blank">DeepReID: Deep Filter Pairing Neural Network for Person Re-identification</a></td><td><span class="gray">[pdf]</a></td><td>2014 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>73%</td><td>568</td><td>413</td><td>155</td><td>19</td><td>320</td><td>235</td></tr><tr><td>13f06b08f371ba8b5d31c3e288b4deb61335b462</td><td>eth_andreas_ess</td><td>ETHZ Pedestrian</td><td><a href="papers/13f06b08f371ba8b5d31c3e288b4deb61335b462.html" target="_blank">Depth and Appearance for Mobile Scene Analysis</a></td><td><span class="gray">[pdf]</a></td><td>2007 IEEE 11th International Conference on Computer Vision</td><td>edu</td><td>ETH Zurich</td><td>Switzerland</td><td>47.37631300</td><td>8.54766990</td><td>62%</td><td>324</td><td>202</td><td>122</td><td>26</td><td>193</td><td>127</td></tr><tr><td>4946ba10a4d5a7d0a38372f23e6622bd347ae273</td><td>coco_action</td><td>COCO-a</td><td><a href="papers/4946ba10a4d5a7d0a38372f23e6622bd347ae273.html" target="_blank">Describing Common Human Visual Actions in Images</a></td><td><a href="https://arxiv.org/pdf/1506.02203.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>68%</td><td>25</td><td>17</td><td>8</td><td>0</td><td>23</td><td>2</td></tr><tr><td>7808937b46acad36e43c30ae4e9f3fd57462853d</td><td>bpad</td><td>BPAD</td><td><a href="papers/7808937b46acad36e43c30ae4e9f3fd57462853d.html" target="_blank">Describing people: A poselet-based approach to attribute classification</a></td><td><span class="gray">[pdf]</a></td><td>2011 International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>230</td><td>140</td><td>90</td><td>14</td><td>163</td><td>66</td></tr><tr><td>d3200d49a19a4a4e4e9745ee39649b65d80c834b</td><td>scut_head</td><td>SCUT HEAD</td><td><a href="papers/d3200d49a19a4a4e4e9745ee39649b65d80c834b.html" target="_blank">Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture</a></td><td><a href="https://arxiv.org/pdf/1803.09256.pdf" target="_blank">[pdf]</a></td><td>2018 24th International Conference on Pattern Recognition (ICPR)</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>9cc8cf0c7d7fa7607659921b6ff657e17e135ecc</td><td>mafa</td><td>MAsked FAces</td><td><a href="papers/9cc8cf0c7d7fa7607659921b6ff657e17e135ecc.html" target="_blank">Detecting Masked Faces in the Wild with LLE-CNNs</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>5</td><td>3</td><td>2</td><td>1</td><td>4</td><td>1</td></tr><tr><td>56ae6d94fc6097ec4ca861f0daa87941d1c10b70</td><td>cmdp</td><td>CMDP</td><td><a href="papers/56ae6d94fc6097ec4ca861f0daa87941d1c10b70.html" target="_blank">Distance Estimation of an Unknown Person from a Portrait</a></td><td><a href="http://authors.library.caltech.edu/49084/13/FaceDistanceEstimation_RONCHI.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>44%</td><td>9</td><td>4</td><td>5</td><td>0</td><td>6</td><td>3</td></tr><tr><td>84fe5b4ac805af63206012d29523a1e033bc827e</td><td>awe_ears</td><td>AWE Ears</td><td><a href="papers/84fe5b4ac805af63206012d29523a1e033bc827e.html" target="_blank">Ear Recognition: More Than a Survey</a></td><td><a href="https://arxiv.org/pdf/1611.06203.pdf" target="_blank">[pdf]</a></td><td>Neurocomputing</td><td></td><td></td><td></td><td></td><td></td><td>77%</td><td>26</td><td>20</td><td>6</td><td>0</td><td>10</td><td>16</td></tr><tr><td>133f01aec1534604d184d56de866a4bd531dac87</td><td>lfw</td><td>LFW</td><td><a href="papers/133f01aec1534604d184d56de866a4bd531dac87.html" target="_blank">Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>183</td><td>109</td><td>74</td><td>13</td><td>103</td><td>77</td></tr><tr><td>c900e0ad4c95948baaf0acd8449fde26f9b4952a</td><td>emotio_net</td><td>EmotioNet Database</td><td><a href="papers/c900e0ad4c95948baaf0acd8449fde26f9b4952a.html" target="_blank">EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>86</td><td>45</td><td>41</td><td>7</td><td>54</td><td>29</td></tr><tr><td>2161f6b7ee3c0acc81603b01dc0df689683577b9</td><td>large_scale_person_search</td><td>Large Scale Person Search</td><td><a href="papers/2161f6b7ee3c0acc81603b01dc0df689683577b9.html" target="_blank">End-to-End Deep Learning for Person Search</a></td><td><a href="https://pdfs.semanticscholar.org/2161/f6b7ee3c0acc81603b01dc0df689683577b9.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>70%</td><td>46</td><td>32</td><td>14</td><td>0</td><td>27</td><td>16</td></tr><tr><td>1bd1645a629f1b612960ab9bba276afd4cf7c666</td><td>brainwash</td><td>Brainwash</td><td><a href="papers/1bd1645a629f1b612960ab9bba276afd4cf7c666.html" target="_blank">End-to-End People Detection in Crowded Scenes</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>Stanford University</td><td>United States</td><td>37.43131385</td><td>-122.16936535</td><td>55%</td><td>42</td><td>23</td><td>19</td><td>1</td><td>19</td><td>19</td></tr><tr><td>6273b3491e94ea4dd1ce42b791d77bdc96ee73a8</td><td>viper</td><td>VIPeR</td><td><a href="papers/6273b3491e94ea4dd1ce42b791d77bdc96ee73a8.html" target="_blank">Evaluating Appearance Models for Recognition, Reacquisition, and Tracking</a></td><td><a href="https://pdfs.semanticscholar.org/7847/b1fbccadb780b655e72c66d3f9e93ddb880c.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>edu</td><td>University of California, Santa Cruz</td><td>United States</td><td>36.99158470</td><td>-122.05827710</td><td>67%</td><td>624</td><td>415</td><td>209</td><td>33</td><td>342</td><td>276</td></tr><tr><td>2258e01865367018ed6f4262c880df85b94959f8</td><td>mot</td><td>MOT</td><td><a href="papers/2258e01865367018ed6f4262c880df85b94959f8.html" target="_blank">Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics</a></td><td><a href="https://cvhci.anthropomatik.kit.edu/images/stories/msmmi/papers/eurasip2008.pdf" target="_blank">[pdf]</a></td><td>EURASIP J. Image and Video Processing</td><td></td><td></td><td></td><td></td><td></td><td>58%</td><td>632</td><td>366</td><td>264</td><td>44</td><td>358</td><td>264</td></tr><tr><td>9e5378e7b336c89735d3bb15cf67eff96f86d39a</td><td>precarious</td><td>Precarious</td><td><a href="papers/9e5378e7b336c89735d3bb15cf67eff96f86d39a.html" target="_blank">Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>36%</td><td>14</td><td>5</td><td>9</td><td>0</td><td>12</td><td>1</td></tr><tr><td>35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62</td><td>coco_qa</td><td>COCO QA</td><td><a href="papers/35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62.html" target="_blank">Exploring Models and Data for Image Question Answering</a></td><td><a href="https://arxiv.org/pdf/1505.02074.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>206</td><td>126</td><td>80</td><td>11</td><td>162</td><td>39</td></tr><tr><td>2cd7821fcf5fae53a185624f7eeda007434ae037</td><td>geofaces</td><td>GeoFaces</td><td><a href="papers/2cd7821fcf5fae53a185624f7eeda007434ae037.html" target="_blank">Exploring the geo-dependence of human face appearance</a></td><td><a href="http://cs.uky.edu/~jacobs/papers/islam2014faces.pdf" target="_blank">[pdf]</a></td><td>IEEE Winter Conference on Applications of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>88%</td><td>8</td><td>7</td><td>1</td><td>0</td><td>5</td><td>3</td></tr><tr><td>2cd7821fcf5fae53a185624f7eeda007434ae037</td><td>geofaces</td><td>GeoFaces</td><td><a href="papers/2cd7821fcf5fae53a185624f7eeda007434ae037.html" target="_blank">Exploring the geo-dependence of human face appearance</a></td><td><a href="http://cs.uky.edu/~jacobs/papers/islam2014faces.pdf" target="_blank">[pdf]</a></td><td>IEEE Winter Conference on Applications of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>88%</td><td>8</td><td>7</td><td>1</td><td>0</td><td>5</td><td>3</td></tr><tr><td>75da1df4ed319926c544eefe17ec8d720feef8c0</td><td>fddb</td><td>FDDB</td><td><a href="papers/75da1df4ed319926c544eefe17ec8d720feef8c0.html" target="_blank">FDDB: A benchmark for face detection in unconstrained settings</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>380</td><td>247</td><td>133</td><td>16</td><td>202</td><td>164</td></tr><tr><td>31de9b3dd6106ce6eec9a35991b2b9083395fd0b</td><td>feret</td><td>FERET</td><td><a href="papers/31de9b3dd6106ce6eec9a35991b2b9083395fd0b.html" target="_blank">FERET ( Face Recognition Technology ) Recognition Algorithm Development and Test Results</a></td><td><a href="https://pdfs.semanticscholar.org/31de/9b3dd6106ce6eec9a35991b2b9083395fd0b.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>75</td><td>39</td><td>36</td><td>5</td><td>54</td><td>20</td></tr><tr><td>0e986f51fe45b00633de9fd0c94d082d2be51406</td><td>afw</td><td>AFW</td><td><a href="papers/0e986f51fe45b00633de9fd0c94d082d2be51406.html" target="_blank">Face detection, pose estimation, and landmark localization in the wild</a></td><td><span class="gray">[pdf]</a></td><td>2012 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>999</td><td>709</td><td>290</td><td>35</td><td>576</td><td>422</td></tr><tr><td>560e0e58d0059259ddf86fcec1fa7975dee6a868</td><td>youtube_faces</td><td>YouTubeFaces</td><td><a href="papers/560e0e58d0059259ddf86fcec1fa7975dee6a868.html" target="_blank">Face recognition in unconstrained videos with matched background similarity</a></td><td><span class="gray">[pdf]</a></td><td>CVPR 2011</td><td>edu</td><td>Tel Aviv University</td><td>Israel</td><td>32.11198890</td><td>34.80459702</td><td>66%</td><td>509</td><td>338</td><td>170</td><td>23</td><td>294</td><td>216</td></tr><tr><td>670637d0303a863c1548d5b19f705860a23e285c</td><td>face_tracer</td><td>FaceTracer</td><td><a href="papers/670637d0303a863c1548d5b19f705860a23e285c.html" target="_blank">Face swapping: automatically replacing faces in photographs</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>6204776d31359d129a582057c2d788a14f8aadeb</td><td>youtube_celebrities</td><td>YouTube Celebrities</td><td><a href="papers/6204776d31359d129a582057c2d788a14f8aadeb.html" target="_blank">Face tracking and recognition with visual constraints in real-world videos</a></td><td><span class="gray">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>Rutgers University</td><td>United States</td><td>40.47913175</td><td>-74.43168868</td><td>57%</td><td>267</td><td>151</td><td>115</td><td>11</td><td>125</td><td>121</td></tr><tr><td>4c170a0dcc8de75587dae21ca508dab2f9343974</td><td>face_tracer</td><td>FaceTracer</td><td><a href="papers/4c170a0dcc8de75587dae21ca508dab2f9343974.html" target="_blank">FaceTracer: A Search Engine for Large Collections of Images with Faces</a></td><td><a href="https://pdfs.semanticscholar.org/73a8/1d311eedac8dea3ca24dc15b6990fa4a725e.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>64%</td><td>225</td><td>144</td><td>81</td><td>17</td><td>146</td><td>77</td></tr><tr><td>7ebb153704706e457ab57b432793d2b6e5d12592</td><td>vgg_celebs_in_places</td><td>CIP</td><td><a href="papers/7ebb153704706e457ab57b432793d2b6e5d12592.html" target="_blank">Faces in Places: compound query retrieval</a></td><td><a href="https://pdfs.semanticscholar.org/7ebb/153704706e457ab57b432793d2b6e5d12592.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>5</td><td>5</td><td>0</td><td>0</td><td>3</td><td>2</td></tr><tr><td>0ab7cff2ccda7269b73ff6efd9d37e1318f7db25</td><td>ibm_dif</td><td>IBM Diversity in Faces</td><td><a href="papers/0ab7cff2ccda7269b73ff6efd9d37e1318f7db25.html" target="_blank">Facial Coding Scheme Reference 1 Craniofacial Distances</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>8a3c5507237957d013a0fe0f082cab7f757af6ee</td><td>mafl</td><td>MAFL</td><td><a href="papers/8a3c5507237957d013a0fe0f082cab7f757af6ee.html" target="_blank">Facial Landmark Detection by Deep Multi-task Learning</a></td><td><a href="https://pdfs.semanticscholar.org/fcd7/1c18192928a2e0b264edd4d919ab2f8f652a.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>70%</td><td>407</td><td>283</td><td>124</td><td>16</td><td>252</td><td>153</td></tr><tr><td>8a3c5507237957d013a0fe0f082cab7f757af6ee</td><td>mtfl</td><td>MTFL</td><td><a href="papers/8a3c5507237957d013a0fe0f082cab7f757af6ee.html" target="_blank">Facial Landmark Detection by Deep Multi-task Learning</a></td><td><a href="https://pdfs.semanticscholar.org/fcd7/1c18192928a2e0b264edd4d919ab2f8f652a.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>70%</td><td>407</td><td>283</td><td>124</td><td>16</td><td>252</td><td>153</td></tr><tr><td>4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7</td><td>deep_fashion</td><td>DeepFashion</td><td><a href="papers/4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7.html" target="_blank">Fashion Landmark Detection in the Wild</a></td><td><a href="https://arxiv.org/pdf/1608.03049.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>73%</td><td>26</td><td>19</td><td>7</td><td>1</td><td>16</td><td>10</td></tr><tr><td>060820f110a72cbf02c14a6d1085bd6e1d994f6a</td><td>caltech_crp</td><td>Caltech CRP</td><td><a href="papers/060820f110a72cbf02c14a6d1085bd6e1d994f6a.html" target="_blank">Fine-grained classification of pedestrians in video: Benchmark and state of the art</a></td><td><a href="https://arxiv.org/pdf/1605.06177.pdf" target="_blank">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>47%</td><td>17</td><td>8</td><td>9</td><td>0</td><td>9</td><td>8</td></tr><tr><td>45e616093a92e5f1e61a7c6037d5f637aa8964af</td><td>malf</td><td>MALF</td><td><a href="papers/45e616093a92e5f1e61a7c6037d5f637aa8964af.html" target="_blank">Fine-grained evaluation on face detection in the wild</a></td><td><span class="gray">[pdf]</a></td><td>2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>17</td><td>12</td><td>5</td><td>0</td><td>12</td><td>5</td></tr><tr><td>1aad2da473888cb7ebc1bfaa15bfa0f1502ce005</td><td>jpl_pose</td><td>JPL-Interaction dataset</td><td><a href="papers/1aad2da473888cb7ebc1bfaa15bfa0f1502ce005.html" target="_blank">First-Person Activity Recognition: What Are They Doing to Me?</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>67%</td><td>148</td><td>99</td><td>49</td><td>7</td><td>105</td><td>43</td></tr><tr><td>7b92d1e53cc87f7a4256695de590098a2f30261e</td><td>appa_real</td><td>APPA-REAL</td><td><a href="papers/7b92d1e53cc87f7a4256695de590098a2f30261e.html" target="_blank">From Apparent to Real Age: Gender, Age, Ethnic, Makeup, and Expression Bias Analysis in Real Age Estimation</a></td><td><span class="gray">[pdf]</a></td><td>2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>774cbb45968607a027ae4729077734db000a1ec5</td><td>urban_tribes</td><td>Urban Tribes</td><td><a href="papers/774cbb45968607a027ae4729077734db000a1ec5.html" target="_blank">From Bikers to Surfers: Visual Recognition of Urban Tribes</a></td><td><a href="http://vision.cornell.edu/se3/wp-content/uploads/2014/09/utribes_bmvc13_final.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>67%</td><td>18</td><td>12</td><td>6</td><td>1</td><td>12</td><td>6</td></tr><tr><td>22f656d0f8426c84a33a267977f511f127bfd7f3</td><td>expw</td><td>ExpW</td><td><a href="papers/22f656d0f8426c84a33a267977f511f127bfd7f3.html" target="_blank">From Facial Expression Recognition to Interpersonal Relation Prediction</a></td><td><a href="https://arxiv.org/pdf/1609.06426.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>11</td><td>6</td><td>5</td><td>0</td><td>5</td><td>4</td></tr><tr><td>18c72175ddbb7d5956d180b65a96005c100f6014</td><td>yale_faces</td><td>YaleFaces</td><td><a href="papers/18c72175ddbb7d5956d180b65a96005c100f6014.html" target="_blank">From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose</a></td><td><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.147.1487&rep=rep1&type=pdf" target="_blank">[pdf]</a></td><td>IEEE Trans. Pattern Anal. Mach. Intell.</td><td></td><td></td><td></td><td></td><td></td><td>56%</td><td>999</td><td>561</td><td>438</td><td>66</td><td>498</td><td>462</td></tr><tr><td>06f02199690961ba52997cde1527e714d2b3bf8f</td><td>columbia_gaze</td><td>Columbia Gaze</td><td><a href="papers/06f02199690961ba52997cde1527e714d2b3bf8f.html" target="_blank">Gaze locking: passive eye contact detection for human-object interaction</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Columbia University</td><td>United States</td><td>40.84198360</td><td>-73.94368971</td><td>76%</td><td>79</td><td>60</td><td>19</td><td>0</td><td>49</td><td>34</td></tr><tr><td>18858cc936947fc96b5c06bbe3c6c2faa5614540</td><td>pilot_parliament</td><td>PPB</td><td><a href="papers/18858cc936947fc96b5c06bbe3c6c2faa5614540.html" target="_blank">Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification</a></td><td><a href="http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a-supp.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>53%</td><td>59</td><td>31</td><td>28</td><td>0</td><td>47</td><td>10</td></tr><tr><td>2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d</td><td>kin_face</td><td>UB KinFace</td><td><a href="papers/2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d.html" target="_blank">Genealogical face recognition based on UB KinFace database</a></td><td><span class="gray">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>SUNY Buffalo</td><td>United States</td><td>42.93362780</td><td>-78.88394479</td><td>55%</td><td>31</td><td>17</td><td>14</td><td>0</td><td>11</td><td>21</td></tr><tr><td>2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9</td><td>graz</td><td>Graz Pedestrian</td><td><a href="papers/2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9.html" target="_blank">Generic object recognition with boosting</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td>edu</td><td>TU Graz</td><td>Austria</td><td>47.07071400</td><td>15.43950400</td><td>53%</td><td>293</td><td>155</td><td>138</td><td>16</td><td>195</td><td>97</td></tr><tr><td>17b46e2dad927836c689d6787ddb3387c6159ece</td><td>geofaces</td><td>GeoFaces</td><td><a href="papers/17b46e2dad927836c689d6787ddb3387c6159ece.html" target="_blank">GeoFaceExplorer: exploring the geo-dependence of facial attributes</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>2</td><td>2</td><td>0</td><td>0</td><td>1</td><td>1</td></tr><tr><td>bd88bb2e4f351352d88ee7375af834360e223498</td><td>hda_plus</td><td>HDA+</td><td><a href="papers/bd88bb2e4f351352d88ee7375af834360e223498.html" target="_blank">HDA dataset-DRAFT 1 A Multi-camera video data set for research on High-Definition surveillance</a></td><td><a href="https://pdfs.semanticscholar.org/bd88/bb2e4f351352d88ee7375af834360e223498.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>2</td><td>0</td><td>2</td><td>0</td><td>1</td><td>2</td></tr><tr><td>a8d0b149c2eadaa02204d3e4356fbc8eccf3b315</td><td>hi4d_adsip</td><td>Hi4D-ADSIP</td><td><a href="papers/a8d0b149c2eadaa02204d3e4356fbc8eccf3b315.html" target="_blank">Hi4D-ADSIP 3-D dynamic facial articulation database</a></td><td><span class="gray">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>15</td><td>9</td><td>6</td><td>1</td><td>4</td><td>11</td></tr><tr><td>2d45cfd838016a6e39f6b766ffe85acd649440c7</td><td>mcgill</td><td>McGill Real World</td><td><a href="papers/2d45cfd838016a6e39f6b766ffe85acd649440c7.html" target="_blank">Hierarchical temporal graphical model for head pose estimation and subsequent attribute classification in real-world videos</a></td><td><span class="gray">[pdf]</a></td><td>Computer Vision and Image Understanding</td><td></td><td></td><td></td><td></td><td></td><td>75%</td><td>8</td><td>6</td><td>2</td><td>0</td><td>5</td><td>3</td></tr><tr><td>3cd40bfa1ff193a96bde0207e5140a399476466c</td><td>tvhi</td><td>TVHI</td><td><a href="papers/3cd40bfa1ff193a96bde0207e5140a399476466c.html" target="_blank">High Five: Recognising human interactions in TV shows</a></td><td><a href="https://pdfs.semanticscholar.org/3cd4/0bfa1ff193a96bde0207e5140a399476466c.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>98</td><td>56</td><td>42</td><td>10</td><td>66</td><td>28</td></tr><tr><td>04c2cda00e5536f4b1508cbd80041e9552880e67</td><td>hipsterwars</td><td>Hipsterwars</td><td><a href="papers/04c2cda00e5536f4b1508cbd80041e9552880e67.html" target="_blank">Hipster Wars: Discovering Elements of Fashion Styles</a></td><td><a href="https://pdfs.semanticscholar.org/04c2/cda00e5536f4b1508cbd80041e9552880e67.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>64%</td><td>95</td><td>61</td><td>34</td><td>4</td><td>59</td><td>35</td></tr><tr><td>10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5</td><td>inria_person</td><td>INRIA Pedestrian</td><td><a href="papers/10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5.html" target="_blank">Histograms of oriented gradients for human detection</a></td><td><span class="gray">[pdf]</a></td><td>2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)</td><td>edu</td><td>INRIA Rhone-Alps, Montbonnot, France</td><td>France</td><td>45.21788600</td><td>5.80736900</td><td>57%</td><td>999</td><td>572</td><td>427</td><td>42</td><td>419</td><td>509</td></tr><tr><td>041d3eedf5e45ce5c5229f0181c5c576ed1fafd6</td><td>ucf_selfie</td><td>UCF Selfie</td><td><a href="papers/041d3eedf5e45ce5c5229f0181c5c576ed1fafd6.html" target="_blank">How to Take a Good Selfie?</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>73%</td><td>11</td><td>8</td><td>3</td><td>0</td><td>7</td><td>5</td></tr><tr><td>44d23df380af207f5ac5b41459c722c87283e1eb</td><td>wider_attribute</td><td>WIDER Attribute</td><td><a href="papers/44d23df380af207f5ac5b41459c722c87283e1eb.html" target="_blank">Human Attribute Recognition by Deep Hierarchical Contexts</a></td><td><a href="https://pdfs.semanticscholar.org/8e28/07f2dd53b03a759e372e07f7191cae65c9fd.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>72%</td><td>18</td><td>13</td><td>5</td><td>0</td><td>14</td><td>4</td></tr><tr><td>44484d2866f222bbb9b6b0870890f9eea1ffb2d0</td><td>cuhk_campus_03</td><td>CUHK03 Campus</td><td><a href="papers/44484d2866f222bbb9b6b0870890f9eea1ffb2d0.html" target="_blank">Human Reidentification with Transferred Metric Learning</a></td><td><a href="http://www.ee.cuhk.edu.hk/~xgwang/papers/liZWaccv12.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>69%</td><td>280</td><td>194</td><td>86</td><td>9</td><td>139</td><td>137</td></tr><tr><td>f41c7bb02fc97d5fb9cadd7a49c3e558a1c58a44</td><td>pa_100k</td><td>PA-100K</td><td><a href="papers/f41c7bb02fc97d5fb9cadd7a49c3e558a1c58a44.html" target="_blank">HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>75%</td><td>55</td><td>41</td><td>14</td><td>0</td><td>36</td><td>17</td></tr><tr><td>57178b36c21fd7f4529ac6748614bb3374714e91</td><td>ijb_c</td><td>IJB-C</td><td><a href="papers/57178b36c21fd7f4529ac6748614bb3374714e91.html" target="_blank">IARPA Janus Benchmark - C: Face Dataset and Protocol</a></td><td><span class="gray">[pdf]</a></td><td>2018 International Conference on Biometrics (ICB)</td><td></td><td></td><td></td><td></td><td></td><td>79%</td><td>14</td><td>11</td><td>3</td><td>0</td><td>12</td><td>1</td></tr><tr><td>0cb2dd5f178e3a297a0c33068961018659d0f443</td><td>ijb_b</td><td>IJB-B</td><td><a href="papers/0cb2dd5f178e3a297a0c33068961018659d0f443.html" target="_blank">IARPA Janus Benchmark-B Face Dataset</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</td><td>edu</td><td>Michigan State University</td><td>United States</td><td>42.71856800</td><td>-84.47791571</td><td>63%</td><td>35</td><td>22</td><td>13</td><td>3</td><td>25</td><td>8</td></tr><tr><td>0297448f3ed948e136bb06ceff10eccb34e5bb77</td><td>ilids_mcts</td><td>i-LIDS Multiple-Camera</td><td><a href="papers/0297448f3ed948e136bb06ceff10eccb34e5bb77.html" target="_blank">Imagery Library for Intelligent Detection Systems (i-LIDS); A Standard for Testing Video Based Detection Systems</a></td><td><span class="gray">[pdf]</a></td><td>Proceedings 40th Annual 2006 International Carnahan Conference on Security Technology</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>35</td><td>20</td><td>15</td><td>2</td><td>21</td><td>14</td></tr><tr><td>7f23a4bb0c777dd72cca7665a5f370ac7980217e</td><td>duke_mtmc</td><td>Duke MTMC</td><td><a href="papers/7f23a4bb0c777dd72cca7665a5f370ac7980217e.html" target="_blank">Improving Person Re-identification by Attribute and Identity Learning</a></td><td><a href="https://arxiv.org/pdf/1703.07220.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>84%</td><td>87</td><td>73</td><td>14</td><td>0</td><td>43</td><td>42</td></tr><tr><td>55c40cbcf49a0225e72d911d762c27bb1c2d14aa</td><td>ifad</td><td>IFAD</td><td><a href="papers/55c40cbcf49a0225e72d911d762c27bb1c2d14aa.html" target="_blank">Indian Face Age Database: A Database for Face Recognition with Age Variation</a></td><td><a href="https://pdfs.semanticscholar.org/025e/4cf3fd3fdeced91e9373b56ee14af7ca432c.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>2</td><td>1</td><td>1</td><td>0</td><td>2</td><td>0</td></tr><tr><td>ca3e88d87e1344d076c964ea89d91a75c417f5ee</td><td>imfdb</td><td>IMFDB</td><td><a href="papers/ca3e88d87e1344d076c964ea89d91a75c417f5ee.html" target="_blank">Indian Movie Face Database: A benchmark for face recognition under wide variations</a></td><td><span class="gray">[pdf]</a></td><td>2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)</td><td>edu</td><td>BVBCET, Hubli, India</td><td>India</td><td>15.36883320</td><td>75.12137960</td><td>65%</td><td>17</td><td>11</td><td>6</td><td>0</td><td>11</td><td>5</td></tr><tr><td>95f12d27c3b4914e0668a268360948bce92f7db3</td><td>helen</td><td>Helen</td><td><a href="papers/95f12d27c3b4914e0668a268360948bce92f7db3.html" target="_blank">Interactive Facial Feature Localization</a></td><td><a href="https://pdfs.semanticscholar.org/95f1/2d27c3b4914e0668a268360948bce92f7db3.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>company</td><td>Adobe</td><td>United States</td><td>37.33077030</td><td>-121.89409510</td><td>85%</td><td>352</td><td>298</td><td>54</td><td>8</td><td>212</td><td>146</td></tr><tr><td>ad01687649d95cd5b56d7399a9603c4b8e2217d7</td><td>mrp_drone</td><td>MRP Drone</td><td><a href="papers/ad01687649d95cd5b56d7399a9603c4b8e2217d7.html" target="_blank">Investigating Open-World Person Re-identification Using a Drone</a></td><td><a href="https://pdfs.semanticscholar.org/ad01/687649d95cd5b56d7399a9603c4b8e2217d7.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>7</td><td>3</td><td>4</td><td>1</td><td>5</td><td>2</td></tr><tr><td>2f43b614607163abf41dfe5d17ef6749a1b61304</td><td>hrt_transgender</td><td>HRT Transgender</td><td><a href="papers/2f43b614607163abf41dfe5d17ef6749a1b61304.html" target="_blank">Investigating the Periocular-Based Face Recognition Across Gender Transformation</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Information Forensics and Security</td><td>edu</td><td>University of North Carolina at Wilmington</td><td>United States</td><td>34.22498270</td><td>-77.86907744</td><td>77%</td><td>13</td><td>10</td><td>3</td><td>0</td><td>6</td><td>8</td></tr><tr><td>066d71fcd997033dce4ca58df924397dfe0b5fd1</td><td>ifdb</td><td>IFDB</td><td><a href="papers/066d71fcd997033dce4ca58df924397dfe0b5fd1.html" target="_blank">Iranian Face Database and Evaluation with a New Detection Algorithm</a></td><td><a href="https://pdfs.semanticscholar.org/066d/71fcd997033dce4ca58df924397dfe0b5fd1.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>b71d1aa90dcbe3638888725314c0d56640c1fef1</td><td>ifdb</td><td>IFDB</td><td><a href="papers/b71d1aa90dcbe3638888725314c0d56640c1fef1.html" target="_blank">Iranian Face Database with age, pose and expression</a></td><td><span class="gray">[pdf]</a></td><td>2007 International Conference on Machine Vision</td><td>edu</td><td>Islamic Azad University</td><td>Iran</td><td>34.84529990</td><td>48.55962120</td><td>48%</td><td>23</td><td>11</td><td>12</td><td>2</td><td>14</td><td>9</td></tr><tr><td>137aa2f891d474fce1e7a1d1e9b3aefe21e22b34</td><td>hrt_transgender</td><td>HRT Transgender</td><td><a href="papers/137aa2f891d474fce1e7a1d1e9b3aefe21e22b34.html" target="_blank">Is the eye region more reliable than the face? A preliminary study of face-based recognition on a transgender dataset</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>7</td><td>4</td><td>3</td><td>1</td><td>3</td><td>5</td></tr><tr><td>0b440695c822a8e35184fb2f60dcdaa8a6de84ae</td><td>kinectface</td><td>KinectFaceDB</td><td><a href="papers/0b440695c822a8e35184fb2f60dcdaa8a6de84ae.html" target="_blank">KinectFaceDB: A Kinect Database for Face Recognition</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Systems, Man, and Cybernetics: Systems</td><td>edu</td><td>University of North Carolina at Chapel Hill</td><td>United States</td><td>35.91139710</td><td>-79.05045290</td><td>61%</td><td>82</td><td>50</td><td>32</td><td>6</td><td>28</td><td>52</td></tr><tr><td>4793f11fbca4a7dba898b9fff68f70d868e2497c</td><td>kin_face</td><td>UB KinFace</td><td><a href="papers/4793f11fbca4a7dba898b9fff68f70d868e2497c.html" target="_blank">Kinship Verification through Transfer Learning</a></td><td><a href="https://pdfs.semanticscholar.org/4793/f11fbca4a7dba898b9fff68f70d868e2497c.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>58%</td><td>71</td><td>41</td><td>30</td><td>2</td><td>29</td><td>42</td></tr><tr><td>2d3482dcff69c7417c7b933f22de606a0e8e42d4</td><td>lfw</td><td>LFW</td><td><a href="papers/2d3482dcff69c7417c7b933f22de606a0e8e42d4.html" target="_blank">Labeled Faces in the Wild : Updates and New Reporting Procedures</a></td><td><a href="https://pdfs.semanticscholar.org/2d34/82dcff69c7417c7b933f22de606a0e8e42d4.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>edu</td><td>University of Massachusetts</td><td>United States</td><td>42.38897850</td><td>-72.52869870</td><td>69%</td><td>123</td><td>85</td><td>38</td><td>3</td><td>71</td><td>51</td></tr><tr><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td><td>lfw</td><td>LFW</td><td><a href="papers/370b5757a5379b15e30d619e4d3fb9e8e13f3256.html" target="_blank">Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments</a></td><td><a href="https://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>63%</td><td>999</td><td>631</td><td>368</td><td>59</td><td>598</td><td>382</td></tr><tr><td>7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22</td><td>lfw</td><td>LFW</td><td><a href="papers/7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22.html" target="_blank">Labeled Faces in the Wild: A Survey</a></td><td><a href="https://pdfs.semanticscholar.org/7de6/e81d775e9cd7becbfd1bd685f4e2a5eebb22.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Stevens Institute of Technology</td><td>United States</td><td>40.74225200</td><td>-74.02709490</td><td>64%</td><td>109</td><td>70</td><td>39</td><td>7</td><td>66</td><td>43</td></tr><tr><td>0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e</td><td>lag</td><td>LAG</td><td><a href="papers/0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e.html" target="_blank">Large age-gap face verification by feature injection in deep networks</a></td><td><a href="https://arxiv.org/pdf/1602.06149.pdf" target="_blank">[pdf]</a></td><td>Pattern Recognition Letters</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>7</td><td>5</td><td>2</td><td>0</td><td>3</td><td>4</td></tr><tr><td>07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1</td><td>uccs</td><td>UCCS</td><td><a href="papers/07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1.html" target="_blank">Large scale unconstrained open set face database</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td>company</td><td>Securics Inc., Colorado Springs, CO</td><td>United States</td><td>38.83388160</td><td>-104.82136340</td><td>83%</td><td>6</td><td>5</td><td>1</td><td>0</td><td>4</td><td>2</td></tr><tr><td>4af89578ac237278be310f7660a408b03f12d603</td><td>geofaces</td><td>GeoFaces</td><td><a href="papers/4af89578ac237278be310f7660a408b03f12d603.html" target="_blank">Large-scale geo-facial image analysis</a></td><td><a href="https://pdfs.semanticscholar.org/3ede/3ed28329bf48fbd06438a69c4f855bef003f.pdf" target="_blank">[pdf]</a></td><td>EURASIP J. Image and Video Processing</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>6</td><td>6</td><td>0</td><td>0</td><td>4</td><td>2</td></tr><tr><td>a0fd85b3400c7b3e11122f44dc5870ae2de9009a</td><td>mafl</td><td>MAFL</td><td><a href="papers/a0fd85b3400c7b3e11122f44dc5870ae2de9009a.html" target="_blank">Learning Deep Representation for Face Alignment with Auxiliary Attributes</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>108</td><td>77</td><td>31</td><td>7</td><td>66</td><td>44</td></tr><tr><td>a0fd85b3400c7b3e11122f44dc5870ae2de9009a</td><td>mtfl</td><td>MTFL</td><td><a href="papers/a0fd85b3400c7b3e11122f44dc5870ae2de9009a.html" target="_blank">Learning Deep Representation for Face Alignment with Auxiliary Attributes</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>108</td><td>77</td><td>31</td><td>7</td><td>66</td><td>44</td></tr><tr><td>853bd61bc48a431b9b1c7cab10c603830c488e39</td><td>casia_webface</td><td>CASIA Webface</td><td><a href="papers/853bd61bc48a431b9b1c7cab10c603830c488e39.html" target="_blank">Learning Face Representation from Scratch</a></td><td><a href="https://arxiv.org/pdf/1411.7923.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td>edu</td><td>Chinese Academy of Sciences</td><td>China</td><td>40.00447950</td><td>116.37023800</td><td>71%</td><td>476</td><td>339</td><td>137</td><td>19</td><td>290</td><td>182</td></tr><tr><td>2a171f8d14b6b8735001a11c217af9587d095848</td><td>social_relation</td><td>Social Relation</td><td><a href="papers/2a171f8d14b6b8735001a11c217af9587d095848.html" target="_blank">Learning Social Relation Traits from Face Images</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>23</td><td>14</td><td>9</td><td>4</td><td>16</td><td>7</td></tr><tr><td>4e4746094bf60ee83e40d8597a6191e463b57f76</td><td>leeds_sports_pose_extended</td><td>Leeds Sports Pose Extended</td><td><a href="papers/4e4746094bf60ee83e40d8597a6191e463b57f76.html" target="_blank">Learning effective human pose estimation from inaccurate annotation</a></td><td><span class="gray">[pdf]</a></td><td>CVPR 2011</td><td>edu</td><td>University of Leeds</td><td>United Kingdom</td><td>53.80387185</td><td>-1.55245712</td><td>70%</td><td>169</td><td>118</td><td>51</td><td>8</td><td>108</td><td>65</td></tr><tr><td>287ddcb3db5562235d83aee318f318b8d5e43fb1</td><td>erce</td><td>ERCe</td><td><a href="papers/287ddcb3db5562235d83aee318f318b8d5e43fb1.html" target="_blank">Learning from Multiple Sources for Video Summarisation</a></td><td><a href="https://arxiv.org/pdf/1501.03069.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>7</td><td>4</td><td>3</td><td>0</td><td>4</td><td>3</td></tr><tr><td>287ddcb3db5562235d83aee318f318b8d5e43fb1</td><td>tisi</td><td>Times Square Intersection</td><td><a href="papers/287ddcb3db5562235d83aee318f318b8d5e43fb1.html" target="_blank">Learning from Multiple Sources for Video Summarisation</a></td><td><a href="https://arxiv.org/pdf/1501.03069.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>7</td><td>4</td><td>3</td><td>0</td><td>4</td><td>3</td></tr><tr><td>5981e6479c3fd4e31644db35d236bfb84ae46514</td><td>mot</td><td>MOT</td><td><a href="papers/5981e6479c3fd4e31644db35d236bfb84ae46514.html" target="_blank">Learning to associate: HybridBoosted multi-target tracker for crowded scene</a></td><td><span class="gray">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>University of Southern California</td><td>United States</td><td>34.02241490</td><td>-118.28634407</td><td>61%</td><td>326</td><td>200</td><td>125</td><td>22</td><td>190</td><td>137</td></tr><tr><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td><td>stickmen_buffy</td><td>Buffy Stickmen</td><td><a href="papers/6dd0597f8513dc100cd0bc1b493768cde45098a9.html" target="_blank">Learning to parse images of articulated bodies</a></td><td><a href="http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2006_899.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>369</td><td>227</td><td>141</td><td>32</td><td>237</td><td>131</td></tr><tr><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td><td>stickmen_pascal</td><td>Stickmen PASCAL</td><td><a href="papers/6dd0597f8513dc100cd0bc1b493768cde45098a9.html" target="_blank">Learning to parse images of articulated bodies</a></td><td><a href="http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2006_899.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>369</td><td>227</td><td>141</td><td>32</td><td>237</td><td>131</td></tr><tr><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td><td>stickmen_pascal</td><td>Stickmen PASCAL</td><td><a href="papers/6dd0597f8513dc100cd0bc1b493768cde45098a9.html" target="_blank">Learning to parse images of articulated bodies</a></td><td><a href="http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2006_899.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>369</td><td>227</td><td>141</td><td>32</td><td>237</td><td>131</td></tr><tr><td>28d4e027c7e90b51b7d8908fce68128d1964668a</td><td>megaface</td><td>MegaFace</td><td><a href="papers/28d4e027c7e90b51b7d8908fce68128d1964668a.html" target="_blank">Level Playing Field for Million Scale Face Recognition</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>University of Washington</td><td>United States</td><td>47.65432380</td><td>-122.30800894</td><td>72%</td><td>39</td><td>28</td><td>11</td><td>2</td><td>29</td><td>9</td></tr><tr><td>46a01565e6afe7c074affb752e7069ee3bf2e4ef</td><td>sdu_vid</td><td>SDU-VID</td><td><a href="papers/46a01565e6afe7c074affb752e7069ee3bf2e4ef.html" target="_blank">Local Descriptors Encoded by Fisher Vectors for Person Re-identification</a></td><td><a href="https://pdfs.semanticscholar.org/a105/f1ef67b4b02da38eadce8ffb4e13aa301a93.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>68%</td><td>197</td><td>133</td><td>64</td><td>15</td><td>108</td><td>88</td></tr><tr><td>140438a77a771a8fb656b39a78ff488066eb6b50</td><td>lfpw</td><td>LFPW</td><td><a href="papers/140438a77a771a8fb656b39a78ff488066eb6b50.html" target="_blank">Localizing Parts of Faces Using a Consensus of Exemplars</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>38b55d95189c5e69cf4ab45098a48fba407609b4</td><td>cuhk_campus_03</td><td>CUHK03 Campus</td><td><a href="papers/38b55d95189c5e69cf4ab45098a48fba407609b4.html" target="_blank">Locally Aligned Feature Transforms across Views</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>64%</td><td>258</td><td>164</td><td>94</td><td>15</td><td>136</td><td>117</td></tr><tr><td>8990cdce3f917dad622e43e033db686b354d057c</td><td>tiny_faces</td><td>TinyFace</td><td><a href="papers/8990cdce3f917dad622e43e033db686b354d057c.html" target="_blank">Low-Resolution Face Recognition</a></td><td><a href="https://arxiv.org/pdf/1811.08965.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>c0387e788a52f10bf35d4d50659cfa515d89fbec</td><td>mars</td><td>MARS</td><td><a href="papers/c0387e788a52f10bf35d4d50659cfa515d89fbec.html" target="_blank">MARS: A Video Benchmark for Large-Scale Person Re-Identification</a></td><td><a href="https://pdfs.semanticscholar.org/c038/7e788a52f10bf35d4d50659cfa515d89fbec.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>68%</td><td>168</td><td>115</td><td>53</td><td>4</td><td>97</td><td>69</td></tr><tr><td>9055b155cbabdce3b98e16e5ac9c0edf00f9552f</td><td>morph</td><td>MORPH Commercial</td><td><a href="papers/9055b155cbabdce3b98e16e5ac9c0edf00f9552f.html" target="_blank">MORPH: a longitudinal image database of normal adult age-progression</a></td><td><span class="gray">[pdf]</a></td><td>7th International Conference on Automatic Face and Gesture Recognition (FGR06)</td><td>edu</td><td>North Carolina University</td><td>United States</td><td>34.22398690</td><td>-77.87013250</td><td>59%</td><td>437</td><td>257</td><td>179</td><td>22</td><td>228</td><td>203</td></tr><tr><td>9055b155cbabdce3b98e16e5ac9c0edf00f9552f</td><td>morph_nc</td><td>MORPH Non-Commercial</td><td><a href="papers/9055b155cbabdce3b98e16e5ac9c0edf00f9552f.html" target="_blank">MORPH: a longitudinal image database of normal adult age-progression</a></td><td><span class="gray">[pdf]</a></td><td>7th International Conference on Automatic Face and Gesture Recognition (FGR06)</td><td>edu</td><td>North Carolina University</td><td>United States</td><td>34.22398690</td><td>-77.87013250</td><td>59%</td><td>437</td><td>257</td><td>179</td><td>22</td><td>228</td><td>203</td></tr><tr><td>291265db88023e92bb8c8e6390438e5da148e8f5</td><td>msceleb</td><td>MsCeleb</td><td><a href="papers/291265db88023e92bb8c8e6390438e5da148e8f5.html" target="_blank">MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition</a></td><td><a href="https://arxiv.org/pdf/1607.08221.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>company</td><td>Microsoft</td><td>United States</td><td>47.64233180</td><td>-122.13693020</td><td>78%</td><td>180</td><td>141</td><td>39</td><td>8</td><td>120</td><td>59</td></tr><tr><td>3dc3f0b64ef80f573e3a5f96e456e52ee980b877</td><td>georgia_tech_face_database</td><td>Georgia Tech Face</td><td><a href="papers/3dc3f0b64ef80f573e3a5f96e456e52ee980b877.html" target="_blank">Maximum Likelihood Training of the Embedded HMM for Face Detection and Recognition</a></td><td><a href="http://www.researchgate.net/profile/Monson_Hayes/publication/221124512_Maximum_Likelihood_Training_of_the_Embedded_HMM_for_Face_Detection_and_Recognition/links/0deec53509be9d6f55000000.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>67</td><td>36</td><td>31</td><td>4</td><td>29</td><td>28</td></tr><tr><td>e58dd160a76349d46f881bd6ddbc2921f08d1050</td><td>gfw</td><td>Grouping Face in the Wild</td><td><a href="papers/e58dd160a76349d46f881bd6ddbc2921f08d1050.html" target="_blank">Merge or Not? Learning to Group Faces via Imitation Learning</a></td><td><a href="https://arxiv.org/pdf/1707.03986.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>2</td><td>2</td><td>0</td><td>0</td><td>2</td><td>0</td></tr><tr><td>5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725</td><td>fpoq</td><td>50 People One Question</td><td><a href="papers/5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725.html" target="_blank">Merging Pose Estimates Across Space and Time</a></td><td><a href="http://authors.library.caltech.edu/41565/1/tracking_bmvc.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>81%</td><td>16</td><td>13</td><td>3</td><td>0</td><td>13</td><td>4</td></tr><tr><td>5e0f8c355a37a5a89351c02f174e7a5ddcb98683</td><td>coco</td><td>COCO</td><td><a href="papers/5e0f8c355a37a5a89351c02f174e7a5ddcb98683.html" target="_blank">Microsoft COCO: Common Objects in Context</a></td><td><a href="https://arxiv.org/pdf/1405.0312.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>999</td><td>608</td><td>391</td><td>25</td><td>722</td><td>259</td></tr><tr><td>41976ebc8ab76d9a6861487c97cc7fcbe3b6015f</td><td>moments_in_time</td><td>Moments in Time</td><td><a href="papers/41976ebc8ab76d9a6861487c97cc7fcbe3b6015f.html" target="_blank">Moments in Time Dataset: one million videos for event understanding</a></td><td><a href="https://arxiv.org/pdf/1801.03150.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>69%</td><td>29</td><td>20</td><td>9</td><td>2</td><td>27</td><td>2</td></tr><tr><td>436f798d1a4e54e5947c1e7d7375c31b2bdb4064</td><td>tud_multiview</td><td>TUD-Multiview</td><td><a href="papers/436f798d1a4e54e5947c1e7d7375c31b2bdb4064.html" target="_blank">Monocular 3D pose estimation and tracking by detection</a></td><td><span class="gray">[pdf]</a></td><td>2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>TU Darmstadt</td><td>Germany</td><td>49.87482770</td><td>8.65632810</td><td>59%</td><td>311</td><td>185</td><td>126</td><td>34</td><td>208</td><td>105</td></tr><tr><td>436f798d1a4e54e5947c1e7d7375c31b2bdb4064</td><td>tud_stadtmitte</td><td>TUD-Stadtmitte</td><td><a href="papers/436f798d1a4e54e5947c1e7d7375c31b2bdb4064.html" target="_blank">Monocular 3D pose estimation and tracking by detection</a></td><td><span class="gray">[pdf]</a></td><td>2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>TU Darmstadt</td><td>Germany</td><td>49.87482770</td><td>8.65632810</td><td>59%</td><td>311</td><td>185</td><td>126</td><td>34</td><td>208</td><td>105</td></tr><tr><td>3b5b6d19d4733ab606c39c69a889f9e67967f151</td><td>qmul_grid</td><td>GRID</td><td><a href="papers/3b5b6d19d4733ab606c39c69a889f9e67967f151.html" target="_blank">Multi-camera activity correlation analysis</a></td><td><span class="gray">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>Queen Mary University of London</td><td>United Kingdom</td><td>51.52472720</td><td>-0.03931035</td><td>69%</td><td>142</td><td>98</td><td>44</td><td>7</td><td>77</td><td>64</td></tr><tr><td>6ad5a38df8dd4cdddd74f31996ce096d41219f72</td><td>tud_brussels</td><td>TUD-Brussels</td><td><a href="papers/6ad5a38df8dd4cdddd74f31996ce096d41219f72.html" target="_blank">Multi-cue onboard pedestrian detection</a></td><td><span class="gray">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>6ad5a38df8dd4cdddd74f31996ce096d41219f72</td><td>tud_motionpairs</td><td>TUD-Motionparis</td><td><a href="papers/6ad5a38df8dd4cdddd74f31996ce096d41219f72.html" target="_blank">Multi-cue onboard pedestrian detection</a></td><td><span class="gray">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>32c801cb7fbeb742edfd94cccfca4934baec71da</td><td>ucf_crowd</td><td>UCF-CC-50</td><td><a href="papers/32c801cb7fbeb742edfd94cccfca4934baec71da.html" target="_blank">Multi-source Multi-scale Counting in Extremely Dense Crowd Images</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>68%</td><td>148</td><td>100</td><td>48</td><td>5</td><td>80</td><td>65</td></tr><tr><td>1e3df3ca8feab0b36fd293fe689f93bb2aaac591</td><td>immediacy</td><td>Immediacy</td><td><a href="papers/1e3df3ca8feab0b36fd293fe689f93bb2aaac591.html" target="_blank">Multi-task Recurrent Neural Network for Immediacy Prediction</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>26</td><td>16</td><td>10</td><td>2</td><td>21</td><td>6</td></tr><tr><td>2b926b3586399d028b46315d7d9fb9d879e4f79c</td><td>frav3d</td><td>FRAV3D</td><td><a href="papers/2b926b3586399d028b46315d7d9fb9d879e4f79c.html" target="_blank">Multimodal 2D, 2.5D & 3D Face Verification</a></td><td><span class="gray">[pdf]</a></td><td>2006 International Conference on Image Processing</td><td>edu</td><td>Universidad Rey Juan Carlos, Spain</td><td>Spain</td><td>40.33586610</td><td>-3.87694320</td><td>57%</td><td>14</td><td>8</td><td>6</td><td>0</td><td>2</td><td>12</td></tr><tr><td>53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4</td><td>bp4d_plus</td><td>BP4D+</td><td><a href="papers/53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4.html" target="_blank">Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>42</td><td>23</td><td>19</td><td>0</td><td>17</td><td>26</td></tr><tr><td>2fda164863a06a92d3a910b96eef927269aeb730</td><td>names_and_faces</td><td>News Dataset</td><td><a href="papers/2fda164863a06a92d3a910b96eef927269aeb730.html" target="_blank">Names and faces in the news</a></td><td><span class="gray">[pdf]</a></td><td>Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06</td><td>distance_nighttime</td><td>Long Distance Heterogeneous Face</td><td><a href="papers/4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06.html" target="_blank">Nighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching</a></td><td><a href="https://pdfs.semanticscholar.org/4156/b7e88f2e0ab0a7c095b9bab199ae2b23bd06.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>22</td><td>11</td><td>11</td><td>3</td><td>11</td><td>10</td></tr><tr><td>3394168ff0719b03ff65bcea35336a76b21fe5e4</td><td>penn_fudan</td><td>Penn Fudan</td><td><a href="papers/3394168ff0719b03ff65bcea35336a76b21fe5e4.html" target="_blank">Object Detection Combining Recognition and Segmentation</a></td><td><a href="https://pdfs.semanticscholar.org/3394/168ff0719b03ff65bcea35336a76b21fe5e4.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>105</td><td>64</td><td>41</td><td>9</td><td>58</td><td>43</td></tr><tr><td>12ad3b5bbbf407f8e54ea692c07633d1a867c566</td><td>graz</td><td>Graz Pedestrian</td><td><a href="papers/12ad3b5bbbf407f8e54ea692c07633d1a867c566.html" target="_blank">Object recognition using segmentation for feature detection</a></td><td><span class="gray">[pdf]</a></td><td>Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.</td><td>edu</td><td>Inst. of Comput. Sci., Univ. of Leoben, Austria</td><td>Austria</td><td>47.38473720</td><td>15.09302010</td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>4f93cd09785c6e77bf4bc5a788e079df524c8d21</td><td>soton</td><td>SOTON HiD</td><td><a href="papers/4f93cd09785c6e77bf4bc5a788e079df524c8d21.html" target="_blank">On a Large Sequence-Based Human Gait Database</a></td><td><a href="https://pdfs.semanticscholar.org/4f93/cd09785c6e77bf4bc5a788e079df524c8d21.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>63%</td><td>150</td><td>95</td><td>55</td><td>17</td><td>103</td><td>51</td></tr><tr><td>6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c</td><td>afad</td><td>AFAD</td><td><a href="papers/6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c.html" target="_blank">Ordinal Regression with Multiple Output CNN for Age Estimation</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>53%</td><td>78</td><td>41</td><td>37</td><td>8</td><td>44</td><td>31</td></tr><tr><td>a7fe834a0af614ce6b50dc093132b031dd9a856b</td><td>market_1501</td><td>Market 1501</td><td><a href="papers/a7fe834a0af614ce6b50dc093132b031dd9a856b.html" target="_blank">Orientation Driven Bag of Appearances for Person Re-identification</a></td><td><a href="https://arxiv.org/pdf/1605.02464.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>7</td><td>3</td><td>4</td><td>0</td><td>4</td><td>4</td></tr><tr><td>a7fe834a0af614ce6b50dc093132b031dd9a856b</td><td>pku_reid</td><td>PKU-Reid</td><td><a href="papers/a7fe834a0af614ce6b50dc093132b031dd9a856b.html" target="_blank">Orientation Driven Bag of Appearances for Person Re-identification</a></td><td><a href="https://arxiv.org/pdf/1605.02464.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>7</td><td>3</td><td>4</td><td>0</td><td>4</td><td>4</td></tr><tr><td>18ae7c9a4bbc832b8b14bc4122070d7939f5e00e</td><td>frgc</td><td>FRGC</td><td><a href="papers/18ae7c9a4bbc832b8b14bc4122070d7939f5e00e.html" target="_blank">Overview of the face recognition grand challenge</a></td><td><span class="gray">[pdf]</a></td><td>2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)</td><td>edu</td><td>NIST</td><td>United States</td><td>39.14004000</td><td>-77.21850600</td><td>57%</td><td>999</td><td>568</td><td>430</td><td>85</td><td>549</td><td>442</td></tr><tr><td>22909dd19a0ec3b6065334cb5be5392cb24d839d</td><td>pets</td><td>PETS 2017</td><td><a href="papers/22909dd19a0ec3b6065334cb5be5392cb24d839d.html" target="_blank">PETS 2017: Dataset and Challenge</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</td><td></td><td></td><td></td><td></td><td></td><td>44%</td><td>9</td><td>4</td><td>5</td><td>0</td><td>1</td><td>8</td></tr><tr><td>56ffa7d906b08d02d6d5a12c7377a57e24ef3391</td><td>unbc_shoulder_pain</td><td>UNBC-McMaster Pain</td><td><a href="papers/56ffa7d906b08d02d6d5a12c7377a57e24ef3391.html" target="_blank">Painful data: The UNBC-McMaster shoulder pain expression archive database</a></td><td><span class="gray">[pdf]</a></td><td>Face and Gesture 2011</td><td>edu</td><td>Carnegie Mellon University</td><td>United States</td><td>40.44416190</td><td>-79.94272826</td><td>54%</td><td>189</td><td>103</td><td>86</td><td>21</td><td>108</td><td>78</td></tr><tr><td>55206f0b5f57ce17358999145506cd01e570358c</td><td>orl</td><td>ORL</td><td><a href="papers/55206f0b5f57ce17358999145506cd01e570358c.html" target="_blank">Parameterisation of a stochastic model for human face identification</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>999</td><td>501</td><td>498</td><td>94</td><td>543</td><td>427</td></tr><tr><td>0486214fb58ee9a04edfe7d6a74c6d0f661a7668</td><td>chokepoint</td><td>ChokePoint</td><td><a href="papers/0486214fb58ee9a04edfe7d6a74c6d0f661a7668.html" target="_blank">Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition</a></td><td><span class="gray">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>138</td><td>83</td><td>55</td><td>6</td><td>76</td><td>63</td></tr><tr><td>488e475eeb3bb39a145f23ede197cd3620f1d98a</td><td>apis</td><td>APiS1.0</td><td><a href="papers/488e475eeb3bb39a145f23ede197cd3620f1d98a.html" target="_blank">Pedestrian Attribute Classification in Surveillance: Database and Evaluation</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision Workshops</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>28</td><td>20</td><td>8</td><td>0</td><td>13</td><td>15</td></tr><tr><td>488e475eeb3bb39a145f23ede197cd3620f1d98a</td><td>svs</td><td>SVS</td><td><a href="papers/488e475eeb3bb39a145f23ede197cd3620f1d98a.html" target="_blank">Pedestrian Attribute Classification in Surveillance: Database and Evaluation</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision Workshops</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>28</td><td>20</td><td>8</td><td>0</td><td>13</td><td>15</td></tr><tr><td>2a4bbee0b4cf52d5aadbbc662164f7efba89566c</td><td>peta</td><td>PETA</td><td><a href="papers/2a4bbee0b4cf52d5aadbbc662164f7efba89566c.html" target="_blank">Pedestrian Attribute Recognition At Far Distance</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>75%</td><td>88</td><td>66</td><td>22</td><td>1</td><td>50</td><td>36</td></tr><tr><td>f72f6a45ee240cc99296a287ff725aaa7e7ebb35</td><td>caltech_pedestrians</td><td>Caltech Pedestrians</td><td><a href="papers/f72f6a45ee240cc99296a287ff725aaa7e7ebb35.html" target="_blank">Pedestrian Detection: An Evaluation of the State of the Art</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td>edu</td><td>California Institute of Technology</td><td>United States</td><td>34.13710185</td><td>-118.12527487</td><td>60%</td><td>999</td><td>596</td><td>403</td><td>70</td><td>527</td><td>466</td></tr><tr><td>1dc35905a1deff8bc74688f2d7e2f48fd2273275</td><td>caltech_pedestrians</td><td>Caltech Pedestrians</td><td><a href="papers/1dc35905a1deff8bc74688f2d7e2f48fd2273275.html" target="_blank">Pedestrian detection: A benchmark</a></td><td><span class="gray">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td><td>tud_campus</td><td>TUD-Campus</td><td><a href="papers/3316521a5527c7700af8ae6aef32a79a8b83672c.html" target="_blank">People-tracking-by-detection and people-detection-by-tracking</a></td><td><span class="gray">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>59%</td><td>545</td><td>324</td><td>220</td><td>37</td><td>330</td><td>218</td></tr><tr><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td><td>tud_crossing</td><td>TUD-Crossing</td><td><a href="papers/3316521a5527c7700af8ae6aef32a79a8b83672c.html" target="_blank">People-tracking-by-detection and people-detection-by-tracking</a></td><td><span class="gray">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>59%</td><td>545</td><td>324</td><td>220</td><td>37</td><td>330</td><td>218</td></tr><tr><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td><td>tud_pedestrian</td><td>TUD-Pedestrian</td><td><a href="papers/3316521a5527c7700af8ae6aef32a79a8b83672c.html" target="_blank">People-tracking-by-detection and people-detection-by-tracking</a></td><td><span class="gray">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>59%</td><td>545</td><td>324</td><td>220</td><td>37</td><td>330</td><td>218</td></tr><tr><td>27a2fad58dd8727e280f97036e0d2bc55ef5424c</td><td>duke_mtmc</td><td>Duke MTMC</td><td><a href="papers/27a2fad58dd8727e280f97036e0d2bc55ef5424c.html" target="_blank">Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking</a></td><td><a href="https://arxiv.org/pdf/1609.01775.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Duke University</td><td>United States</td><td>35.99905220</td><td>-78.92906290</td><td>85%</td><td>169</td><td>144</td><td>25</td><td>3</td><td>113</td><td>54</td></tr><tr><td>27a2fad58dd8727e280f97036e0d2bc55ef5424c</td><td>mot</td><td>MOT</td><td><a href="papers/27a2fad58dd8727e280f97036e0d2bc55ef5424c.html" target="_blank">Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking</a></td><td><a href="https://arxiv.org/pdf/1609.01775.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Duke University</td><td>United States</td><td>35.99905220</td><td>-78.92906290</td><td>85%</td><td>169</td><td>144</td><td>25</td><td>3</td><td>113</td><td>54</td></tr><tr><td>16c7c31a7553d99f1837fc6e88e77b5ccbb346b8</td><td>prid</td><td>PRID</td><td><a href="papers/16c7c31a7553d99f1837fc6e88e77b5ccbb346b8.html" target="_blank">Person Re-identification by Descriptive and Discriminative Classification</a></td><td><a href="https://pdfs.semanticscholar.org/4c1b/f0592be3e535faf256c95e27982db9b3d3d3.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>68%</td><td>386</td><td>263</td><td>123</td><td>23</td><td>204</td><td>180</td></tr><tr><td>98bb029afe2a1239c3fdab517323066f0957b81b</td><td>ilids_mcts_vid</td><td>iLIDS-VID</td><td><a href="papers/98bb029afe2a1239c3fdab517323066f0957b81b.html" target="_blank">Person Re-identification by Video Ranking</a></td><td><a href="https://pdfs.semanticscholar.org/98bb/029afe2a1239c3fdab517323066f0957b81b.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>68%</td><td>209</td><td>143</td><td>66</td><td>8</td><td>111</td><td>97</td></tr><tr><td>98bb029afe2a1239c3fdab517323066f0957b81b</td><td>sdu_vid</td><td>SDU-VID</td><td><a href="papers/98bb029afe2a1239c3fdab517323066f0957b81b.html" target="_blank">Person Re-identification by Video Ranking</a></td><td><a href="https://pdfs.semanticscholar.org/98bb/029afe2a1239c3fdab517323066f0957b81b.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>68%</td><td>209</td><td>143</td><td>66</td><td>8</td><td>111</td><td>97</td></tr><tr><td>0b84f07af44f964817675ad961def8a51406dd2e</td><td>prw</td><td>PRW</td><td><a href="papers/0b84f07af44f964817675ad961def8a51406dd2e.html" target="_blank">Person Re-identification in the Wild</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>68%</td><td>77</td><td>52</td><td>25</td><td>1</td><td>47</td><td>27</td></tr><tr><td>a0cc5f73a37723a6dd465924143f1cb4976d0169</td><td>msmt_17</td><td>MSMT17</td><td><a href="papers/a0cc5f73a37723a6dd465924143f1cb4976d0169.html" target="_blank">Person Transfer GAN to Bridge Domain Gap for Person Re-identification</a></td><td><span class="gray">[pdf]</a></td><td>2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>92%</td><td>24</td><td>22</td><td>2</td><td>1</td><td>20</td><td>4</td></tr><tr><td>1c2802c2199b6d15ecefe7ba0c39bfe44363de38</td><td>youtube_poses</td><td>YouTube Pose</td><td><a href="papers/1c2802c2199b6d15ecefe7ba0c39bfe44363de38.html" target="_blank">Personalizing Human Video Pose Estimation</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>Oxford University</td><td>United Kingdom</td><td>51.75208490</td><td>-1.25166460</td><td>64%</td><td>36</td><td>23</td><td>13</td><td>2</td><td>30</td><td>8</td></tr><tr><td>2830fb5282de23d7784b4b4bc37065d27839a412</td><td>h3d</td><td>H3D</td><td><a href="papers/2830fb5282de23d7784b4b4bc37065d27839a412.html" target="_blank">Poselets: Body part detectors trained using 3D human pose annotations</a></td><td><span class="gray">[pdf]</a></td><td>2009 IEEE 12th International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>58%</td><td>716</td><td>413</td><td>303</td><td>59</td><td>492</td><td>222</td></tr><tr><td>3765df816dc5a061bc261e190acc8bdd9d47bec0</td><td>rafd</td><td>RaFD</td><td><a href="papers/3765df816dc5a061bc261e190acc8bdd9d47bec0.html" target="_blank">Presentation and validation of the Radboud Faces Database</a></td><td><a href="https://pdfs.semanticscholar.org/3765/df816dc5a061bc261e190acc8bdd9d47bec0.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>48%</td><td>487</td><td>234</td><td>253</td><td>39</td><td>342</td><td>144</td></tr><tr><td>636b8ffc09b1b23ff714ac8350bb35635e49fa3c</td><td>caltech_10k_web_faces</td><td>Caltech 10K Web Faces</td><td><a href="papers/636b8ffc09b1b23ff714ac8350bb35635e49fa3c.html" target="_blank">Pruning training sets for learning of object categories</a></td><td><span class="gray">[pdf]</a></td><td>2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)</td><td></td><td></td><td></td><td></td><td></td><td>70%</td><td>63</td><td>44</td><td>19</td><td>4</td><td>42</td><td>20</td></tr><tr><td>3531332efe19be21e7401ba1f04570a142617236</td><td>ufdd</td><td>UFDD</td><td><a href="papers/3531332efe19be21e7401ba1f04570a142617236.html" target="_blank">Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results</a></td><td><a href="https://arxiv.org/pdf/1804.10275.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>4</td><td>2</td><td>2</td><td>1</td><td>4</td><td>0</td></tr><tr><td>140c95e53c619eac594d70f6369f518adfea12ef</td><td>ijb_a</td><td>IJB-A</td><td><a href="papers/140c95e53c619eac594d70f6369f518adfea12ef.html" target="_blank">Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>66%</td><td>237</td><td>156</td><td>81</td><td>14</td><td>159</td><td>76</td></tr><tr><td>c72a2ea819df9b0e8cd267eebcc6528b8741e03d</td><td>megaage</td><td>MegaAge</td><td><a href="papers/c72a2ea819df9b0e8cd267eebcc6528b8741e03d.html" target="_blank">Quantifying Facial Age by Posterior of Age Comparisons</a></td><td><a href="https://arxiv.org/pdf/1708.09687.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>75%</td><td>4</td><td>3</td><td>1</td><td>0</td><td>4</td><td>0</td></tr><tr><td>922e0a51a3b8c67c4c6ac09a577ff674cbd28b34</td><td>v47</td><td>V47</td><td><a href="papers/922e0a51a3b8c67c4c6ac09a577ff674cbd28b34.html" target="_blank">Re-identification of pedestrians with variable occlusion and scale</a></td><td><span class="gray">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td>edu</td><td>Kingston University</td><td>United Kingdom</td><td>51.42930860</td><td>-0.26840440</td><td>56%</td><td>9</td><td>5</td><td>4</td><td>1</td><td>5</td><td>4</td></tr><tr><td>6f3c76b7c0bd8e1d122c6ea808a271fd4749c951</td><td>ward</td><td>WARD</td><td><a href="papers/6f3c76b7c0bd8e1d122c6ea808a271fd4749c951.html" target="_blank">Re-identify people in wide area camera network</a></td><td><span class="gray">[pdf]</a></td><td>2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops</td><td>edu</td><td>University of Udine</td><td>Italy</td><td>46.08107230</td><td>13.21194740</td><td>60%</td><td>60</td><td>36</td><td>24</td><td>1</td><td>38</td><td>21</td></tr><tr><td>54983972aafc8e149259d913524581357b0f91c3</td><td>reseed</td><td>ReSEED</td><td><a href="papers/54983972aafc8e149259d913524581357b0f91c3.html" target="_blank">ReSEED: social event dEtection dataset</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>67%</td><td>6</td><td>4</td><td>2</td><td>1</td><td>1</td><td>5</td></tr><tr><td>65355cbb581a219bd7461d48b3afd115263ea760</td><td>complex_activities</td><td>Ongoing Complex Activities</td><td><a href="papers/65355cbb581a219bd7461d48b3afd115263ea760.html" target="_blank">Recognition of ongoing complex activities by sequence prediction over a hierarchical label space</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Winter Conference on Applications of Computer Vision (WACV)</td><td></td><td></td><td></td><td></td><td></td><td>33%</td><td>3</td><td>1</td><td>2</td><td>0</td><td>3</td><td>0</td></tr><tr><td>e8de844fefd54541b71c9823416daa238be65546</td><td>visual_phrases</td><td>Phrasal Recognition</td><td><a href="papers/e8de844fefd54541b71c9823416daa238be65546.html" target="_blank">Recognition using visual phrases</a></td><td><span class="gray">[pdf]</a></td><td>CVPR 2011</td><td>edu</td><td>University of Illinois, Urbana-Champaign</td><td>United States</td><td>40.11116745</td><td>-88.22587665</td><td>58%</td><td>246</td><td>142</td><td>104</td><td>17</td><td>170</td><td>68</td></tr><tr><td>356b431d4f7a2a0a38cf971c84568207dcdbf189</td><td>wider</td><td>WIDER</td><td><a href="papers/356b431d4f7a2a0a38cf971c84568207dcdbf189.html" target="_blank">Recognize complex events from static images by fusing deep channels</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>68%</td><td>44</td><td>30</td><td>14</td><td>1</td><td>29</td><td>15</td></tr><tr><td>25474c21613607f6bb7687a281d5f9d4ffa1f9f3</td><td>faceplace</td><td>Face Place</td><td><a href="papers/25474c21613607f6bb7687a281d5f9d4ffa1f9f3.html" target="_blank">Recognizing disguised faces</a></td><td><a href="https://pdfs.semanticscholar.org/d936/7ceb0be378c3a9ddf7cb741c678c1a3c574c.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>34%</td><td>29</td><td>10</td><td>19</td><td>0</td><td>18</td><td>10</td></tr><tr><td>4053e3423fb70ad9140ca89351df49675197196a</td><td>bio_id</td><td>BioID Face</td><td><a href="papers/4053e3423fb70ad9140ca89351df49675197196a.html" target="_blank">Robust Face Detection Using the Hausdorff Distance</a></td><td><a href="https://pdfs.semanticscholar.org/4053/e3423fb70ad9140ca89351df49675197196a.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>511</td><td>289</td><td>222</td><td>49</td><td>329</td><td>182</td></tr><tr><td>2724ba85ec4a66de18da33925e537f3902f21249</td><td>cofw</td><td>COFW</td><td><a href="papers/2724ba85ec4a66de18da33925e537f3902f21249.html" target="_blank">Robust Face Landmark Estimation under Occlusion</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision</td><td>edu</td><td>California Institute of Technology</td><td>United States</td><td>34.13710185</td><td>-118.12527487</td><td>72%</td><td>325</td><td>233</td><td>92</td><td>12</td><td>194</td><td>133</td></tr><tr><td>c570d1247e337f91e555c3be0e8c8a5aba539d9f</td><td>mcgill</td><td>McGill Real World</td><td><a href="papers/c570d1247e337f91e555c3be0e8c8a5aba539d9f.html" target="_blank">Robust semi-automatic head pose labeling for real-world face video sequences</a></td><td><span class="gray">[pdf]</a></td><td>Multimedia Tools and Applications</td><td>edu</td><td>McGill University</td><td>Canada</td><td>45.50397610</td><td>-73.57496870</td><td>44%</td><td>18</td><td>8</td><td>10</td><td>0</td><td>13</td><td>7</td></tr><tr><td>e27ef52c641c2b5100a1b34fd0b819e84a31b4df</td><td>sarc3d</td><td>Sarc3D</td><td><a href="papers/e27ef52c641c2b5100a1b34fd0b819e84a31b4df.html" target="_blank">SARC3D: A New 3D Body Model for People Tracking and Re-identification</a></td><td><a href="https://pdfs.semanticscholar.org/e27e/f52c641c2b5100a1b34fd0b819e84a31b4df.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>74%</td><td>34</td><td>25</td><td>9</td><td>2</td><td>21</td><td>12</td></tr><tr><td>bd26dabab576adb6af30484183c9c9c8379bf2e0</td><td>scut_fbp</td><td>SCUT-FBP</td><td><a href="papers/bd26dabab576adb6af30484183c9c9c8379bf2e0.html" target="_blank">SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Systems, Man, and Cybernetics</td><td></td><td></td><td></td><td></td><td></td><td>47%</td><td>19</td><td>9</td><td>10</td><td>2</td><td>6</td><td>13</td></tr><tr><td>29a705a5fa76641e0d8963f1fdd67ee4c0d92d3d</td><td>scface</td><td>SCface</td><td><a href="papers/29a705a5fa76641e0d8963f1fdd67ee4c0d92d3d.html" target="_blank">SCface – surveillance cameras face database</a></td><td><a href="http://scface.org/SCface%20-%20Surveillance%20Cameras%20Face%20Database.pdf" target="_blank">[pdf]</a></td><td>Multimedia Tools and Applications</td><td></td><td></td><td></td><td></td><td></td><td>56%</td><td>179</td><td>101</td><td>78</td><td>15</td><td>88</td><td>89</td></tr><tr><td>d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9</td><td>stair_actions</td><td>STAIR Action</td><td><a href="papers/d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9.html" target="_blank">STAIR Actions: A Video Dataset of Everyday Home Actions</a></td><td><a href="https://arxiv.org/pdf/1804.04326.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><td>833fa04463d90aab4a9fe2870d480f0b40df446e</td><td>sun_attributes</td><td>SUN</td><td><a href="papers/833fa04463d90aab4a9fe2870d480f0b40df446e.html" target="_blank">SUN attribute database: Discovering, annotating, and recognizing scene attributes</a></td><td><span class="gray">[pdf]</a></td><td>2012 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>Brown University</td><td>United States</td><td>41.82686820</td><td>-71.40123146</td><td>60%</td><td>264</td><td>159</td><td>105</td><td>27</td><td>206</td><td>56</td></tr><tr><td>4308bd8c28e37e2ed9a3fcfe74d5436cce34b410</td><td>market_1501</td><td>Market 1501</td><td><a href="papers/4308bd8c28e37e2ed9a3fcfe74d5436cce34b410.html" target="_blank">Scalable Person Re-identification: A Benchmark</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td>company</td><td>Microsoft</td><td>United States</td><td>47.64233180</td><td>-122.13693020</td><td>77%</td><td>460</td><td>355</td><td>105</td><td>9</td><td>263</td><td>185</td></tr><tr><td>9c23859ec7313f2e756a3e85575735e0c52249f4</td><td>facebook_100</td><td>Facebook100</td><td><a href="papers/9c23859ec7313f2e756a3e85575735e0c52249f4.html" target="_blank">Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook</a></td><td><span class="gray">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>Harvard University</td><td>United States</td><td>42.36782045</td><td>-71.12666653</td><td>62%</td><td>52</td><td>32</td><td>20</td><td>3</td><td>38</td><td>13</td></tr><tr><td>9c23859ec7313f2e756a3e85575735e0c52249f4</td><td>pubfig_83</td><td>pubfig83</td><td><a href="papers/9c23859ec7313f2e756a3e85575735e0c52249f4.html" target="_blank">Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook</a></td><td><span class="gray">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>Harvard University</td><td>United States</td><td>42.36782045</td><td>-71.12666653</td><td>62%</td><td>52</td><td>32</td><td>20</td><td>3</td><td>38</td><td>13</td></tr><tr><td>51eba481dac6b229a7490f650dff7b17ce05df73</td><td>imsitu</td><td>imSitu</td><td><a href="papers/51eba481dac6b229a7490f650dff7b17ce05df73.html" target="_blank">Situation Recognition: Visual Semantic Role Labeling for Image Understanding</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>52</td><td>34</td><td>18</td><td>1</td><td>46</td><td>6</td></tr><tr><td>570f37ed63142312e6ccdf00ecc376341ec72b9f</td><td>stanford_drone</td><td>Stanford Drone</td><td><a href="papers/570f37ed63142312e6ccdf00ecc376341ec72b9f.html" target="_blank">Social LSTM: Human Trajectory Prediction in Crowded Spaces</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>56%</td><td>224</td><td>125</td><td>99</td><td>3</td><td>140</td><td>81</td></tr><tr><td>23e824d1dfc33f3780dd18076284f07bd99f1c43</td><td>mifs</td><td>MIFS</td><td><a href="papers/23e824d1dfc33f3780dd18076284f07bd99f1c43.html" target="_blank">Spoofing faces using makeup: An investigative study</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)</td><td>edu</td><td>INRIA Méditerranée</td><td>France</td><td>43.61581310</td><td>7.06838000</td><td>67%</td><td>6</td><td>4</td><td>2</td><td>0</td><td>1</td><td>5</td></tr><tr><td>1a40092b493c6b8840257ab7f96051d1a4dbfeb2</td><td>sports_videos_in_the_wild</td><td>SVW</td><td><a href="papers/1a40092b493c6b8840257ab7f96051d1a4dbfeb2.html" target="_blank">Sports Videos in the Wild (SVW): A video dataset for sports analysis</a></td><td><span class="gray">[pdf]</a></td><td>2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)</td><td></td><td></td><td></td><td></td><td></td><td>86%</td><td>7</td><td>6</td><td>1</td><td>1</td><td>5</td><td>2</td></tr><tr><td>9361b784e73e9238d5cefbea5ac40d35d1e3103f</td><td>oxford_town_centre</td><td>TownCentre</td><td><a href="papers/9361b784e73e9238d5cefbea5ac40d35d1e3103f.html" target="_blank">Stable multi-target tracking in real-time surveillance video</a></td><td><span class="gray">[pdf]</a></td><td>CVPR 2011</td><td>edu</td><td>University of Oxford</td><td>United Kingdom</td><td>51.75345380</td><td>-1.25400997</td><td>67%</td><td>328</td><td>221</td><td>107</td><td>13</td><td>186</td><td>140</td></tr><tr><td>2306b2a8fba28539306052764a77a0d0f5d1236a</td><td>qmul_surv_face</td><td>QMUL-SurvFace</td><td><a href="papers/2306b2a8fba28539306052764a77a0d0f5d1236a.html" target="_blank">Surveillance Face Recognition Challenge</a></td><td><a href="https://arxiv.org/pdf/1804.09691.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td>edu</td><td>Queen Mary University of London</td><td>United Kingdom</td><td>51.52472720</td><td>-0.03931035</td><td>100%</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><td>f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f</td><td>pku_reid</td><td>PKU-Reid</td><td><a href="papers/f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f.html" target="_blank">Swiss-System Based Cascade Ranking for Gait-Based Person Re-Identification</a></td><td><a href="https://pdfs.semanticscholar.org/f6c8/d5e35d7e4d60a0104f233ac1a3ab757da53f.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>4</td><td>2</td><td>2</td><td>0</td><td>1</td><td>2</td></tr><tr><td>4d58f886f5150b2d5e48fd1b5a49e09799bf895d</td><td>texas_3dfrd</td><td>Texas 3DFRD</td><td><a href="papers/4d58f886f5150b2d5e48fd1b5a49e09799bf895d.html" target="_blank">Texas 3D Face Recognition Database</a></td><td><span class="gray">[pdf]</a></td><td>2010 IEEE Southwest Symposium on Image Analysis & Interpretation (SSIAI)</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>66</td><td>40</td><td>26</td><td>3</td><td>40</td><td>27</td></tr><tr><td>6d96f946aaabc734af7fe3fc4454cf8547fcd5ed</td><td>ar_facedb</td><td>AR Face</td><td><a href="papers/6d96f946aaabc734af7fe3fc4454cf8547fcd5ed.html" target="_blank">The AR face database</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>58%</td><td>999</td><td>579</td><td>420</td><td>58</td><td>458</td><td>530</td></tr><tr><td>2485c98aa44131d1a2f7d1355b1e372f2bb148ad</td><td>cas_peal</td><td>CAS-PEAL</td><td><a href="papers/2485c98aa44131d1a2f7d1355b1e372f2bb148ad.html" target="_blank">The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans</td><td></td><td></td><td></td><td></td><td></td><td>59%</td><td>429</td><td>254</td><td>175</td><td>38</td><td>198</td><td>234</td></tr><tr><td>47662d1a368daf70ba70ef2d59eb6209f98b675d</td><td>fia</td><td>CMU FiA</td><td><a href="papers/47662d1a368daf70ba70ef2d59eb6209f98b675d.html" target="_blank">The CMU Face In Action (FIA) Database</a></td><td><a href="https://pdfs.semanticscholar.org/4766/2d1a368daf70ba70ef2d59eb6209f98b675d.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>48%</td><td>54</td><td>26</td><td>28</td><td>5</td><td>40</td><td>16</td></tr><tr><td>4d423acc78273b75134e2afd1777ba6d3a398973</td><td>cmu_pie</td><td>CMU PIE</td><td><a href="papers/4d423acc78273b75134e2afd1777ba6d3a398973.html" target="_blank">The CMU Pose, Illumination, and Expression (PIE) Database</a></td><td><a href="http://www.comp.nus.edu.sg/~tsim/piedb.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>59%</td><td>760</td><td>449</td><td>310</td><td>49</td><td>404</td><td>345</td></tr><tr><td>4d423acc78273b75134e2afd1777ba6d3a398973</td><td>multi_pie</td><td>MULTIPIE</td><td><a href="papers/4d423acc78273b75134e2afd1777ba6d3a398973.html" target="_blank">The CMU Pose, Illumination, and Expression (PIE) Database</a></td><td><a href="http://www.comp.nus.edu.sg/~tsim/piedb.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>59%</td><td>760</td><td>449</td><td>310</td><td>49</td><td>404</td><td>345</td></tr><tr><td>4df3143922bcdf7db78eb91e6b5359d6ada004d2</td><td>cfd</td><td>CFD</td><td><a href="papers/4df3143922bcdf7db78eb91e6b5359d6ada004d2.html" target="_blank">The Chicago face database: A free stimulus set of faces and norming data.</a></td><td><a href="https://pdfs.semanticscholar.org/4df3/143922bcdf7db78eb91e6b5359d6ada004d2.pdf" target="_blank">[pdf]</a></td><td>Behavior research methods</td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>99</td><td>59</td><td>40</td><td>1</td><td>73</td><td>21</td></tr><tr><td>20388099cc415c772926e47bcbbe554e133343d1</td><td>cafe</td><td>#N/A</td><td><a href="papers/20388099cc415c772926e47bcbbe554e133343d1.html" target="_blank">The Child Affective Facial Expression (CAFE) set: validity and reliability from untrained adults</a></td><td><a href="https://pdfs.semanticscholar.org/2038/8099cc415c772926e47bcbbe554e133343d1.pdf" target="_blank">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>37</td><td>20</td><td>17</td><td>3</td><td>30</td><td>7</td></tr><tr><td>4e6ee936eb50dd032f7138702fa39b7c18ee8907</td><td>dartmouth_children</td><td>Dartmouth Children</td><td><a href="papers/4e6ee936eb50dd032f7138702fa39b7c18ee8907.html" target="_blank">The Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set</a></td><td><a href="https://pdfs.semanticscholar.org/4e6e/e936eb50dd032f7138702fa39b7c18ee8907.pdf" target="_blank">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>21</td><td>11</td><td>10</td><td>2</td><td>18</td><td>3</td></tr><tr><td>9e31e77f9543ab42474ba4e9330676e18c242e72</td><td>imdb_face</td><td>IMDb Face</td><td><a href="papers/9e31e77f9543ab42474ba4e9330676e18c242e72.html" target="_blank">The Devil of Face Recognition is in the Noise</a></td><td><a href="https://arxiv.org/pdf/1807.11649.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Nanyang Technological University</td><td>Singapore</td><td>1.34841040</td><td>103.68297965</td><td>50%</td><td>6</td><td>3</td><td>3</td><td>0</td><td>4</td><td>1</td></tr><tr><td>71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6</td><td>umd_faces</td><td>UMD</td><td><a href="papers/71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6.html" target="_blank">The Do’s and Don’ts for CNN-Based Face Verification</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE International Conference on Computer Vision Workshops (ICCVW)</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>26</td><td>16</td><td>10</td><td>2</td><td>16</td><td>8</td></tr><tr><td>72a155c987816ae81c858fddbd6beab656d86220</td><td>europersons</td><td>EuroCity Persons</td><td><a href="papers/72a155c987816ae81c858fddbd6beab656d86220.html" target="_blank">The EuroCity Persons Dataset: A Novel Benchmark for Object Detection</a></td><td><a href="https://arxiv.org/pdf/1805.07193.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>2</td><td>0</td><td>2</td><td>0</td><td>2</td><td>0</td></tr><tr><td>4d9a02d080636e9666c4d1cc438b9893391ec6c7</td><td>cohn_kanade_plus</td><td>CK+</td><td><a href="papers/4d9a02d080636e9666c4d1cc438b9893391ec6c7.html" target="_blank">The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression</a></td><td><span class="gray">[pdf]</a></td><td>2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops</td><td>edu</td><td>University of Pittsburgh</td><td>United States</td><td>40.44415295</td><td>-79.96243993</td><td>61%</td><td>999</td><td>607</td><td>392</td><td>57</td><td>470</td><td>518</td></tr><tr><td>0f0fcf041559703998abf310e56f8a2f90ee6f21</td><td>feret</td><td>FERET</td><td><a href="papers/0f0fcf041559703998abf310e56f8a2f90ee6f21.html" target="_blank">The FERET Evaluation Methodology for Face-Recognition Algorithms</a></td><td><a href="https://pdfs.semanticscholar.org/5099/7a5605c1f61e09e9a96789ed7495be6625aa.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>34%</td><td>29</td><td>10</td><td>19</td><td>3</td><td>18</td><td>9</td></tr><tr><td>0c4a139bb87c6743c7905b29a3cfec27a5130652</td><td>feret</td><td>FERET</td><td><a href="papers/0c4a139bb87c6743c7905b29a3cfec27a5130652.html" target="_blank">The FERET Verification Testing Protocol for Face Recognition Algorithms</a></td><td><a href="https://pdfs.semanticscholar.org/8d2a/1c768fce6f71584dd993fb97e7b6419aaf60.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>edu</td><td>City University of New York</td><td>United States</td><td>40.87228250</td><td>-73.89489171</td><td>51%</td><td>115</td><td>59</td><td>56</td><td>8</td><td>75</td><td>37</td></tr><tr><td>dc8b25e35a3acb812beb499844734081722319b4</td><td>feret</td><td>FERET</td><td><a href="papers/dc8b25e35a3acb812beb499844734081722319b4.html" target="_blank">The FERET database and evaluation procedure for face-recognition algorithms</a></td><td><a href="http://biometrics.nist.gov/cs_links/face/frvt/feret/FERET_Database_evaluation_procedure.pdf" target="_blank">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>999</td><td>521</td><td>478</td><td>103</td><td>591</td><td>421</td></tr><tr><td>8f02ec0be21461fbcedf51d864f944cfc42c875f</td><td>hda_plus</td><td>HDA+</td><td><a href="papers/8f02ec0be21461fbcedf51d864f944cfc42c875f.html" target="_blank">The HDA+ Data Set for Research on Fully Automated Re-identification Systems</a></td><td><a href="https://pdfs.semanticscholar.org/8f02/ec0be21461fbcedf51d864f944cfc42c875f.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>16</td><td>8</td><td>8</td><td>1</td><td>10</td><td>6</td></tr><tr><td>8be57cdad86fdf8c8290df4ca3149592f3c46dd3</td><td>m2vts</td><td>m2vts</td><td><a href="papers/8be57cdad86fdf8c8290df4ca3149592f3c46dd3.html" target="_blank">The M2VTS Multimodal Face Database (Release 1.00)</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>45%</td><td>73</td><td>33</td><td>40</td><td>2</td><td>39</td><td>33</td></tr><tr><td>ea050801199f98a1c7c1df6769f23f658299a3ae</td><td>mpi_large</td><td>Large MPI Facial Expression</td><td><a href="papers/ea050801199f98a1c7c1df6769f23f658299a3ae.html" target="_blank">The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</a></td><td><a href="https://pdfs.semanticscholar.org/ea05/0801199f98a1c7c1df6769f23f658299a3ae.pdf" target="_blank">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>33</td><td>17</td><td>16</td><td>4</td><td>29</td><td>4</td></tr><tr><td>ea050801199f98a1c7c1df6769f23f658299a3ae</td><td>mpi_small</td><td>Small MPI Facial Expression</td><td><a href="papers/ea050801199f98a1c7c1df6769f23f658299a3ae.html" target="_blank">The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</a></td><td><a href="https://pdfs.semanticscholar.org/ea05/0801199f98a1c7c1df6769f23f658299a3ae.pdf" target="_blank">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>33</td><td>17</td><td>16</td><td>4</td><td>29</td><td>4</td></tr><tr><td>578d4ad74818086bb64f182f72e2c8bd31e3d426</td><td>mr2</td><td>MR2</td><td><a href="papers/578d4ad74818086bb64f182f72e2c8bd31e3d426.html" target="_blank">The MR2: A multi-racial, mega-resolution database of facial stimuli.</a></td><td><a href="http://www.mpmlab.org/The%20MR2%20face%20database.pdf" target="_blank">[pdf]</a></td><td>Behavior research methods</td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>7</td><td>3</td><td>4</td><td>0</td><td>7</td><td>0</td></tr><tr><td>f1af714b92372c8e606485a3982eab2f16772ad8</td><td>mug_faces</td><td>MUG Faces</td><td><a href="papers/f1af714b92372c8e606485a3982eab2f16772ad8.html" target="_blank">The MUG facial expression database</a></td><td><span class="gray">[pdf]</a></td><td>11th International Workshop on Image Analysis for Multimedia Interactive Services WIAMIS 10</td><td>edu</td><td>Aristotle University of Thessaloniki</td><td>Greece</td><td>40.62984145</td><td>22.95889350</td><td>55%</td><td>82</td><td>45</td><td>37</td><td>4</td><td>34</td><td>47</td></tr><tr><td>79828e6e9f137a583082b8b5a9dfce0c301989b8</td><td>mapillary</td><td>Mapillary</td><td><a href="papers/79828e6e9f137a583082b8b5a9dfce0c301989b8.html" target="_blank">The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>61</td><td>37</td><td>24</td><td>0</td><td>43</td><td>16</td></tr><tr><td>96e0cfcd81cdeb8282e29ef9ec9962b125f379b0</td><td>megaface</td><td>MegaFace</td><td><a href="papers/96e0cfcd81cdeb8282e29ef9ec9962b125f379b0.html" target="_blank">The MegaFace Benchmark: 1 Million Faces for Recognition at Scale</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>76%</td><td>139</td><td>106</td><td>33</td><td>5</td><td>100</td><td>37</td></tr><tr><td>0ee1916a0cb2dc7d3add086b5f1092c3d4beb38a</td><td>voc</td><td>VOC</td><td><a href="papers/0ee1916a0cb2dc7d3add086b5f1092c3d4beb38a.html" target="_blank">The Pascal Visual Object Classes (VOC) Challenge</a></td><td><a href="http://eprints.pascal-network.org/archive/00006187/01/PascalVOC_IJCV2009.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td>company</td><td>Microsoft</td><td>United States</td><td>47.64233180</td><td>-122.13693020</td><td>60%</td><td>999</td><td>599</td><td>399</td><td>29</td><td>557</td><td>422</td></tr><tr><td>66e6f08873325d37e0ec20a4769ce881e04e964e</td><td>sun_attributes</td><td>SUN</td><td><a href="papers/66e6f08873325d37e0ec20a4769ce881e04e964e.html" target="_blank">The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding</a></td><td><a href="http://www.cc.gatech.edu/~hays/papers/attribute_ijcv.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>116</td><td>70</td><td>46</td><td>14</td><td>84</td><td>31</td></tr><tr><td>8b2dd5c61b23ead5ae5508bb8ce808b5ea266730</td><td>10k_US_adult_faces</td><td>10K US Adult Faces</td><td><a href="papers/8b2dd5c61b23ead5ae5508bb8ce808b5ea266730.html" target="_blank">The intrinsic memorability of face photographs.</a></td><td><a href="https://pdfs.semanticscholar.org/8b2d/d5c61b23ead5ae5508bb8ce808b5ea266730.pdf" target="_blank">[pdf]</a></td><td>Journal of experimental psychology. General</td><td></td><td></td><td></td><td></td><td></td><td>56%</td><td>52</td><td>29</td><td>23</td><td>2</td><td>36</td><td>14</td></tr><tr><td>d178cde92ab3dc0dd2ebee5a76a33d556c39448b</td><td>jiku_mobile</td><td>Jiku Mobile Video Dataset</td><td><a href="papers/d178cde92ab3dc0dd2ebee5a76a33d556c39448b.html" target="_blank">The jiku mobile video dataset</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td>edu</td><td>National University of Singapore</td><td>Singapore</td><td>1.29620180</td><td>103.77689944</td><td>71%</td><td>24</td><td>17</td><td>7</td><td>0</td><td>6</td><td>19</td></tr><tr><td>ae0aee03d946efffdc7af2362a42d3750e7dd48a</td><td>put_face</td><td>Put Face</td><td><a href="papers/ae0aee03d946efffdc7af2362a42d3750e7dd48a.html" target="_blank">The put face database</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>99</td><td>54</td><td>45</td><td>5</td><td>55</td><td>48</td></tr><tr><td>19d1b811df60f86cbd5e04a094b07f32fff7a32a</td><td>york_3d</td><td>UOY 3D Face Database</td><td><a href="papers/19d1b811df60f86cbd5e04a094b07f32fff7a32a.html" target="_blank">Three-dimensional face recognition: an eigensurface approach</a></td><td><span class="gray">[pdf]</a></td><td>2004 International Conference on Image Processing, 2004. ICIP '04.</td><td></td><td></td><td></td><td></td><td></td><td>42%</td><td>38</td><td>16</td><td>22</td><td>4</td><td>24</td><td>13</td></tr><tr><td>2edb87494278ad11641b6cf7a3f8996de12b8e14</td><td>qmul_grid</td><td>GRID</td><td><a href="papers/2edb87494278ad11641b6cf7a3f8996de12b8e14.html" target="_blank">Time-Delayed Correlation Analysis for Multi-Camera Activity Understanding</a></td><td><a href="http://www.eecs.qmul.ac.uk/~ccloy/files/ijcv_2010.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td>edu</td><td>Queen Mary University of London</td><td>United Kingdom</td><td>51.52472720</td><td>-0.03931035</td><td>63%</td><td>84</td><td>53</td><td>31</td><td>4</td><td>51</td><td>33</td></tr><tr><td>64e0690dd176a93de9d4328f6e31fc4afe1e7536</td><td>duke_mtmc</td><td>Duke MTMC</td><td><a href="papers/64e0690dd176a93de9d4328f6e31fc4afe1e7536.html" target="_blank">Tracking Multiple People Online and in Real Time</a></td><td><a href="https://pdfs.semanticscholar.org/64e0/690dd176a93de9d4328f6e31fc4afe1e7536.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>78%</td><td>23</td><td>18</td><td>5</td><td>1</td><td>12</td><td>10</td></tr><tr><td>298cbc3dfbbb3a20af4eed97906650a4ea1c29e0</td><td>ferplus</td><td>FER+</td><td><a href="papers/298cbc3dfbbb3a20af4eed97906650a4ea1c29e0.html" target="_blank">Training deep networks for facial expression recognition with crowd-sourced label distribution</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>74%</td><td>34</td><td>25</td><td>9</td><td>0</td><td>18</td><td>16</td></tr><tr><td>4eab317b5ac436a949849ed286baa3de2a541eef</td><td>laofiw</td><td>LAOFIW</td><td><a href="papers/4eab317b5ac436a949849ed286baa3de2a541eef.html" target="_blank">Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings</a></td><td><a href="https://arxiv.org/pdf/1809.02169.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>2</td><td>2</td><td>0</td><td>0</td><td>2</td><td>0</td></tr><tr><td>b5f2846a506fc417e7da43f6a7679146d99c5e96</td><td>ucf_101</td><td>UCF101</td><td><a href="papers/b5f2846a506fc417e7da43f6a7679146d99c5e96.html" target="_blank">UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild</a></td><td><a href="https://arxiv.org/pdf/1212.0402.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>64%</td><td>999</td><td>642</td><td>357</td><td>56</td><td>628</td><td>362</td></tr><tr><td>16e8b0a1e8451d5f697b94c0c2b32a00abee1d52</td><td>umb</td><td>UMB</td><td><a href="papers/16e8b0a1e8451d5f697b94c0c2b32a00abee1d52.html" target="_blank">UMB-DB: A database of partially occluded 3D faces</a></td><td><span class="gray">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td></td><td></td><td></td><td></td><td></td><td>66%</td><td>47</td><td>31</td><td>16</td><td>2</td><td>22</td><td>24</td></tr><tr><td>31b05f65405534a696a847dd19c621b7b8588263</td><td>umd_faces</td><td>UMD</td><td><a href="papers/31b05f65405534a696a847dd19c621b7b8588263.html" target="_blank">UMDFaces: An annotated face dataset for training deep networks</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE International Joint Conference on Biometrics (IJCB)</td><td>edu</td><td>University of Maryland</td><td>United States</td><td>39.28996850</td><td>-76.62196103</td><td>79%</td><td>42</td><td>33</td><td>9</td><td>2</td><td>30</td><td>11</td></tr><tr><td>8627f019882b024aef92e4eb9355c499c733e5b7</td><td>used</td><td>USED Social Event Dataset</td><td><a href="papers/8627f019882b024aef92e4eb9355c499c733e5b7.html" target="_blank">USED: a large-scale social event detection dataset</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td>edu</td><td>University of Trento</td><td>Italy</td><td>46.06588360</td><td>11.11598940</td><td>71%</td><td>7</td><td>5</td><td>2</td><td>0</td><td>3</td><td>4</td></tr><tr><td>d4f1eb008eb80595bcfdac368e23ae9754e1e745</td><td>uccs</td><td>UCCS</td><td><a href="papers/d4f1eb008eb80595bcfdac368e23ae9754e1e745.html" target="_blank">Unconstrained Face Detection and Open-Set Face Recognition Challenge</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE International Joint Conference on Biometrics (IJCB)</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>5</td><td>5</td><td>0</td><td>0</td><td>4</td><td>1</td></tr><tr><td>4b4106614c1d553365bad75d7866bff0de6056ed</td><td>ufi</td><td>UFI</td><td><a href="papers/4b4106614c1d553365bad75d7866bff0de6056ed.html" target="_blank">Unconstrained Facial Images: Database for Face Recognition Under Real-World Conditions</a></td><td><a href="http://home.zcu.cz/~pkral/papers/kral_micai15.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>12</td><td>6</td><td>6</td><td>0</td><td>4</td><td>6</td></tr><tr><td>08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7</td><td>kin_face</td><td>UB KinFace</td><td><a href="papers/08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7.html" target="_blank">Understanding Kin Relationships in a Photo</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Multimedia</td><td></td><td></td><td></td><td></td><td></td><td>63%</td><td>94</td><td>59</td><td>35</td><td>1</td><td>33</td><td>61</td></tr><tr><td>5a4df9bef1872865f0b619ac3aacc97f49e4a035</td><td>cuhk_train_station</td><td>CUHK Train Station Dataset</td><td><a href="papers/5a4df9bef1872865f0b619ac3aacc97f49e4a035.html" target="_blank">Understanding collective crowd behaviors: Learning a Mixture model of Dynamic pedestrian-Agents</a></td><td><span class="gray">[pdf]</a></td><td>2012 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>Chinese University of Hong Kong</td><td>China</td><td>22.41626320</td><td>114.21093180</td><td>58%</td><td>141</td><td>82</td><td>59</td><td>5</td><td>60</td><td>75</td></tr><tr><td>21d9d0deed16f0ad62a4865e9acf0686f4f15492</td><td>images_of_groups</td><td>Images of Groups</td><td><a href="papers/21d9d0deed16f0ad62a4865e9acf0686f4f15492.html" target="_blank">Understanding images of groups of people</a></td><td><span class="gray">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>Carnegie Mellon University</td><td>United States</td><td>40.44416190</td><td>-79.94272826</td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>15e1af79939dbf90790b03d8aa02477783fb1d0f</td><td>duke_mtmc</td><td>Duke MTMC</td><td><a href="papers/15e1af79939dbf90790b03d8aa02477783fb1d0f.html" target="_blank">Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro</a></td><td><a href="https://arxiv.org/pdf/1701.07717.pdf" target="_blank">[pdf]</a></td><td>2017 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>fd8168f1c50de85bac58a8d328df0a50248b16ae</td><td>nd_2006</td><td>ND-2006</td><td><a href="papers/fd8168f1c50de85bac58a8d328df0a50248b16ae.html" target="_blank">Using a Multi-Instance Enrollment Representation to Improve 3D Face Recognition</a></td><td><span class="gray">[pdf]</a></td><td>2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems</td><td>edu</td><td>University of Notre Dame</td><td>United States</td><td>41.70456775</td><td>-86.23822026</td><td>63%</td><td>35</td><td>22</td><td>13</td><td>3</td><td>18</td><td>15</td></tr><tr><td>4563b46d42079242f06567b3f2e2f7a80cb3befe</td><td>vadana</td><td>VADANA</td><td><a href="papers/4563b46d42079242f06567b3f2e2f7a80cb3befe.html" target="_blank">VADANA: A dense dataset for facial image analysis</a></td><td><span class="gray">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td>edu</td><td>University of Delaware</td><td>United States</td><td>39.68103280</td><td>-75.75401840</td><td>67%</td><td>15</td><td>10</td><td>5</td><td>0</td><td>5</td><td>10</td></tr><tr><td>70c59dc3470ae867016f6ab0e008ac8ba03774a1</td><td>vgg_faces2</td><td>VGG Face2</td><td><a href="papers/70c59dc3470ae867016f6ab0e008ac8ba03774a1.html" target="_blank">VGGFace2: A Dataset for Recognising Faces across Pose and Age</a></td><td><a href="https://arxiv.org/pdf/1710.08092.pdf" target="_blank">[pdf]</a></td><td>2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)</td><td></td><td></td><td></td><td></td><td></td><td>80%</td><td>83</td><td>66</td><td>17</td><td>3</td><td>61</td><td>20</td></tr><tr><td>01959ef569f74c286956024866c1d107099199f7</td><td>vqa</td><td>VQA</td><td><a href="papers/01959ef569f74c286956024866c1d107099199f7.html" target="_blank">VQA: Visual Question Answering</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>b6c293f0420f7e945b5916ae44269fb53e139275</td><td>erce</td><td>ERCe</td><td><a href="papers/b6c293f0420f7e945b5916ae44269fb53e139275.html" target="_blank">Video Synopsis by Heterogeneous Multi-source Correlation</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>29</td><td>15</td><td>14</td><td>2</td><td>14</td><td>13</td></tr><tr><td>b6c293f0420f7e945b5916ae44269fb53e139275</td><td>tisi</td><td>Times Square Intersection</td><td><a href="papers/b6c293f0420f7e945b5916ae44269fb53e139275.html" target="_blank">Video Synopsis by Heterogeneous Multi-source Correlation</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>29</td><td>15</td><td>14</td><td>2</td><td>14</td><td>13</td></tr><tr><td>5194cbd51f9769ab25260446b4fa17204752e799</td><td>violent_flows</td><td>Violent Flows</td><td><a href="papers/5194cbd51f9769ab25260446b4fa17204752e799.html" target="_blank">Violent flows: Real-time detection of violent crowd behavior</a></td><td><span class="gray">[pdf]</a></td><td>2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops</td><td>edu</td><td>Open University of Israel</td><td>Israel</td><td>32.77824165</td><td>34.99565673</td><td>65%</td><td>88</td><td>57</td><td>31</td><td>6</td><td>45</td><td>44</td></tr><tr><td>026e3363b7f76b51cc711886597a44d5f1fd1de2</td><td>kitti</td><td>KITTI</td><td><a href="papers/026e3363b7f76b51cc711886597a44d5f1fd1de2.html" target="_blank">Vision meets robotics: The KITTI dataset</a></td><td><a href="https://pdfs.semanticscholar.org/026e/3363b7f76b51cc711886597a44d5f1fd1de2.pdf" target="_blank">[pdf]</a></td><td>I. J. Robotics Res.</td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>999</td><td>602</td><td>397</td><td>36</td><td>553</td><td>462</td></tr><tr><td>066000d44d6691d27202896691f08b27117918b9</td><td>psu</td><td>PSU</td><td><a href="papers/066000d44d6691d27202896691f08b27117918b9.html" target="_blank">Vision-Based Analysis of Small Groups in Pedestrian Crowds</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>168</td><td>90</td><td>78</td><td>10</td><td>85</td><td>79</td></tr><tr><td>dd65f71dac86e36eecbd3ed225d016c3336b4a13</td><td>families_in_the_wild</td><td>FIW</td><td><a href="papers/dd65f71dac86e36eecbd3ed225d016c3336b4a13.html" target="_blank">Visual Kinship Recognition of Families in the Wild</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td>edu</td><td>University of Massachusetts Dartmouth</td><td>United States</td><td>41.62772475</td><td>-71.00724501</td><td>80%</td><td>5</td><td>4</td><td>1</td><td>0</td><td>2</td><td>3</td></tr><tr><td>8875ae233bc074f5cd6c4ebba447b536a7e847a5</td><td>voxceleb2</td><td>VoxCeleb2</td><td><a href="papers/8875ae233bc074f5cd6c4ebba447b536a7e847a5.html" target="_blank">VoxCeleb2: Deep Speaker Recognition.</a></td><td><a href="https://pdfs.semanticscholar.org/8875/ae233bc074f5cd6c4ebba447b536a7e847a5.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>6%</td><td>34</td><td>2</td><td>31</td><td>1</td><td>3</td><td>2</td></tr><tr><td>52d7eb0fbc3522434c13cc247549f74bb9609c5d</td><td>wider_face</td><td>WIDER FACE</td><td><a href="papers/52d7eb0fbc3522434c13cc247549f74bb9609c5d.html" target="_blank">WIDER FACE: A Face Detection Benchmark</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>Chinese University of Hong Kong</td><td>China</td><td>22.41626320</td><td>114.21093180</td><td>66%</td><td>178</td><td>117</td><td>61</td><td>12</td><td>112</td><td>66</td></tr><tr><td>36bccfb2ad847096bc76777e544f305813cd8f5b</td><td>wildtrack</td><td>WildTrack</td><td><a href="papers/36bccfb2ad847096bc76777e544f305813cd8f5b.html" target="_blank">WILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection</a></td><td><a href="http://openaccess.thecvf.com/content_cvpr_2018/Supplemental/1562-supp.pdf" target="_blank">[pdf]</a></td><td>2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>5ad4e9f947c1653c247d418f05dad758a3f9277b</td><td>wlfdb</td><td>WLFDB</td><td><a href="papers/5ad4e9f947c1653c247d418f05dad758a3f9277b.html" target="_blank">WLFDB : Weakly Labeled Face Databases</a></td><td><a href="https://pdfs.semanticscholar.org/5ad4/e9f947c1653c247d418f05dad758a3f9277b.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><td>0dc11a37cadda92886c56a6fb5191ded62099c28</td><td>stickmen_family</td><td>We Are Family Stickmen</td><td><a href="papers/0dc11a37cadda92886c56a6fb5191ded62099c28.html" target="_blank">We Are Family: Joint Pose Estimation of Multiple Persons</a></td><td><a href="http://eprints.pascal-network.org/archive/00007964/01/eichner10eccv.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>64%</td><td>78</td><td>50</td><td>28</td><td>5</td><td>54</td><td>23</td></tr><tr><td>0c91808994a250d7be332400a534a9291ca3b60e</td><td>graz</td><td>Graz Pedestrian</td><td><a href="papers/0c91808994a250d7be332400a534a9291ca3b60e.html" target="_blank">Weak Hypotheses and Boosting for Generic Object Detection and Recognition</a></td><td><a href="https://pdfs.semanticscholar.org/0c91/808994a250d7be332400a534a9291ca3b60e.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>56%</td><td>236</td><td>131</td><td>105</td><td>17</td><td>161</td><td>77</td></tr><tr><td>2a75f34663a60ab1b04a0049ed1d14335129e908</td><td>mmi_facial_expression</td><td>MMI Facial Expression Dataset</td><td><a href="papers/2a75f34663a60ab1b04a0049ed1d14335129e908.html" target="_blank">Web-based database for facial expression analysis</a></td><td><span class="gray">[pdf]</a></td><td>2005 IEEE International Conference on Multimedia and Expo</td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>464</td><td>250</td><td>214</td><td>45</td><td>282</td><td>188</td></tr><tr><td>9b9bf5e623cb8af7407d2d2d857bc3f1b531c182</td><td>who_goes_there</td><td>WGT</td><td><a href="papers/9b9bf5e623cb8af7407d2d2d857bc3f1b531c182.html" target="_blank">Who goes there?: approaches to mapping facial appearance diversity</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td>edu</td><td>University of Kentucky</td><td>United States</td><td>38.03337420</td><td>-84.50177580</td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>b62628ac06bbac998a3ab825324a41a11bc3a988</td><td>m2vtsdb_extended</td><td>xm2vtsdb</td><td><a href="papers/b62628ac06bbac998a3ab825324a41a11bc3a988.html" target="_blank">XM2VTSDB : The extended M2VTS database</a></td><td><a href="https://pdfs.semanticscholar.org/b626/28ac06bbac998a3ab825324a41a11bc3a988.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>864</td><td>539</td><td>325</td><td>37</td><td>493</td><td>404</td></tr><tr><td>010f0f4929e6a6644fb01f0e43820f91d0fad292</td><td>yfcc_100m</td><td>YFCC100M</td><td><a href="papers/010f0f4929e6a6644fb01f0e43820f91d0fad292.html" target="_blank">YFCC100M: the new data in multimedia research</a></td><td><span class="gray">[pdf]</a></td><td>Commun. ACM</td><td>edu</td><td>Carnegie Mellon University</td><td>United States</td><td>40.44416190</td><td>-79.94272826</td><td>64%</td><td>274</td><td>175</td><td>99</td><td>23</td><td>172</td><td>100</td></tr><tr><td>a94cae786d515d3450d48267e12ca954aab791c4</td><td>yawdd</td><td>YawDD</td><td><a href="papers/a94cae786d515d3450d48267e12ca954aab791c4.html" target="_blank">YawDD: a yawning detection dataset</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>80%</td><td>15</td><td>12</td><td>3</td><td>1</td><td>2</td><td>13</td></tr></table></body></html>
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+<!doctype html><html><head><meta charset='utf-8'><title>All Papers</title><link rel='stylesheet' href='reports.css'></head><body><h2>All Papers</h2><table border='1' cellpadding='3' cellspacing='3'><th>Paper ID</th><th>Megapixels Key</th><th>Megapixels Name</th><th>Report Link</th><th>PDF Link</th><th>Journal</th><th>Type</th><th>Address</th><th>Country</th><th>Lat</th><th>Lng</th><th>Coverage</th><th>Total Citations</th><th>Geocoded Citations</th><th>Unknown Citations</th><th>Empty Citations</th><th>With PDF</th><th>With DOI</th><tr><td>3325860c0c82a93b2eac654f5324dd6a776f609e</td><td>mpii_human_pose</td><td>MPII Human Pose</td><td><a href="papers/3325860c0c82a93b2eac654f5324dd6a776f609e.html" target="_blank">2D Human Pose Estimation: New Benchmark and State of the Art Analysis</a></td><td><span class="gray">[pdf]</a></td><td>2014 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>387</td><td>251</td><td>136</td><td>21</td><td>291</td><td>96</td></tr><tr><td>e4754afaa15b1b53e70743880484b8d0736990ff</td><td>fiw_300</td><td>300-W</td><td><a href="papers/e4754afaa15b1b53e70743880484b8d0736990ff.html" target="_blank">300 Faces In-The-Wild Challenge: database and results</a></td><td><a href="http://ibug.doc.ic.ac.uk/media/uploads/documents/1-s2.0-s0262885616000147-main.pdf" target="_blank">[pdf]</a></td><td>Image Vision Comput.</td><td>edu</td><td>Imperial College London</td><td>United Kingdom</td><td>51.49887085</td><td>-0.17560797</td><td>71%</td><td>129</td><td>92</td><td>37</td><td>6</td><td>74</td><td>55</td></tr><tr><td>044d9a8c61383312cdafbcc44b9d00d650b21c70</td><td>fiw_300</td><td>300-W</td><td><a href="papers/044d9a8c61383312cdafbcc44b9d00d650b21c70.html" target="_blank">300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision Workshops</td><td></td><td></td><td></td><td></td><td></td><td>79%</td><td>323</td><td>255</td><td>68</td><td>15</td><td>208</td><td>120</td></tr><tr><td>2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e</td><td>3dpes</td><td>3DPeS</td><td><a href="papers/2e8d0f1802e50cccfd3c0aabac0d0beab3a7846e.html" target="_blank">3DPeS: 3D people dataset for surveillance and forensics</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>133</td><td>82</td><td>51</td><td>9</td><td>73</td><td>58</td></tr><tr><td>a40f9bfd3c45658ee8da70e1f2dfbe1f0c744d43</td><td>4dfab</td><td>4DFAB</td><td><a href="papers/a40f9bfd3c45658ee8da70e1f2dfbe1f0c744d43.html" target="_blank">4DFAB: A Large Scale 4D Facial Expression Database for Biometric Applications</a></td><td><a href="https://arxiv.org/pdf/1712.01443.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>25%</td><td>4</td><td>1</td><td>3</td><td>0</td><td>2</td><td>2</td></tr><tr><td>31b58ced31f22eab10bd3ee2d9174e7c14c27c01</td><td>tiny_images</td><td>Tiny Images</td><td><a href="papers/31b58ced31f22eab10bd3ee2d9174e7c14c27c01.html" target="_blank">80 Million Tiny Images: A Large Data Set for Nonparametric Object and Scene Recognition</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>999</td><td>574</td><td>425</td><td>89</td><td>644</td><td>337</td></tr><tr><td>d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae</td><td>b3d_ac</td><td>B3D(AC)</td><td><a href="papers/d08cc366a4a0192a01e9a7495af1eb5d9f9e73ae.html" target="_blank">A 3-D Audio-Visual Corpus of Affective Communication</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Multimedia</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>42</td><td>23</td><td>19</td><td>2</td><td>26</td><td>15</td></tr><tr><td>4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b38461</td><td>3dddb_unconstrained</td><td>3D Dynamic</td><td><a href="papers/4d4bb462c9f1d4e4ab1e4aa6a75cc0bc71b38461.html" target="_blank">A 3D Dynamic Database for Unconstrained Face Recognition</a></td><td><a href="https://pdfs.semanticscholar.org/4d4b/b462c9f1d4e4ab1e4aa6a75cc0bc71b38461.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>2</td><td>1</td><td>1</td><td>0</td><td>1</td><td>1</td></tr><tr><td>639937b3a1b8bded3f7e9a40e85bd3770016cf3c</td><td>bfm</td><td>BFM</td><td><a href="papers/639937b3a1b8bded3f7e9a40e85bd3770016cf3c.html" target="_blank">A 3D Face Model for Pose and Illumination Invariant Face Recognition</a></td><td><span class="gray">[pdf]</a></td><td>2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>343</td><td>194</td><td>149</td><td>23</td><td>223</td><td>114</td></tr><tr><td>cc589c499dcf323fe4a143bbef0074c3e31f9b60</td><td>bu_3dfe</td><td>BU-3DFE</td><td><a href="papers/cc589c499dcf323fe4a143bbef0074c3e31f9b60.html" target="_blank">A 3D facial expression database for facial behavior research</a></td><td><span class="gray">[pdf]</a></td><td>7th International Conference on Automatic Face and Gesture Recognition (FGR06)</td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>588</td><td>316</td><td>271</td><td>44</td><td>306</td><td>282</td></tr><tr><td>22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b</td><td>saivt</td><td>SAIVT SoftBio</td><td><a href="papers/22646e00a7ba34d1b5fbe3b1efcd91a1e1be3c2b.html" target="_blank">A Database for Person Re-Identification in Multi-Camera Surveillance Networks</a></td><td><span class="gray">[pdf]</a></td><td>2012 International Conference on Digital Image Computing Techniques and Applications (DICTA)</td><td></td><td></td><td></td><td></td><td></td><td>58%</td><td>65</td><td>38</td><td>27</td><td>6</td><td>45</td><td>20</td></tr><tr><td>070de852bc6eb275d7ca3a9cdde8f6be8795d1a3</td><td>d3dfacs</td><td>D3DFACS</td><td><a href="papers/070de852bc6eb275d7ca3a9cdde8f6be8795d1a3.html" target="_blank">A FACS valid 3D dynamic action unit database with applications to 3D dynamic morphable facial modeling</a></td><td><span class="gray">[pdf]</a></td><td>2011 International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>50</td><td>26</td><td>24</td><td>5</td><td>31</td><td>18</td></tr><tr><td>563c940054e4b456661762c1ab858e6f730c3159</td><td>data_61</td><td>Data61 Pedestrian</td><td><a href="papers/563c940054e4b456661762c1ab858e6f730c3159.html" target="_blank">A Multi-modal Graphical Model for Scene Analysis</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE Winter Conference on Applications of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>8</td><td>4</td><td>4</td><td>0</td><td>5</td><td>3</td></tr><tr><td>221c18238b829c12b911706947ab38fd017acef7</td><td>rap_pedestrian</td><td>RAP</td><td><a href="papers/221c18238b829c12b911706947ab38fd017acef7.html" target="_blank">A Richly Annotated Dataset for Pedestrian Attribute Recognition</a></td><td><a href="https://arxiv.org/pdf/1603.07054.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>69%</td><td>26</td><td>18</td><td>8</td><td>0</td><td>16</td><td>10</td></tr><tr><td>013909077ad843eb6df7a3e8e290cfd5575999d2</td><td>fiw_300</td><td>300-W</td><td><a href="papers/013909077ad843eb6df7a3e8e290cfd5575999d2.html" target="_blank">A Semi-automatic Methodology for Facial Landmark Annotation</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops</td><td></td><td></td><td></td><td></td><td></td><td>80%</td><td>184</td><td>148</td><td>36</td><td>8</td><td>120</td><td>67</td></tr><tr><td>3b4ec8af470948a72a6ed37a9fd226719a874ebc</td><td>sdu_vid</td><td>SDU-VID</td><td><a href="papers/3b4ec8af470948a72a6ed37a9fd226719a874ebc.html" target="_blank">A Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>66%</td><td>95</td><td>63</td><td>32</td><td>6</td><td>50</td><td>45</td></tr><tr><td>6403117f9c005ae81f1e8e6d1302f4a045e3d99d</td><td>alert_airport</td><td>ALERT Airport</td><td><a href="papers/6403117f9c005ae81f1e8e6d1302f4a045e3d99d.html" target="_blank">A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>45%</td><td>20</td><td>9</td><td>11</td><td>0</td><td>9</td><td>11</td></tr><tr><td>0d3bb75852098b25d90f31d2f48fd0cb4944702b</td><td>face_scrub</td><td>FaceScrub</td><td><a href="papers/0d3bb75852098b25d90f31d2f48fd0cb4944702b.html" target="_blank">A data-driven approach to cleaning large face datasets</a></td><td><span class="gray">[pdf]</a></td><td>2014 IEEE International Conference on Image Processing (ICIP)</td><td></td><td></td><td></td><td></td><td></td><td>64%</td><td>138</td><td>89</td><td>49</td><td>1</td><td>95</td><td>41</td></tr><tr><td>b91f54e1581fbbf60392364323d00a0cd43e493c</td><td>bp4d_spontanous</td><td>BP4D-Spontanous</td><td><a href="papers/b91f54e1581fbbf60392364323d00a0cd43e493c.html" target="_blank">A high-resolution spontaneous 3D dynamic facial expression database</a></td><td><span class="gray">[pdf]</a></td><td>2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)</td><td>edu</td><td>SUNY Binghamton</td><td>United States</td><td>42.08779975</td><td>-75.97066066</td><td>52%</td><td>154</td><td>80</td><td>74</td><td>6</td><td>80</td><td>75</td></tr><tr><td>8b56e33f33e582f3e473dba573a16b598ed9bcdc</td><td>fei</td><td>FEI</td><td><a href="papers/8b56e33f33e582f3e473dba573a16b598ed9bcdc.html" target="_blank">A new ranking method for principal components analysis and its application to face image analysis</a></td><td><span class="gray">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>169</td><td>93</td><td>76</td><td>6</td><td>69</td><td>102</td></tr><tr><td>2624d84503bc2f8e190e061c5480b6aa4d89277a</td><td>afew_va</td><td>AFEW-VA</td><td><a href="papers/2624d84503bc2f8e190e061c5480b6aa4d89277a.html" target="_blank">AFEW-VA database for valence and arousal estimation in-the-wild</a></td><td><a href="https://ibug.doc.ic.ac.uk/media/uploads/documents/afew-va.pdf" target="_blank">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>18</td><td>9</td><td>9</td><td>0</td><td>12</td><td>5</td></tr><tr><td>2ad0ee93d029e790ebb50574f403a09854b65b7e</td><td>yale_faces</td><td>YaleFaces</td><td><a href="papers/2ad0ee93d029e790ebb50574f403a09854b65b7e.html" target="_blank">Acquiring linear subspaces for face recognition under variable lighting</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>999</td><td>554</td><td>445</td><td>94</td><td>495</td><td>491</td></tr><tr><td>57fe081950f21ca03b5b375ae3e84b399c015861</td><td>cvc_01_barcelona</td><td>CVC-01</td><td><a href="papers/57fe081950f21ca03b5b375ae3e84b399c015861.html" target="_blank">Adaptive Image Sampling and Windows Classification for On-board Pedestrian Detection</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>51%</td><td>47</td><td>24</td><td>23</td><td>1</td><td>23</td><td>24</td></tr><tr><td>758d7e1be64cc668c59ef33ba8882c8597406e53</td><td>affectnet</td><td>AffectNet</td><td><a href="papers/758d7e1be64cc668c59ef33ba8882c8597406e53.html" target="_blank">AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild</a></td><td><a href="https://arxiv.org/pdf/1708.03985.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>37</td><td>23</td><td>14</td><td>0</td><td>25</td><td>11</td></tr><tr><td>47aeb3b82f54b5ae8142b4bdda7b614433e69b9a</td><td>am_fed</td><td>AM-FED</td><td><a href="papers/47aeb3b82f54b5ae8142b4bdda7b614433e69b9a.html" target="_blank">Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops</td><td></td><td></td><td></td><td></td><td></td><td>46%</td><td>83</td><td>38</td><td>45</td><td>6</td><td>43</td><td>39</td></tr><tr><td>1be498d4bbc30c3bfd0029114c784bc2114d67c0</td><td>adience</td><td>Adience</td><td><a href="papers/1be498d4bbc30c3bfd0029114c784bc2114d67c0.html" target="_blank">Age and Gender Estimation of Unfiltered Faces</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Information Forensics and Security</td><td>edu</td><td>Open University of Israel</td><td>Israel</td><td>32.77824165</td><td>34.99565673</td><td>87%</td><td>179</td><td>156</td><td>23</td><td>1</td><td>98</td><td>80</td></tr><tr><td>d818568838433a6d6831adde49a58cef05e0c89f</td><td>agedb</td><td>AgeDB</td><td><a href="papers/d818568838433a6d6831adde49a58cef05e0c89f.html" target="_blank">AgeDB: The First Manually Collected, In-the-Wild Age Database</a></td><td><a href="http://eprints.mdx.ac.uk/22044/1/agedb_kotsia.pdf" target="_blank">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</td><td>edu</td><td>Imperial College London</td><td>United Kingdom</td><td>51.49887085</td><td>-0.17560797</td><td>94%</td><td>18</td><td>17</td><td>1</td><td>0</td><td>14</td><td>3</td></tr><tr><td>a74251efa970b92925b89eeef50a5e37d9281ad0</td><td>aflw</td><td>AFLW</td><td><a href="papers/a74251efa970b92925b89eeef50a5e37d9281ad0.html" target="_blank">Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization</a></td><td><span class="gray">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td>edu</td><td>TU Graz</td><td>Austria</td><td>47.07071400</td><td>15.43950400</td><td>69%</td><td>318</td><td>218</td><td>100</td><td>27</td><td>211</td><td>107</td></tr><tr><td>2ce2560cf59db59ce313bbeb004e8ce55c5ce928</td><td>texas_3dfrd</td><td>Texas 3DFRD</td><td><a href="papers/2ce2560cf59db59ce313bbeb004e8ce55c5ce928.html" target="_blank">Anthropometric 3D Face Recognition</a></td><td><a href="http://live.ece.utexas.edu/publications/2010/sg_ijcv_june10.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>63%</td><td>91</td><td>57</td><td>34</td><td>5</td><td>60</td><td>31</td></tr><tr><td>633c851ebf625ad7abdda2324e9de093cf623141</td><td>appa_real</td><td>APPA-REAL</td><td><a href="papers/633c851ebf625ad7abdda2324e9de093cf623141.html" target="_blank">Apparent and Real Age Estimation in Still Images with Deep Residual Regressors on Appa-Real Database</a></td><td><span class="gray">[pdf]</a></td><td>2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017)</td><td></td><td></td><td></td><td></td><td></td><td>70%</td><td>10</td><td>7</td><td>3</td><td>0</td><td>8</td><td>3</td></tr><tr><td>0df0d1adea39a5bef318b74faa37de7f3e00b452</td><td>mpii_gaze</td><td>MPIIGaze</td><td><a href="papers/0df0d1adea39a5bef318b74faa37de7f3e00b452.html" target="_blank">Appearance-based gaze estimation in the wild</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>73%</td><td>149</td><td>109</td><td>40</td><td>3</td><td>94</td><td>54</td></tr><tr><td>759a3b3821d9f0e08e0b0a62c8b693230afc3f8d</td><td>pubfig</td><td>PubFig</td><td><a href="papers/759a3b3821d9f0e08e0b0a62c8b693230afc3f8d.html" target="_blank">Attribute and simile classifiers for face verification</a></td><td><span class="gray">[pdf]</a></td><td>2009 IEEE 12th International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>64%</td><td>914</td><td>585</td><td>329</td><td>47</td><td>586</td><td>316</td></tr><tr><td>faf40ce28857aedf183e193486f5b4b0a8c478a2</td><td>iit_dehli_ear</td><td>IIT Dehli Ear</td><td><a href="papers/faf40ce28857aedf183e193486f5b4b0a8c478a2.html" target="_blank">Automated Human Identification Using Ear Imaging</a></td><td><a href="https://pdfs.semanticscholar.org/faf4/0ce28857aedf183e193486f5b4b0a8c478a2.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>80</td><td>40</td><td>40</td><td>6</td><td>35</td><td>44</td></tr><tr><td>2160788824c4c29ffe213b2cbeb3f52972d73f37</td><td>3d_rma</td><td>3D-RMA</td><td><a href="papers/2160788824c4c29ffe213b2cbeb3f52972d73f37.html" target="_blank">Automatic 3D face authentication</a></td><td><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.31.9190&rep=rep1&type=pdf" target="_blank">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>100</td><td>54</td><td>46</td><td>8</td><td>63</td><td>36</td></tr><tr><td>213a579af9e4f57f071b884aa872651372b661fd</td><td>bbc_pose</td><td>BBC Pose</td><td><a href="papers/213a579af9e4f57f071b884aa872651372b661fd.html" target="_blank">Automatic and Efficient Human Pose Estimation for Sign Language Videos</a></td><td><a href="http://tomas.pfister.fi/files/charles13ijcv.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>26</td><td>17</td><td>9</td><td>1</td><td>16</td><td>11</td></tr><tr><td>fcc6fe6007c322641796cb8792718641856a22a7</td><td>miw</td><td>MIW</td><td><a href="papers/fcc6fe6007c322641796cb8792718641856a22a7.html" target="_blank">Automatic facial makeup detection with application in face recognition</a></td><td><span class="gray">[pdf]</a></td><td>2013 International Conference on Biometrics (ICB)</td><td>edu</td><td>West Virginia University</td><td>United States</td><td>39.65404635</td><td>-79.96475355</td><td>71%</td><td>49</td><td>35</td><td>14</td><td>1</td><td>19</td><td>29</td></tr><tr><td>fcc6fe6007c322641796cb8792718641856a22a7</td><td>youtube_makeup</td><td>YMU</td><td><a href="papers/fcc6fe6007c322641796cb8792718641856a22a7.html" target="_blank">Automatic facial makeup detection with application in face recognition</a></td><td><span class="gray">[pdf]</a></td><td>2013 International Conference on Biometrics (ICB)</td><td>edu</td><td>West Virginia University</td><td>United States</td><td>39.65404635</td><td>-79.96475355</td><td>71%</td><td>49</td><td>35</td><td>14</td><td>1</td><td>19</td><td>29</td></tr><tr><td>0a85bdff552615643dd74646ac881862a7c7072d</td><td>pipa</td><td>PIPA</td><td><a href="papers/0a85bdff552615643dd74646ac881862a7c7072d.html" target="_blank">Beyond frontal faces: Improving Person Recognition using multiple cues</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>company</td><td>Facebook</td><td>United States</td><td>37.39367170</td><td>-122.08072620</td><td>91%</td><td>54</td><td>49</td><td>4</td><td>1</td><td>41</td><td>12</td></tr><tr><td>2acf7e58f0a526b957be2099c10aab693f795973</td><td>bosphorus</td><td>The Bosphorus</td><td><a href="papers/2acf7e58f0a526b957be2099c10aab693f795973.html" target="_blank">Bosphorus Database for 3D Face Analysis</a></td><td><a href="https://pdfs.semanticscholar.org/4254/fbba3846008f50671edc9cf70b99d7304543.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>56%</td><td>352</td><td>198</td><td>154</td><td>17</td><td>162</td><td>188</td></tr><tr><td>37d6f0eb074d207b53885bd2eb78ccc8a04be597</td><td>vmu</td><td>VMU</td><td><a href="papers/37d6f0eb074d207b53885bd2eb78ccc8a04be597.html" target="_blank">Can facial cosmetics affect the matching accuracy of face recognition systems?</a></td><td><span class="gray">[pdf]</a></td><td>2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td>edu</td><td>West Virginia University</td><td>United States</td><td>39.65404635</td><td>-79.96475355</td><td>62%</td><td>53</td><td>33</td><td>20</td><td>0</td><td>19</td><td>31</td></tr><tr><td>37d6f0eb074d207b53885bd2eb78ccc8a04be597</td><td>youtube_makeup</td><td>YMU</td><td><a href="papers/37d6f0eb074d207b53885bd2eb78ccc8a04be597.html" target="_blank">Can facial cosmetics affect the matching accuracy of face recognition systems?</a></td><td><span class="gray">[pdf]</a></td><td>2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td>edu</td><td>West Virginia University</td><td>United States</td><td>39.65404635</td><td>-79.96475355</td><td>62%</td><td>53</td><td>33</td><td>20</td><td>0</td><td>19</td><td>31</td></tr><tr><td>8d5998cd984e7cce307da7d46f155f9db99c6590</td><td>chalearn</td><td>ChaLearn</td><td><a href="papers/8d5998cd984e7cce307da7d46f155f9db99c6590.html" target="_blank">ChaLearn looking at people: A review of events and resources</a></td><td><span class="gray">[pdf]</a></td><td>2017 International Joint Conference on Neural Networks (IJCNN)</td><td></td><td></td><td></td><td></td><td></td><td>69%</td><td>13</td><td>9</td><td>4</td><td>1</td><td>8</td><td>4</td></tr><tr><td>2bf8541199728262f78d4dced6fb91479b39b738</td><td>clothing_co_parsing</td><td>CCP</td><td><a href="papers/2bf8541199728262f78d4dced6fb91479b39b738.html" target="_blank">Clothing Co-parsing by Joint Image Segmentation and Labeling</a></td><td><span class="gray">[pdf]</a></td><td>2014 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>70%</td><td>60</td><td>42</td><td>18</td><td>0</td><td>34</td><td>28</td></tr><tr><td>22ad2c8c0f4d6aa4328b38d894b814ec22579761</td><td>gallagher</td><td>Gallagher</td><td><a href="papers/22ad2c8c0f4d6aa4328b38d894b814ec22579761.html" target="_blank">Clothing cosegmentation for recognizing people</a></td><td><span class="gray">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>Carnegie Mellon University</td><td>United States</td><td>40.44416190</td><td>-79.94272826</td><td>65%</td><td>178</td><td>116</td><td>62</td><td>7</td><td>100</td><td>86</td></tr><tr><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td><td>leeds_sports_pose</td><td>Leeds Sports Pose</td><td><a href="papers/4b1d23d17476fcf78f4cbadf69fb130b1aa627c0.html" target="_blank">Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</a></td><td><a href="http://www.bmva.org/bmvc/2010/conference/paper12/abstract12.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>285</td><td>185</td><td>100</td><td>13</td><td>197</td><td>93</td></tr><tr><td>4b1d23d17476fcf78f4cbadf69fb130b1aa627c0</td><td>stickmen_buffy</td><td>Buffy Stickmen</td><td><a href="papers/4b1d23d17476fcf78f4cbadf69fb130b1aa627c0.html" target="_blank">Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation</a></td><td><a href="http://www.bmva.org/bmvc/2010/conference/paper12/abstract12.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>285</td><td>185</td><td>100</td><td>13</td><td>197</td><td>93</td></tr><tr><td>45c31cde87258414f33412b3b12fc5bec7cb3ba9</td><td>jaffe</td><td>JAFFE</td><td><a href="papers/45c31cde87258414f33412b3b12fc5bec7cb3ba9.html" target="_blank">Coding Facial Expressions with Gabor Wavelets</a></td><td><a href="http://physics.lbl.gov/patrecog/images/Facerecog_gabor.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>899</td><td>508</td><td>391</td><td>51</td><td>431</td><td>451</td></tr><tr><td>b1f4423c227fa37b9680787be38857069247a307</td><td>afew_va</td><td>AFEW-VA</td><td><a href="papers/b1f4423c227fa37b9680787be38857069247a307.html" target="_blank">Collecting Large, Richly Annotated Facial-Expression Databases from Movies</a></td><td><span class="gray">[pdf]</a></td><td>IEEE MultiMedia</td><td>edu</td><td>Australian National University</td><td>Australia</td><td>-35.27769990</td><td>149.11852700</td><td>64%</td><td>181</td><td>115</td><td>66</td><td>8</td><td>87</td><td>97</td></tr><tr><td>7f4040b482d16354d5938c1d1b926b544652bf5b</td><td>nova_emotions</td><td>Novaemötions Dataset</td><td><a href="papers/7f4040b482d16354d5938c1d1b926b544652bf5b.html" target="_blank">Competitive affective gaming: winning with a smile</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Universidade NOVA de Lisboa, Caparica, Portugal</td><td>Portugal</td><td>38.66096400</td><td>-9.20581300</td><td>78%</td><td>9</td><td>7</td><td>2</td><td>0</td><td>5</td><td>4</td></tr><tr><td>079a0a3bf5200994e1f972b1b9197bf2f90e87d4</td><td>mit_cbcl</td><td>MIT CBCL</td><td><a href="papers/079a0a3bf5200994e1f972b1b9197bf2f90e87d4.html" target="_blank">Component-Based Face Recognition with 3D Morphable Models</a></td><td><span class="gray">[pdf]</a></td><td>2004 Conference on Computer Vision and Pattern Recognition Workshop</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>23fc83c8cfff14a16df7ca497661264fc54ed746</td><td>cohn_kanade</td><td>CK</td><td><a href="papers/23fc83c8cfff14a16df7ca497661264fc54ed746.html" target="_blank">Comprehensive Database for Facial Expression Analysis</a></td><td><a href="https://pdfs.semanticscholar.org/23fc/83c8cfff14a16df7ca497661264fc54ed746.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>999</td><td>553</td><td>446</td><td>69</td><td>540</td><td>439</td></tr><tr><td>09d78009687bec46e70efcf39d4612822e61cb8c</td><td>raid</td><td>RAiD</td><td><a href="papers/09d78009687bec46e70efcf39d4612822e61cb8c.html" target="_blank">Consistent Re-identification in a Camera Network</a></td><td><a href="https://pdfs.semanticscholar.org/c27f/099e6e7e3f7f9979cbe9e0a5175fc5848ea0.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>49</td><td>35</td><td>14</td><td>3</td><td>34</td><td>13</td></tr><tr><td>0ceda9dae8b9f322df65ca2ef02caca9758aec6f</td><td>casablanca</td><td>Casablanca</td><td><a href="papers/0ceda9dae8b9f322df65ca2ef02caca9758aec6f.html" target="_blank">Context-Aware CNNs for Person Head Detection</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>33</td><td>20</td><td>13</td><td>1</td><td>23</td><td>11</td></tr><tr><td>0ceda9dae8b9f322df65ca2ef02caca9758aec6f</td><td>hollywood_headset</td><td>HollywoodHeads</td><td><a href="papers/0ceda9dae8b9f322df65ca2ef02caca9758aec6f.html" target="_blank">Context-Aware CNNs for Person Head Detection</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>33</td><td>20</td><td>13</td><td>1</td><td>23</td><td>11</td></tr><tr><td>c06b13d0ec3f5c43e2782cd22542588e233733c3</td><td>nova_emotions</td><td>Novaemötions Dataset</td><td><a href="papers/c06b13d0ec3f5c43e2782cd22542588e233733c3.html" target="_blank">Crowdsourcing facial expressions for affective-interaction</a></td><td><span class="gray">[pdf]</a></td><td>Computer Vision and Image Understanding</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><td>8355d095d3534ef511a9af68a3b2893339e3f96b</td><td>imdb_wiki</td><td>IMDB-Wiki</td><td><a href="papers/8355d095d3534ef511a9af68a3b2893339e3f96b.html" target="_blank">DEX: Deep EXpectation of Apparent Age from a Single Image</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision Workshop (ICCVW)</td><td></td><td></td><td></td><td></td><td></td><td>78%</td><td>122</td><td>95</td><td>27</td><td>4</td><td>75</td><td>48</td></tr><tr><td>5a5f0287484f0d480fed1ce585dbf729586f0edc</td><td>disfa</td><td>DISFA</td><td><a href="papers/5a5f0287484f0d480fed1ce585dbf729586f0edc.html" target="_blank">DISFA: A Spontaneous Facial Action Intensity Database</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Affective Computing</td><td>edu</td><td>University of Denver</td><td>United States</td><td>39.67665410</td><td>-104.96220300</td><td>54%</td><td>184</td><td>100</td><td>84</td><td>17</td><td>96</td><td>89</td></tr><tr><td>10195a163ab6348eef37213a46f60a3d87f289c5</td><td>imdb_wiki</td><td>IMDB-Wiki</td><td><a href="papers/10195a163ab6348eef37213a46f60a3d87f289c5.html" target="_blank">Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks</a></td><td><a href="http://www.vision.ee.ethz.ch/en/publications/papers/articles/eth_biwi_01299.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td>edu</td><td>ETH Zurich</td><td>Switzerland</td><td>47.37631300</td><td>8.54766990</td><td>72%</td><td>145</td><td>105</td><td>40</td><td>9</td><td>93</td><td>51</td></tr><tr><td>162ea969d1929ed180cc6de9f0bf116993ff6e06</td><td>vgg_faces</td><td>VGG Face</td><td><a href="papers/162ea969d1929ed180cc6de9f0bf116993ff6e06.html" target="_blank">Deep Face Recognition</a></td><td><a href="https://pdfs.semanticscholar.org/f372/ab9b3270d4e4f6a0258c83c2736c3a5c0454.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>999</td><td>646</td><td>353</td><td>48</td><td>558</td><td>429</td></tr><tr><td>6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4</td><td>celeba</td><td>CelebA</td><td><a href="papers/6424b69f3ff4d35249c0bb7ef912fbc2c86f4ff4.html" target="_blank">Deep Learning Face Attributes in the Wild</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td>edu</td><td>Chinese University of Hong Kong</td><td>China</td><td>22.41626320</td><td>114.21093180</td><td>57%</td><td>919</td><td>526</td><td>392</td><td>61</td><td>694</td><td>201</td></tr><tr><td>18010284894ed0edcca74e5bf768ee2e15ef7841</td><td>deep_fashion</td><td>DeepFashion</td><td><a href="papers/18010284894ed0edcca74e5bf768ee2e15ef7841.html" target="_blank">DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>64%</td><td>176</td><td>112</td><td>64</td><td>2</td><td>113</td><td>62</td></tr><tr><td>6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3</td><td>cuhk_campus_03</td><td>CUHK03 Campus</td><td><a href="papers/6bd36e9fd0ef20a3074e1430a6cc601e6d407fc3.html" target="_blank">DeepReID: Deep Filter Pairing Neural Network for Person Re-identification</a></td><td><span class="gray">[pdf]</a></td><td>2014 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>73%</td><td>568</td><td>412</td><td>156</td><td>19</td><td>320</td><td>235</td></tr><tr><td>13f06b08f371ba8b5d31c3e288b4deb61335b462</td><td>eth_andreas_ess</td><td>ETHZ Pedestrian</td><td><a href="papers/13f06b08f371ba8b5d31c3e288b4deb61335b462.html" target="_blank">Depth and Appearance for Mobile Scene Analysis</a></td><td><span class="gray">[pdf]</a></td><td>2007 IEEE 11th International Conference on Computer Vision</td><td>edu</td><td>ETH Zurich</td><td>Switzerland</td><td>47.37631300</td><td>8.54766990</td><td>63%</td><td>324</td><td>203</td><td>121</td><td>26</td><td>193</td><td>127</td></tr><tr><td>4946ba10a4d5a7d0a38372f23e6622bd347ae273</td><td>coco_action</td><td>COCO-a</td><td><a href="papers/4946ba10a4d5a7d0a38372f23e6622bd347ae273.html" target="_blank">Describing Common Human Visual Actions in Images</a></td><td><a href="https://arxiv.org/pdf/1506.02203.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>68%</td><td>25</td><td>17</td><td>8</td><td>0</td><td>23</td><td>2</td></tr><tr><td>7808937b46acad36e43c30ae4e9f3fd57462853d</td><td>bpad</td><td>BPAD</td><td><a href="papers/7808937b46acad36e43c30ae4e9f3fd57462853d.html" target="_blank">Describing people: A poselet-based approach to attribute classification</a></td><td><span class="gray">[pdf]</a></td><td>2011 International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>230</td><td>140</td><td>90</td><td>14</td><td>163</td><td>66</td></tr><tr><td>d3200d49a19a4a4e4e9745ee39649b65d80c834b</td><td>scut_head</td><td>SCUT HEAD</td><td><a href="papers/d3200d49a19a4a4e4e9745ee39649b65d80c834b.html" target="_blank">Detecting Heads using Feature Refine Net and Cascaded Multi-scale Architecture</a></td><td><a href="https://arxiv.org/pdf/1803.09256.pdf" target="_blank">[pdf]</a></td><td>2018 24th International Conference on Pattern Recognition (ICPR)</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>9cc8cf0c7d7fa7607659921b6ff657e17e135ecc</td><td>mafa</td><td>MAsked FAces</td><td><a href="papers/9cc8cf0c7d7fa7607659921b6ff657e17e135ecc.html" target="_blank">Detecting Masked Faces in the Wild with LLE-CNNs</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>80%</td><td>5</td><td>4</td><td>1</td><td>0</td><td>4</td><td>1</td></tr><tr><td>56ae6d94fc6097ec4ca861f0daa87941d1c10b70</td><td>cmdp</td><td>CMDP</td><td><a href="papers/56ae6d94fc6097ec4ca861f0daa87941d1c10b70.html" target="_blank">Distance Estimation of an Unknown Person from a Portrait</a></td><td><a href="http://authors.library.caltech.edu/49084/13/FaceDistanceEstimation_RONCHI.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>44%</td><td>9</td><td>4</td><td>5</td><td>0</td><td>6</td><td>3</td></tr><tr><td>84fe5b4ac805af63206012d29523a1e033bc827e</td><td>awe_ears</td><td>AWE Ears</td><td><a href="papers/84fe5b4ac805af63206012d29523a1e033bc827e.html" target="_blank">Ear Recognition: More Than a Survey</a></td><td><a href="https://arxiv.org/pdf/1611.06203.pdf" target="_blank">[pdf]</a></td><td>Neurocomputing</td><td></td><td></td><td></td><td></td><td></td><td>77%</td><td>26</td><td>20</td><td>6</td><td>0</td><td>10</td><td>16</td></tr><tr><td>133f01aec1534604d184d56de866a4bd531dac87</td><td>lfw</td><td>LFW</td><td><a href="papers/133f01aec1534604d184d56de866a4bd531dac87.html" target="_blank">Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>183</td><td>109</td><td>74</td><td>13</td><td>103</td><td>77</td></tr><tr><td>c900e0ad4c95948baaf0acd8449fde26f9b4952a</td><td>emotio_net</td><td>EmotioNet Database</td><td><a href="papers/c900e0ad4c95948baaf0acd8449fde26f9b4952a.html" target="_blank">EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>86</td><td>45</td><td>41</td><td>7</td><td>54</td><td>29</td></tr><tr><td>2161f6b7ee3c0acc81603b01dc0df689683577b9</td><td>large_scale_person_search</td><td>Large Scale Person Search</td><td><a href="papers/2161f6b7ee3c0acc81603b01dc0df689683577b9.html" target="_blank">End-to-End Deep Learning for Person Search</a></td><td><a href="https://pdfs.semanticscholar.org/2161/f6b7ee3c0acc81603b01dc0df689683577b9.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>70%</td><td>46</td><td>32</td><td>14</td><td>0</td><td>27</td><td>16</td></tr><tr><td>1bd1645a629f1b612960ab9bba276afd4cf7c666</td><td>brainwash</td><td>Brainwash</td><td><a href="papers/1bd1645a629f1b612960ab9bba276afd4cf7c666.html" target="_blank">End-to-End People Detection in Crowded Scenes</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>Stanford University</td><td>United States</td><td>37.43131385</td><td>-122.16936535</td><td>55%</td><td>42</td><td>23</td><td>19</td><td>1</td><td>19</td><td>19</td></tr><tr><td>6273b3491e94ea4dd1ce42b791d77bdc96ee73a8</td><td>viper</td><td>VIPeR</td><td><a href="papers/6273b3491e94ea4dd1ce42b791d77bdc96ee73a8.html" target="_blank">Evaluating Appearance Models for Recognition, Reacquisition, and Tracking</a></td><td><a href="https://pdfs.semanticscholar.org/7847/b1fbccadb780b655e72c66d3f9e93ddb880c.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>edu</td><td>University of California, Santa Cruz</td><td>United States</td><td>36.99158470</td><td>-122.05827710</td><td>66%</td><td>624</td><td>414</td><td>210</td><td>33</td><td>342</td><td>276</td></tr><tr><td>2258e01865367018ed6f4262c880df85b94959f8</td><td>mot</td><td>MOT</td><td><a href="papers/2258e01865367018ed6f4262c880df85b94959f8.html" target="_blank">Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics</a></td><td><a href="https://cvhci.anthropomatik.kit.edu/images/stories/msmmi/papers/eurasip2008.pdf" target="_blank">[pdf]</a></td><td>EURASIP J. Image and Video Processing</td><td></td><td></td><td></td><td></td><td></td><td>58%</td><td>632</td><td>366</td><td>264</td><td>44</td><td>358</td><td>264</td></tr><tr><td>9e5378e7b336c89735d3bb15cf67eff96f86d39a</td><td>precarious</td><td>Precarious</td><td><a href="papers/9e5378e7b336c89735d3bb15cf67eff96f86d39a.html" target="_blank">Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>36%</td><td>14</td><td>5</td><td>9</td><td>0</td><td>12</td><td>1</td></tr><tr><td>35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62</td><td>coco_qa</td><td>COCO QA</td><td><a href="papers/35b0331dfcd2897abd5749b49ff5e2b8ba0f7a62.html" target="_blank">Exploring Models and Data for Image Question Answering</a></td><td><a href="https://arxiv.org/pdf/1505.02074.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>206</td><td>126</td><td>80</td><td>11</td><td>162</td><td>39</td></tr><tr><td>2cd7821fcf5fae53a185624f7eeda007434ae037</td><td>geofaces</td><td>GeoFaces</td><td><a href="papers/2cd7821fcf5fae53a185624f7eeda007434ae037.html" target="_blank">Exploring the geo-dependence of human face appearance</a></td><td><a href="http://cs.uky.edu/~jacobs/papers/islam2014faces.pdf" target="_blank">[pdf]</a></td><td>IEEE Winter Conference on Applications of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>88%</td><td>8</td><td>7</td><td>1</td><td>0</td><td>5</td><td>3</td></tr><tr><td>2cd7821fcf5fae53a185624f7eeda007434ae037</td><td>geofaces</td><td>GeoFaces</td><td><a href="papers/2cd7821fcf5fae53a185624f7eeda007434ae037.html" target="_blank">Exploring the geo-dependence of human face appearance</a></td><td><a href="http://cs.uky.edu/~jacobs/papers/islam2014faces.pdf" target="_blank">[pdf]</a></td><td>IEEE Winter Conference on Applications of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>88%</td><td>8</td><td>7</td><td>1</td><td>0</td><td>5</td><td>3</td></tr><tr><td>75da1df4ed319926c544eefe17ec8d720feef8c0</td><td>fddb</td><td>FDDB</td><td><a href="papers/75da1df4ed319926c544eefe17ec8d720feef8c0.html" target="_blank">FDDB: A benchmark for face detection in unconstrained settings</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>380</td><td>247</td><td>133</td><td>16</td><td>202</td><td>164</td></tr><tr><td>31de9b3dd6106ce6eec9a35991b2b9083395fd0b</td><td>feret</td><td>FERET</td><td><a href="papers/31de9b3dd6106ce6eec9a35991b2b9083395fd0b.html" target="_blank">FERET ( Face Recognition Technology ) Recognition Algorithm Development and Test Results</a></td><td><a href="https://pdfs.semanticscholar.org/31de/9b3dd6106ce6eec9a35991b2b9083395fd0b.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>75</td><td>39</td><td>36</td><td>5</td><td>54</td><td>20</td></tr><tr><td>0e986f51fe45b00633de9fd0c94d082d2be51406</td><td>afw</td><td>AFW</td><td><a href="papers/0e986f51fe45b00633de9fd0c94d082d2be51406.html" target="_blank">Face detection, pose estimation, and landmark localization in the wild</a></td><td><span class="gray">[pdf]</a></td><td>2012 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>999</td><td>709</td><td>290</td><td>35</td><td>576</td><td>422</td></tr><tr><td>560e0e58d0059259ddf86fcec1fa7975dee6a868</td><td>youtube_faces</td><td>YouTubeFaces</td><td><a href="papers/560e0e58d0059259ddf86fcec1fa7975dee6a868.html" target="_blank">Face recognition in unconstrained videos with matched background similarity</a></td><td><span class="gray">[pdf]</a></td><td>CVPR 2011</td><td>edu</td><td>Tel Aviv University</td><td>Israel</td><td>32.11198890</td><td>34.80459702</td><td>66%</td><td>509</td><td>338</td><td>170</td><td>23</td><td>294</td><td>216</td></tr><tr><td>670637d0303a863c1548d5b19f705860a23e285c</td><td>face_tracer</td><td>FaceTracer</td><td><a href="papers/670637d0303a863c1548d5b19f705860a23e285c.html" target="_blank">Face swapping: automatically replacing faces in photographs</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>6204776d31359d129a582057c2d788a14f8aadeb</td><td>youtube_celebrities</td><td>YouTube Celebrities</td><td><a href="papers/6204776d31359d129a582057c2d788a14f8aadeb.html" target="_blank">Face tracking and recognition with visual constraints in real-world videos</a></td><td><span class="gray">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>Rutgers University</td><td>United States</td><td>40.47913175</td><td>-74.43168868</td><td>57%</td><td>267</td><td>151</td><td>115</td><td>11</td><td>125</td><td>121</td></tr><tr><td>4c170a0dcc8de75587dae21ca508dab2f9343974</td><td>face_tracer</td><td>FaceTracer</td><td><a href="papers/4c170a0dcc8de75587dae21ca508dab2f9343974.html" target="_blank">FaceTracer: A Search Engine for Large Collections of Images with Faces</a></td><td><a href="https://pdfs.semanticscholar.org/73a8/1d311eedac8dea3ca24dc15b6990fa4a725e.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>64%</td><td>225</td><td>144</td><td>81</td><td>17</td><td>146</td><td>77</td></tr><tr><td>7ebb153704706e457ab57b432793d2b6e5d12592</td><td>vgg_celebs_in_places</td><td>CIP</td><td><a href="papers/7ebb153704706e457ab57b432793d2b6e5d12592.html" target="_blank">Faces in Places: compound query retrieval</a></td><td><a href="https://pdfs.semanticscholar.org/7ebb/153704706e457ab57b432793d2b6e5d12592.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>5</td><td>5</td><td>0</td><td>0</td><td>3</td><td>2</td></tr><tr><td>0ab7cff2ccda7269b73ff6efd9d37e1318f7db25</td><td>ibm_dif</td><td>IBM Diversity in Faces</td><td><a href="papers/0ab7cff2ccda7269b73ff6efd9d37e1318f7db25.html" target="_blank">Facial Coding Scheme Reference 1 Craniofacial Distances</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>8a3c5507237957d013a0fe0f082cab7f757af6ee</td><td>mafl</td><td>MAFL</td><td><a href="papers/8a3c5507237957d013a0fe0f082cab7f757af6ee.html" target="_blank">Facial Landmark Detection by Deep Multi-task Learning</a></td><td><a href="https://pdfs.semanticscholar.org/fcd7/1c18192928a2e0b264edd4d919ab2f8f652a.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>70%</td><td>407</td><td>283</td><td>124</td><td>16</td><td>252</td><td>153</td></tr><tr><td>8a3c5507237957d013a0fe0f082cab7f757af6ee</td><td>mtfl</td><td>MTFL</td><td><a href="papers/8a3c5507237957d013a0fe0f082cab7f757af6ee.html" target="_blank">Facial Landmark Detection by Deep Multi-task Learning</a></td><td><a href="https://pdfs.semanticscholar.org/fcd7/1c18192928a2e0b264edd4d919ab2f8f652a.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>70%</td><td>407</td><td>283</td><td>124</td><td>16</td><td>252</td><td>153</td></tr><tr><td>4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7</td><td>deep_fashion</td><td>DeepFashion</td><td><a href="papers/4fefd1bc8dc4e0ab37ee3324ddfa43ad9d6a04a7.html" target="_blank">Fashion Landmark Detection in the Wild</a></td><td><a href="https://arxiv.org/pdf/1608.03049.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>73%</td><td>26</td><td>19</td><td>7</td><td>1</td><td>16</td><td>10</td></tr><tr><td>060820f110a72cbf02c14a6d1085bd6e1d994f6a</td><td>caltech_crp</td><td>Caltech CRP</td><td><a href="papers/060820f110a72cbf02c14a6d1085bd6e1d994f6a.html" target="_blank">Fine-grained classification of pedestrians in video: Benchmark and state of the art</a></td><td><a href="https://arxiv.org/pdf/1605.06177.pdf" target="_blank">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>47%</td><td>17</td><td>8</td><td>9</td><td>0</td><td>9</td><td>8</td></tr><tr><td>45e616093a92e5f1e61a7c6037d5f637aa8964af</td><td>malf</td><td>MALF</td><td><a href="papers/45e616093a92e5f1e61a7c6037d5f637aa8964af.html" target="_blank">Fine-grained evaluation on face detection in the wild</a></td><td><span class="gray">[pdf]</a></td><td>2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>17</td><td>12</td><td>5</td><td>0</td><td>12</td><td>5</td></tr><tr><td>1aad2da473888cb7ebc1bfaa15bfa0f1502ce005</td><td>jpl_pose</td><td>JPL-Interaction dataset</td><td><a href="papers/1aad2da473888cb7ebc1bfaa15bfa0f1502ce005.html" target="_blank">First-Person Activity Recognition: What Are They Doing to Me?</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>67%</td><td>148</td><td>99</td><td>49</td><td>7</td><td>105</td><td>43</td></tr><tr><td>7b92d1e53cc87f7a4256695de590098a2f30261e</td><td>appa_real</td><td>APPA-REAL</td><td><a href="papers/7b92d1e53cc87f7a4256695de590098a2f30261e.html" target="_blank">From Apparent to Real Age: Gender, Age, Ethnic, Makeup, and Expression Bias Analysis in Real Age Estimation</a></td><td><span class="gray">[pdf]</a></td><td>2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>774cbb45968607a027ae4729077734db000a1ec5</td><td>urban_tribes</td><td>Urban Tribes</td><td><a href="papers/774cbb45968607a027ae4729077734db000a1ec5.html" target="_blank">From Bikers to Surfers: Visual Recognition of Urban Tribes</a></td><td><a href="http://vision.cornell.edu/se3/wp-content/uploads/2014/09/utribes_bmvc13_final.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>67%</td><td>18</td><td>12</td><td>6</td><td>1</td><td>12</td><td>6</td></tr><tr><td>22f656d0f8426c84a33a267977f511f127bfd7f3</td><td>expw</td><td>ExpW</td><td><a href="papers/22f656d0f8426c84a33a267977f511f127bfd7f3.html" target="_blank">From Facial Expression Recognition to Interpersonal Relation Prediction</a></td><td><a href="https://arxiv.org/pdf/1609.06426.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>11</td><td>6</td><td>5</td><td>0</td><td>5</td><td>4</td></tr><tr><td>18c72175ddbb7d5956d180b65a96005c100f6014</td><td>yale_faces</td><td>YaleFaces</td><td><a href="papers/18c72175ddbb7d5956d180b65a96005c100f6014.html" target="_blank">From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose</a></td><td><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.147.1487&rep=rep1&type=pdf" target="_blank">[pdf]</a></td><td>IEEE Trans. Pattern Anal. Mach. Intell.</td><td></td><td></td><td></td><td></td><td></td><td>56%</td><td>999</td><td>561</td><td>438</td><td>66</td><td>498</td><td>462</td></tr><tr><td>06f02199690961ba52997cde1527e714d2b3bf8f</td><td>columbia_gaze</td><td>Columbia Gaze</td><td><a href="papers/06f02199690961ba52997cde1527e714d2b3bf8f.html" target="_blank">Gaze locking: passive eye contact detection for human-object interaction</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Columbia University</td><td>United States</td><td>40.84198360</td><td>-73.94368971</td><td>76%</td><td>79</td><td>60</td><td>19</td><td>0</td><td>49</td><td>34</td></tr><tr><td>18858cc936947fc96b5c06bbe3c6c2faa5614540</td><td>pilot_parliament</td><td>PPB</td><td><a href="papers/18858cc936947fc96b5c06bbe3c6c2faa5614540.html" target="_blank">Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification</a></td><td><a href="http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a-supp.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>53%</td><td>59</td><td>31</td><td>28</td><td>0</td><td>47</td><td>10</td></tr><tr><td>2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d</td><td>kin_face</td><td>UB KinFace</td><td><a href="papers/2eb84aaba316b095d4bb51da1a3e4365bbf9ab1d.html" target="_blank">Genealogical face recognition based on UB KinFace database</a></td><td><span class="gray">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>SUNY Buffalo</td><td>United States</td><td>42.93362780</td><td>-78.88394479</td><td>55%</td><td>31</td><td>17</td><td>14</td><td>0</td><td>11</td><td>21</td></tr><tr><td>2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9</td><td>graz</td><td>Graz Pedestrian</td><td><a href="papers/2eed184680edcdec8a3b605ad1a3ba8e8f7cc2e9.html" target="_blank">Generic object recognition with boosting</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td>edu</td><td>TU Graz</td><td>Austria</td><td>47.07071400</td><td>15.43950400</td><td>53%</td><td>293</td><td>155</td><td>138</td><td>16</td><td>195</td><td>97</td></tr><tr><td>17b46e2dad927836c689d6787ddb3387c6159ece</td><td>geofaces</td><td>GeoFaces</td><td><a href="papers/17b46e2dad927836c689d6787ddb3387c6159ece.html" target="_blank">GeoFaceExplorer: exploring the geo-dependence of facial attributes</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>2</td><td>2</td><td>0</td><td>0</td><td>1</td><td>1</td></tr><tr><td>bd88bb2e4f351352d88ee7375af834360e223498</td><td>hda_plus</td><td>HDA+</td><td><a href="papers/bd88bb2e4f351352d88ee7375af834360e223498.html" target="_blank">HDA dataset-DRAFT 1 A Multi-camera video data set for research on High-Definition surveillance</a></td><td><a href="https://pdfs.semanticscholar.org/bd88/bb2e4f351352d88ee7375af834360e223498.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>2</td><td>0</td><td>2</td><td>0</td><td>1</td><td>2</td></tr><tr><td>a8d0b149c2eadaa02204d3e4356fbc8eccf3b315</td><td>hi4d_adsip</td><td>Hi4D-ADSIP</td><td><a href="papers/a8d0b149c2eadaa02204d3e4356fbc8eccf3b315.html" target="_blank">Hi4D-ADSIP 3-D dynamic facial articulation database</a></td><td><span class="gray">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>15</td><td>9</td><td>6</td><td>1</td><td>4</td><td>11</td></tr><tr><td>2d45cfd838016a6e39f6b766ffe85acd649440c7</td><td>mcgill</td><td>McGill Real World</td><td><a href="papers/2d45cfd838016a6e39f6b766ffe85acd649440c7.html" target="_blank">Hierarchical temporal graphical model for head pose estimation and subsequent attribute classification in real-world videos</a></td><td><span class="gray">[pdf]</a></td><td>Computer Vision and Image Understanding</td><td></td><td></td><td></td><td></td><td></td><td>75%</td><td>8</td><td>6</td><td>2</td><td>0</td><td>5</td><td>3</td></tr><tr><td>3cd40bfa1ff193a96bde0207e5140a399476466c</td><td>tvhi</td><td>TVHI</td><td><a href="papers/3cd40bfa1ff193a96bde0207e5140a399476466c.html" target="_blank">High Five: Recognising human interactions in TV shows</a></td><td><a href="https://pdfs.semanticscholar.org/3cd4/0bfa1ff193a96bde0207e5140a399476466c.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>98</td><td>56</td><td>42</td><td>10</td><td>66</td><td>28</td></tr><tr><td>04c2cda00e5536f4b1508cbd80041e9552880e67</td><td>hipsterwars</td><td>Hipsterwars</td><td><a href="papers/04c2cda00e5536f4b1508cbd80041e9552880e67.html" target="_blank">Hipster Wars: Discovering Elements of Fashion Styles</a></td><td><a href="https://pdfs.semanticscholar.org/04c2/cda00e5536f4b1508cbd80041e9552880e67.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>64%</td><td>95</td><td>61</td><td>34</td><td>4</td><td>59</td><td>35</td></tr><tr><td>10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5</td><td>inria_person</td><td>INRIA Pedestrian</td><td><a href="papers/10d6b12fa07c7c8d6c8c3f42c7f1c061c131d4c5.html" target="_blank">Histograms of oriented gradients for human detection</a></td><td><span class="gray">[pdf]</a></td><td>2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)</td><td>edu</td><td>INRIA Rhone-Alps, Montbonnot, France</td><td>France</td><td>45.21788600</td><td>5.80736900</td><td>57%</td><td>999</td><td>572</td><td>427</td><td>42</td><td>419</td><td>509</td></tr><tr><td>041d3eedf5e45ce5c5229f0181c5c576ed1fafd6</td><td>ucf_selfie</td><td>UCF Selfie</td><td><a href="papers/041d3eedf5e45ce5c5229f0181c5c576ed1fafd6.html" target="_blank">How to Take a Good Selfie?</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>73%</td><td>11</td><td>8</td><td>3</td><td>0</td><td>7</td><td>5</td></tr><tr><td>44d23df380af207f5ac5b41459c722c87283e1eb</td><td>wider_attribute</td><td>WIDER Attribute</td><td><a href="papers/44d23df380af207f5ac5b41459c722c87283e1eb.html" target="_blank">Human Attribute Recognition by Deep Hierarchical Contexts</a></td><td><a href="https://pdfs.semanticscholar.org/8e28/07f2dd53b03a759e372e07f7191cae65c9fd.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>72%</td><td>18</td><td>13</td><td>5</td><td>0</td><td>14</td><td>4</td></tr><tr><td>44484d2866f222bbb9b6b0870890f9eea1ffb2d0</td><td>cuhk_campus_03</td><td>CUHK03 Campus</td><td><a href="papers/44484d2866f222bbb9b6b0870890f9eea1ffb2d0.html" target="_blank">Human Reidentification with Transferred Metric Learning</a></td><td><a href="http://www.ee.cuhk.edu.hk/~xgwang/papers/liZWaccv12.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>69%</td><td>280</td><td>194</td><td>86</td><td>9</td><td>139</td><td>137</td></tr><tr><td>f41c7bb02fc97d5fb9cadd7a49c3e558a1c58a44</td><td>pa_100k</td><td>PA-100K</td><td><a href="papers/f41c7bb02fc97d5fb9cadd7a49c3e558a1c58a44.html" target="_blank">HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>75%</td><td>55</td><td>41</td><td>14</td><td>0</td><td>36</td><td>17</td></tr><tr><td>57178b36c21fd7f4529ac6748614bb3374714e91</td><td>ijb_c</td><td>IJB-C</td><td><a href="papers/57178b36c21fd7f4529ac6748614bb3374714e91.html" target="_blank">IARPA Janus Benchmark - C: Face Dataset and Protocol</a></td><td><span class="gray">[pdf]</a></td><td>2018 International Conference on Biometrics (ICB)</td><td></td><td></td><td></td><td></td><td></td><td>79%</td><td>14</td><td>11</td><td>3</td><td>0</td><td>12</td><td>1</td></tr><tr><td>0cb2dd5f178e3a297a0c33068961018659d0f443</td><td>ijb_b</td><td>IJB-B</td><td><a href="papers/0cb2dd5f178e3a297a0c33068961018659d0f443.html" target="_blank">IARPA Janus Benchmark-B Face Dataset</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</td><td>edu</td><td>Michigan State University</td><td>United States</td><td>42.71856800</td><td>-84.47791571</td><td>63%</td><td>35</td><td>22</td><td>13</td><td>3</td><td>25</td><td>8</td></tr><tr><td>0297448f3ed948e136bb06ceff10eccb34e5bb77</td><td>ilids_mcts</td><td>i-LIDS Multiple-Camera</td><td><a href="papers/0297448f3ed948e136bb06ceff10eccb34e5bb77.html" target="_blank">Imagery Library for Intelligent Detection Systems (i-LIDS); A Standard for Testing Video Based Detection Systems</a></td><td><span class="gray">[pdf]</a></td><td>Proceedings 40th Annual 2006 International Carnahan Conference on Security Technology</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>35</td><td>20</td><td>15</td><td>2</td><td>21</td><td>14</td></tr><tr><td>7f23a4bb0c777dd72cca7665a5f370ac7980217e</td><td>duke_mtmc</td><td>Duke MTMC</td><td><a href="papers/7f23a4bb0c777dd72cca7665a5f370ac7980217e.html" target="_blank">Improving Person Re-identification by Attribute and Identity Learning</a></td><td><a href="https://arxiv.org/pdf/1703.07220.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>84%</td><td>87</td><td>73</td><td>14</td><td>0</td><td>43</td><td>42</td></tr><tr><td>55c40cbcf49a0225e72d911d762c27bb1c2d14aa</td><td>ifad</td><td>IFAD</td><td><a href="papers/55c40cbcf49a0225e72d911d762c27bb1c2d14aa.html" target="_blank">Indian Face Age Database: A Database for Face Recognition with Age Variation</a></td><td><a href="https://pdfs.semanticscholar.org/025e/4cf3fd3fdeced91e9373b56ee14af7ca432c.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>2</td><td>1</td><td>1</td><td>0</td><td>2</td><td>0</td></tr><tr><td>ca3e88d87e1344d076c964ea89d91a75c417f5ee</td><td>imfdb</td><td>IMFDB</td><td><a href="papers/ca3e88d87e1344d076c964ea89d91a75c417f5ee.html" target="_blank">Indian Movie Face Database: A benchmark for face recognition under wide variations</a></td><td><span class="gray">[pdf]</a></td><td>2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)</td><td>edu</td><td>BVBCET, Hubli, India</td><td>India</td><td>15.36883320</td><td>75.12137960</td><td>65%</td><td>17</td><td>11</td><td>6</td><td>0</td><td>11</td><td>5</td></tr><tr><td>95f12d27c3b4914e0668a268360948bce92f7db3</td><td>helen</td><td>Helen</td><td><a href="papers/95f12d27c3b4914e0668a268360948bce92f7db3.html" target="_blank">Interactive Facial Feature Localization</a></td><td><a href="https://pdfs.semanticscholar.org/95f1/2d27c3b4914e0668a268360948bce92f7db3.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>company</td><td>Adobe</td><td>United States</td><td>37.33077030</td><td>-121.89409510</td><td>85%</td><td>352</td><td>298</td><td>54</td><td>8</td><td>212</td><td>146</td></tr><tr><td>ad01687649d95cd5b56d7399a9603c4b8e2217d7</td><td>mrp_drone</td><td>MRP Drone</td><td><a href="papers/ad01687649d95cd5b56d7399a9603c4b8e2217d7.html" target="_blank">Investigating Open-World Person Re-identification Using a Drone</a></td><td><a href="https://pdfs.semanticscholar.org/ad01/687649d95cd5b56d7399a9603c4b8e2217d7.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>7</td><td>3</td><td>4</td><td>1</td><td>5</td><td>2</td></tr><tr><td>2f43b614607163abf41dfe5d17ef6749a1b61304</td><td>hrt_transgender</td><td>HRT Transgender</td><td><a href="papers/2f43b614607163abf41dfe5d17ef6749a1b61304.html" target="_blank">Investigating the Periocular-Based Face Recognition Across Gender Transformation</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Information Forensics and Security</td><td>edu</td><td>University of North Carolina at Wilmington</td><td>United States</td><td>34.22498270</td><td>-77.86907744</td><td>77%</td><td>13</td><td>10</td><td>3</td><td>0</td><td>6</td><td>8</td></tr><tr><td>066d71fcd997033dce4ca58df924397dfe0b5fd1</td><td>ifdb</td><td>IFDB</td><td><a href="papers/066d71fcd997033dce4ca58df924397dfe0b5fd1.html" target="_blank">Iranian Face Database and Evaluation with a New Detection Algorithm</a></td><td><a href="https://pdfs.semanticscholar.org/066d/71fcd997033dce4ca58df924397dfe0b5fd1.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>b71d1aa90dcbe3638888725314c0d56640c1fef1</td><td>ifdb</td><td>IFDB</td><td><a href="papers/b71d1aa90dcbe3638888725314c0d56640c1fef1.html" target="_blank">Iranian Face Database with age, pose and expression</a></td><td><span class="gray">[pdf]</a></td><td>2007 International Conference on Machine Vision</td><td>edu</td><td>Islamic Azad University</td><td>Iran</td><td>34.84529990</td><td>48.55962120</td><td>48%</td><td>23</td><td>11</td><td>12</td><td>2</td><td>14</td><td>9</td></tr><tr><td>137aa2f891d474fce1e7a1d1e9b3aefe21e22b34</td><td>hrt_transgender</td><td>HRT Transgender</td><td><a href="papers/137aa2f891d474fce1e7a1d1e9b3aefe21e22b34.html" target="_blank">Is the eye region more reliable than the face? A preliminary study of face-based recognition on a transgender dataset</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>7</td><td>4</td><td>3</td><td>1</td><td>3</td><td>5</td></tr><tr><td>0b440695c822a8e35184fb2f60dcdaa8a6de84ae</td><td>kinectface</td><td>KinectFaceDB</td><td><a href="papers/0b440695c822a8e35184fb2f60dcdaa8a6de84ae.html" target="_blank">KinectFaceDB: A Kinect Database for Face Recognition</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Systems, Man, and Cybernetics: Systems</td><td>edu</td><td>University of North Carolina at Chapel Hill</td><td>United States</td><td>35.91139710</td><td>-79.05045290</td><td>61%</td><td>82</td><td>50</td><td>32</td><td>6</td><td>28</td><td>52</td></tr><tr><td>4793f11fbca4a7dba898b9fff68f70d868e2497c</td><td>kin_face</td><td>UB KinFace</td><td><a href="papers/4793f11fbca4a7dba898b9fff68f70d868e2497c.html" target="_blank">Kinship Verification through Transfer Learning</a></td><td><a href="https://pdfs.semanticscholar.org/4793/f11fbca4a7dba898b9fff68f70d868e2497c.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>58%</td><td>71</td><td>41</td><td>30</td><td>2</td><td>29</td><td>42</td></tr><tr><td>2d3482dcff69c7417c7b933f22de606a0e8e42d4</td><td>lfw</td><td>LFW</td><td><a href="papers/2d3482dcff69c7417c7b933f22de606a0e8e42d4.html" target="_blank">Labeled Faces in the Wild : Updates and New Reporting Procedures</a></td><td><a href="https://pdfs.semanticscholar.org/2d34/82dcff69c7417c7b933f22de606a0e8e42d4.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>edu</td><td>University of Massachusetts</td><td>United States</td><td>42.38897850</td><td>-72.52869870</td><td>69%</td><td>123</td><td>85</td><td>38</td><td>3</td><td>71</td><td>51</td></tr><tr><td>370b5757a5379b15e30d619e4d3fb9e8e13f3256</td><td>lfw</td><td>LFW</td><td><a href="papers/370b5757a5379b15e30d619e4d3fb9e8e13f3256.html" target="_blank">Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments</a></td><td><a href="https://pdfs.semanticscholar.org/c6b3/ca4f939e36a9679a70e14ce8b1bbbc5618f3.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>63%</td><td>999</td><td>632</td><td>367</td><td>59</td><td>598</td><td>382</td></tr><tr><td>7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22</td><td>lfw</td><td>LFW</td><td><a href="papers/7de6e81d775e9cd7becbfd1bd685f4e2a5eebb22.html" target="_blank">Labeled Faces in the Wild: A Survey</a></td><td><a href="https://pdfs.semanticscholar.org/7de6/e81d775e9cd7becbfd1bd685f4e2a5eebb22.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Stevens Institute of Technology</td><td>United States</td><td>40.74225200</td><td>-74.02709490</td><td>64%</td><td>109</td><td>70</td><td>39</td><td>7</td><td>66</td><td>43</td></tr><tr><td>0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e</td><td>lag</td><td>LAG</td><td><a href="papers/0d2dd4fc016cb6a517d8fb43a7cc3ff62964832e.html" target="_blank">Large age-gap face verification by feature injection in deep networks</a></td><td><a href="https://arxiv.org/pdf/1602.06149.pdf" target="_blank">[pdf]</a></td><td>Pattern Recognition Letters</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>7</td><td>5</td><td>2</td><td>0</td><td>3</td><td>4</td></tr><tr><td>07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1</td><td>uccs</td><td>UCCS</td><td><a href="papers/07fcbae86f7a3ad3ea1cf95178459ee9eaf77cb1.html" target="_blank">Large scale unconstrained open set face database</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)</td><td>company</td><td>Securics Inc., Colorado Springs, CO</td><td>United States</td><td>38.83388160</td><td>-104.82136340</td><td>83%</td><td>6</td><td>5</td><td>1</td><td>0</td><td>4</td><td>2</td></tr><tr><td>4af89578ac237278be310f7660a408b03f12d603</td><td>geofaces</td><td>GeoFaces</td><td><a href="papers/4af89578ac237278be310f7660a408b03f12d603.html" target="_blank">Large-scale geo-facial image analysis</a></td><td><a href="https://pdfs.semanticscholar.org/3ede/3ed28329bf48fbd06438a69c4f855bef003f.pdf" target="_blank">[pdf]</a></td><td>EURASIP J. Image and Video Processing</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>6</td><td>6</td><td>0</td><td>0</td><td>4</td><td>2</td></tr><tr><td>a0fd85b3400c7b3e11122f44dc5870ae2de9009a</td><td>mafl</td><td>MAFL</td><td><a href="papers/a0fd85b3400c7b3e11122f44dc5870ae2de9009a.html" target="_blank">Learning Deep Representation for Face Alignment with Auxiliary Attributes</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>108</td><td>77</td><td>31</td><td>7</td><td>66</td><td>44</td></tr><tr><td>a0fd85b3400c7b3e11122f44dc5870ae2de9009a</td><td>mtfl</td><td>MTFL</td><td><a href="papers/a0fd85b3400c7b3e11122f44dc5870ae2de9009a.html" target="_blank">Learning Deep Representation for Face Alignment with Auxiliary Attributes</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>108</td><td>77</td><td>31</td><td>7</td><td>66</td><td>44</td></tr><tr><td>853bd61bc48a431b9b1c7cab10c603830c488e39</td><td>casia_webface</td><td>CASIA Webface</td><td><a href="papers/853bd61bc48a431b9b1c7cab10c603830c488e39.html" target="_blank">Learning Face Representation from Scratch</a></td><td><a href="https://arxiv.org/pdf/1411.7923.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td>edu</td><td>Chinese Academy of Sciences</td><td>China</td><td>40.00447950</td><td>116.37023800</td><td>71%</td><td>476</td><td>339</td><td>137</td><td>19</td><td>290</td><td>182</td></tr><tr><td>2a171f8d14b6b8735001a11c217af9587d095848</td><td>social_relation</td><td>Social Relation</td><td><a href="papers/2a171f8d14b6b8735001a11c217af9587d095848.html" target="_blank">Learning Social Relation Traits from Face Images</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>23</td><td>14</td><td>9</td><td>4</td><td>16</td><td>7</td></tr><tr><td>4e4746094bf60ee83e40d8597a6191e463b57f76</td><td>leeds_sports_pose_extended</td><td>Leeds Sports Pose Extended</td><td><a href="papers/4e4746094bf60ee83e40d8597a6191e463b57f76.html" target="_blank">Learning effective human pose estimation from inaccurate annotation</a></td><td><span class="gray">[pdf]</a></td><td>CVPR 2011</td><td>edu</td><td>University of Leeds</td><td>United Kingdom</td><td>53.80387185</td><td>-1.55245712</td><td>70%</td><td>169</td><td>118</td><td>51</td><td>8</td><td>108</td><td>65</td></tr><tr><td>287ddcb3db5562235d83aee318f318b8d5e43fb1</td><td>erce</td><td>ERCe</td><td><a href="papers/287ddcb3db5562235d83aee318f318b8d5e43fb1.html" target="_blank">Learning from Multiple Sources for Video Summarisation</a></td><td><a href="https://arxiv.org/pdf/1501.03069.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>7</td><td>4</td><td>3</td><td>0</td><td>4</td><td>3</td></tr><tr><td>287ddcb3db5562235d83aee318f318b8d5e43fb1</td><td>tisi</td><td>Times Square Intersection</td><td><a href="papers/287ddcb3db5562235d83aee318f318b8d5e43fb1.html" target="_blank">Learning from Multiple Sources for Video Summarisation</a></td><td><a href="https://arxiv.org/pdf/1501.03069.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>7</td><td>4</td><td>3</td><td>0</td><td>4</td><td>3</td></tr><tr><td>5981e6479c3fd4e31644db35d236bfb84ae46514</td><td>mot</td><td>MOT</td><td><a href="papers/5981e6479c3fd4e31644db35d236bfb84ae46514.html" target="_blank">Learning to associate: HybridBoosted multi-target tracker for crowded scene</a></td><td><span class="gray">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>University of Southern California</td><td>United States</td><td>34.02241490</td><td>-118.28634407</td><td>61%</td><td>326</td><td>200</td><td>125</td><td>22</td><td>190</td><td>137</td></tr><tr><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td><td>stickmen_buffy</td><td>Buffy Stickmen</td><td><a href="papers/6dd0597f8513dc100cd0bc1b493768cde45098a9.html" target="_blank">Learning to parse images of articulated bodies</a></td><td><a href="http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2006_899.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>369</td><td>227</td><td>141</td><td>32</td><td>237</td><td>131</td></tr><tr><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td><td>stickmen_pascal</td><td>Stickmen PASCAL</td><td><a href="papers/6dd0597f8513dc100cd0bc1b493768cde45098a9.html" target="_blank">Learning to parse images of articulated bodies</a></td><td><a href="http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2006_899.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>369</td><td>227</td><td>141</td><td>32</td><td>237</td><td>131</td></tr><tr><td>6dd0597f8513dc100cd0bc1b493768cde45098a9</td><td>stickmen_pascal</td><td>Stickmen PASCAL</td><td><a href="papers/6dd0597f8513dc100cd0bc1b493768cde45098a9.html" target="_blank">Learning to parse images of articulated bodies</a></td><td><a href="http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2006_899.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>369</td><td>227</td><td>141</td><td>32</td><td>237</td><td>131</td></tr><tr><td>28d4e027c7e90b51b7d8908fce68128d1964668a</td><td>megaface</td><td>MegaFace</td><td><a href="papers/28d4e027c7e90b51b7d8908fce68128d1964668a.html" target="_blank">Level Playing Field for Million Scale Face Recognition</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>University of Washington</td><td>United States</td><td>47.65432380</td><td>-122.30800894</td><td>72%</td><td>39</td><td>28</td><td>11</td><td>2</td><td>29</td><td>9</td></tr><tr><td>46a01565e6afe7c074affb752e7069ee3bf2e4ef</td><td>sdu_vid</td><td>SDU-VID</td><td><a href="papers/46a01565e6afe7c074affb752e7069ee3bf2e4ef.html" target="_blank">Local Descriptors Encoded by Fisher Vectors for Person Re-identification</a></td><td><a href="https://pdfs.semanticscholar.org/a105/f1ef67b4b02da38eadce8ffb4e13aa301a93.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>67%</td><td>197</td><td>132</td><td>65</td><td>15</td><td>108</td><td>88</td></tr><tr><td>140438a77a771a8fb656b39a78ff488066eb6b50</td><td>lfpw</td><td>LFPW</td><td><a href="papers/140438a77a771a8fb656b39a78ff488066eb6b50.html" target="_blank">Localizing Parts of Faces Using a Consensus of Exemplars</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>38b55d95189c5e69cf4ab45098a48fba407609b4</td><td>cuhk_campus_03</td><td>CUHK03 Campus</td><td><a href="papers/38b55d95189c5e69cf4ab45098a48fba407609b4.html" target="_blank">Locally Aligned Feature Transforms across Views</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>64%</td><td>258</td><td>164</td><td>94</td><td>15</td><td>136</td><td>117</td></tr><tr><td>8990cdce3f917dad622e43e033db686b354d057c</td><td>tiny_faces</td><td>TinyFace</td><td><a href="papers/8990cdce3f917dad622e43e033db686b354d057c.html" target="_blank">Low-Resolution Face Recognition</a></td><td><a href="https://arxiv.org/pdf/1811.08965.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>c0387e788a52f10bf35d4d50659cfa515d89fbec</td><td>mars</td><td>MARS</td><td><a href="papers/c0387e788a52f10bf35d4d50659cfa515d89fbec.html" target="_blank">MARS: A Video Benchmark for Large-Scale Person Re-Identification</a></td><td><a href="https://pdfs.semanticscholar.org/c038/7e788a52f10bf35d4d50659cfa515d89fbec.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>68%</td><td>168</td><td>115</td><td>53</td><td>4</td><td>97</td><td>69</td></tr><tr><td>9055b155cbabdce3b98e16e5ac9c0edf00f9552f</td><td>morph</td><td>MORPH Commercial</td><td><a href="papers/9055b155cbabdce3b98e16e5ac9c0edf00f9552f.html" target="_blank">MORPH: a longitudinal image database of normal adult age-progression</a></td><td><span class="gray">[pdf]</a></td><td>7th International Conference on Automatic Face and Gesture Recognition (FGR06)</td><td>edu</td><td>North Carolina University</td><td>United States</td><td>34.22398690</td><td>-77.87013250</td><td>59%</td><td>437</td><td>258</td><td>178</td><td>22</td><td>228</td><td>203</td></tr><tr><td>9055b155cbabdce3b98e16e5ac9c0edf00f9552f</td><td>morph_nc</td><td>MORPH Non-Commercial</td><td><a href="papers/9055b155cbabdce3b98e16e5ac9c0edf00f9552f.html" target="_blank">MORPH: a longitudinal image database of normal adult age-progression</a></td><td><span class="gray">[pdf]</a></td><td>7th International Conference on Automatic Face and Gesture Recognition (FGR06)</td><td>edu</td><td>North Carolina University</td><td>United States</td><td>34.22398690</td><td>-77.87013250</td><td>59%</td><td>437</td><td>258</td><td>178</td><td>22</td><td>228</td><td>203</td></tr><tr><td>291265db88023e92bb8c8e6390438e5da148e8f5</td><td>msceleb</td><td>MsCeleb</td><td><a href="papers/291265db88023e92bb8c8e6390438e5da148e8f5.html" target="_blank">MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition</a></td><td><a href="https://arxiv.org/pdf/1607.08221.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>company</td><td>Microsoft</td><td>United States</td><td>47.64233180</td><td>-122.13693020</td><td>78%</td><td>180</td><td>141</td><td>39</td><td>8</td><td>120</td><td>59</td></tr><tr><td>3dc3f0b64ef80f573e3a5f96e456e52ee980b877</td><td>georgia_tech_face_database</td><td>Georgia Tech Face</td><td><a href="papers/3dc3f0b64ef80f573e3a5f96e456e52ee980b877.html" target="_blank">Maximum Likelihood Training of the Embedded HMM for Face Detection and Recognition</a></td><td><a href="http://www.researchgate.net/profile/Monson_Hayes/publication/221124512_Maximum_Likelihood_Training_of_the_Embedded_HMM_for_Face_Detection_and_Recognition/links/0deec53509be9d6f55000000.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>67</td><td>36</td><td>31</td><td>4</td><td>29</td><td>28</td></tr><tr><td>e58dd160a76349d46f881bd6ddbc2921f08d1050</td><td>gfw</td><td>Grouping Face in the Wild</td><td><a href="papers/e58dd160a76349d46f881bd6ddbc2921f08d1050.html" target="_blank">Merge or Not? Learning to Group Faces via Imitation Learning</a></td><td><a href="https://arxiv.org/pdf/1707.03986.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>2</td><td>2</td><td>0</td><td>0</td><td>2</td><td>0</td></tr><tr><td>5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725</td><td>fpoq</td><td>50 People One Question</td><td><a href="papers/5753b2b5e442eaa3be066daa4a2ca8d8a0bb1725.html" target="_blank">Merging Pose Estimates Across Space and Time</a></td><td><a href="http://authors.library.caltech.edu/41565/1/tracking_bmvc.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>81%</td><td>16</td><td>13</td><td>3</td><td>0</td><td>13</td><td>4</td></tr><tr><td>5e0f8c355a37a5a89351c02f174e7a5ddcb98683</td><td>coco</td><td>COCO</td><td><a href="papers/5e0f8c355a37a5a89351c02f174e7a5ddcb98683.html" target="_blank">Microsoft COCO: Common Objects in Context</a></td><td><a href="https://arxiv.org/pdf/1405.0312.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>999</td><td>608</td><td>391</td><td>25</td><td>722</td><td>259</td></tr><tr><td>41976ebc8ab76d9a6861487c97cc7fcbe3b6015f</td><td>moments_in_time</td><td>Moments in Time</td><td><a href="papers/41976ebc8ab76d9a6861487c97cc7fcbe3b6015f.html" target="_blank">Moments in Time Dataset: one million videos for event understanding</a></td><td><a href="https://arxiv.org/pdf/1801.03150.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td>edu</td><td>Columbia University</td><td>United States</td><td>40.84198360</td><td>-73.94368971</td><td>76%</td><td>29</td><td>22</td><td>7</td><td>2</td><td>27</td><td>2</td></tr><tr><td>436f798d1a4e54e5947c1e7d7375c31b2bdb4064</td><td>tud_multiview</td><td>TUD-Multiview</td><td><a href="papers/436f798d1a4e54e5947c1e7d7375c31b2bdb4064.html" target="_blank">Monocular 3D pose estimation and tracking by detection</a></td><td><span class="gray">[pdf]</a></td><td>2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>TU Darmstadt</td><td>Germany</td><td>49.87482770</td><td>8.65632810</td><td>59%</td><td>311</td><td>185</td><td>126</td><td>34</td><td>208</td><td>105</td></tr><tr><td>436f798d1a4e54e5947c1e7d7375c31b2bdb4064</td><td>tud_stadtmitte</td><td>TUD-Stadtmitte</td><td><a href="papers/436f798d1a4e54e5947c1e7d7375c31b2bdb4064.html" target="_blank">Monocular 3D pose estimation and tracking by detection</a></td><td><span class="gray">[pdf]</a></td><td>2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>TU Darmstadt</td><td>Germany</td><td>49.87482770</td><td>8.65632810</td><td>59%</td><td>311</td><td>185</td><td>126</td><td>34</td><td>208</td><td>105</td></tr><tr><td>3b5b6d19d4733ab606c39c69a889f9e67967f151</td><td>qmul_grid</td><td>GRID</td><td><a href="papers/3b5b6d19d4733ab606c39c69a889f9e67967f151.html" target="_blank">Multi-camera activity correlation analysis</a></td><td><span class="gray">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>Queen Mary University of London</td><td>United Kingdom</td><td>51.52472720</td><td>-0.03931035</td><td>69%</td><td>142</td><td>98</td><td>44</td><td>7</td><td>77</td><td>64</td></tr><tr><td>6ad5a38df8dd4cdddd74f31996ce096d41219f72</td><td>tud_brussels</td><td>TUD-Brussels</td><td><a href="papers/6ad5a38df8dd4cdddd74f31996ce096d41219f72.html" target="_blank">Multi-cue onboard pedestrian detection</a></td><td><span class="gray">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>6ad5a38df8dd4cdddd74f31996ce096d41219f72</td><td>tud_motionpairs</td><td>TUD-Motionparis</td><td><a href="papers/6ad5a38df8dd4cdddd74f31996ce096d41219f72.html" target="_blank">Multi-cue onboard pedestrian detection</a></td><td><span class="gray">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>32c801cb7fbeb742edfd94cccfca4934baec71da</td><td>ucf_crowd</td><td>UCF-CC-50</td><td><a href="papers/32c801cb7fbeb742edfd94cccfca4934baec71da.html" target="_blank">Multi-source Multi-scale Counting in Extremely Dense Crowd Images</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>68%</td><td>148</td><td>100</td><td>48</td><td>5</td><td>80</td><td>65</td></tr><tr><td>1e3df3ca8feab0b36fd293fe689f93bb2aaac591</td><td>immediacy</td><td>Immediacy</td><td><a href="papers/1e3df3ca8feab0b36fd293fe689f93bb2aaac591.html" target="_blank">Multi-task Recurrent Neural Network for Immediacy Prediction</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>26</td><td>16</td><td>10</td><td>2</td><td>21</td><td>6</td></tr><tr><td>2b926b3586399d028b46315d7d9fb9d879e4f79c</td><td>frav3d</td><td>FRAV3D</td><td><a href="papers/2b926b3586399d028b46315d7d9fb9d879e4f79c.html" target="_blank">Multimodal 2D, 2.5D & 3D Face Verification</a></td><td><span class="gray">[pdf]</a></td><td>2006 International Conference on Image Processing</td><td>edu</td><td>Universidad Rey Juan Carlos, Spain</td><td>Spain</td><td>40.33586610</td><td>-3.87694320</td><td>57%</td><td>14</td><td>8</td><td>6</td><td>0</td><td>2</td><td>12</td></tr><tr><td>53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4</td><td>bp4d_plus</td><td>BP4D+</td><td><a href="papers/53ae38a6bb2b21b42bac4f0c4c8ed1f9fa02f9d4.html" target="_blank">Multimodal Spontaneous Emotion Corpus for Human Behavior Analysis</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>42</td><td>23</td><td>19</td><td>0</td><td>17</td><td>26</td></tr><tr><td>2fda164863a06a92d3a910b96eef927269aeb730</td><td>names_and_faces</td><td>News Dataset</td><td><a href="papers/2fda164863a06a92d3a910b96eef927269aeb730.html" target="_blank">Names and faces in the news</a></td><td><span class="gray">[pdf]</a></td><td>Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06</td><td>distance_nighttime</td><td>Long Distance Heterogeneous Face</td><td><a href="papers/4156b7e88f2e0ab0a7c095b9bab199ae2b23bd06.html" target="_blank">Nighttime Face Recognition at Long Distance: Cross-Distance and Cross-Spectral Matching</a></td><td><a href="https://pdfs.semanticscholar.org/4156/b7e88f2e0ab0a7c095b9bab199ae2b23bd06.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>22</td><td>11</td><td>11</td><td>3</td><td>11</td><td>10</td></tr><tr><td>3394168ff0719b03ff65bcea35336a76b21fe5e4</td><td>penn_fudan</td><td>Penn Fudan</td><td><a href="papers/3394168ff0719b03ff65bcea35336a76b21fe5e4.html" target="_blank">Object Detection Combining Recognition and Segmentation</a></td><td><a href="https://pdfs.semanticscholar.org/3394/168ff0719b03ff65bcea35336a76b21fe5e4.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>105</td><td>64</td><td>41</td><td>9</td><td>58</td><td>43</td></tr><tr><td>12ad3b5bbbf407f8e54ea692c07633d1a867c566</td><td>graz</td><td>Graz Pedestrian</td><td><a href="papers/12ad3b5bbbf407f8e54ea692c07633d1a867c566.html" target="_blank">Object recognition using segmentation for feature detection</a></td><td><span class="gray">[pdf]</a></td><td>Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.</td><td>edu</td><td>Inst. of Comput. Sci., Univ. of Leoben, Austria</td><td>Austria</td><td>47.38473720</td><td>15.09302010</td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>4f93cd09785c6e77bf4bc5a788e079df524c8d21</td><td>soton</td><td>SOTON HiD</td><td><a href="papers/4f93cd09785c6e77bf4bc5a788e079df524c8d21.html" target="_blank">On a Large Sequence-Based Human Gait Database</a></td><td><a href="https://pdfs.semanticscholar.org/4f93/cd09785c6e77bf4bc5a788e079df524c8d21.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>63%</td><td>150</td><td>95</td><td>55</td><td>17</td><td>103</td><td>51</td></tr><tr><td>6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c</td><td>afad</td><td>AFAD</td><td><a href="papers/6618cff7f2ed440a0d2fa9e74ad5469df5cdbe4c.html" target="_blank">Ordinal Regression with Multiple Output CNN for Age Estimation</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>78</td><td>42</td><td>36</td><td>8</td><td>44</td><td>31</td></tr><tr><td>a7fe834a0af614ce6b50dc093132b031dd9a856b</td><td>market_1501</td><td>Market 1501</td><td><a href="papers/a7fe834a0af614ce6b50dc093132b031dd9a856b.html" target="_blank">Orientation Driven Bag of Appearances for Person Re-identification</a></td><td><a href="https://arxiv.org/pdf/1605.02464.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>7</td><td>3</td><td>4</td><td>0</td><td>4</td><td>4</td></tr><tr><td>a7fe834a0af614ce6b50dc093132b031dd9a856b</td><td>pku_reid</td><td>PKU-Reid</td><td><a href="papers/a7fe834a0af614ce6b50dc093132b031dd9a856b.html" target="_blank">Orientation Driven Bag of Appearances for Person Re-identification</a></td><td><a href="https://arxiv.org/pdf/1605.02464.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>7</td><td>3</td><td>4</td><td>0</td><td>4</td><td>4</td></tr><tr><td>18ae7c9a4bbc832b8b14bc4122070d7939f5e00e</td><td>frgc</td><td>FRGC</td><td><a href="papers/18ae7c9a4bbc832b8b14bc4122070d7939f5e00e.html" target="_blank">Overview of the face recognition grand challenge</a></td><td><span class="gray">[pdf]</a></td><td>2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)</td><td>edu</td><td>NIST</td><td>United States</td><td>39.14004000</td><td>-77.21850600</td><td>57%</td><td>999</td><td>568</td><td>430</td><td>85</td><td>549</td><td>442</td></tr><tr><td>22909dd19a0ec3b6065334cb5be5392cb24d839d</td><td>pets</td><td>PETS 2017</td><td><a href="papers/22909dd19a0ec3b6065334cb5be5392cb24d839d.html" target="_blank">PETS 2017: Dataset and Challenge</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)</td><td></td><td></td><td></td><td></td><td></td><td>44%</td><td>9</td><td>4</td><td>5</td><td>0</td><td>1</td><td>8</td></tr><tr><td>56ffa7d906b08d02d6d5a12c7377a57e24ef3391</td><td>unbc_shoulder_pain</td><td>UNBC-McMaster Pain</td><td><a href="papers/56ffa7d906b08d02d6d5a12c7377a57e24ef3391.html" target="_blank">Painful data: The UNBC-McMaster shoulder pain expression archive database</a></td><td><span class="gray">[pdf]</a></td><td>Face and Gesture 2011</td><td>edu</td><td>Carnegie Mellon University</td><td>United States</td><td>40.44416190</td><td>-79.94272826</td><td>54%</td><td>189</td><td>103</td><td>86</td><td>21</td><td>108</td><td>78</td></tr><tr><td>55206f0b5f57ce17358999145506cd01e570358c</td><td>orl</td><td>ORL</td><td><a href="papers/55206f0b5f57ce17358999145506cd01e570358c.html" target="_blank">Parameterisation of a stochastic model for human face identification</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>999</td><td>501</td><td>498</td><td>94</td><td>543</td><td>427</td></tr><tr><td>0486214fb58ee9a04edfe7d6a74c6d0f661a7668</td><td>chokepoint</td><td>ChokePoint</td><td><a href="papers/0486214fb58ee9a04edfe7d6a74c6d0f661a7668.html" target="_blank">Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition</a></td><td><span class="gray">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>138</td><td>83</td><td>55</td><td>6</td><td>76</td><td>63</td></tr><tr><td>488e475eeb3bb39a145f23ede197cd3620f1d98a</td><td>apis</td><td>APiS1.0</td><td><a href="papers/488e475eeb3bb39a145f23ede197cd3620f1d98a.html" target="_blank">Pedestrian Attribute Classification in Surveillance: Database and Evaluation</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision Workshops</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>28</td><td>20</td><td>8</td><td>0</td><td>13</td><td>15</td></tr><tr><td>488e475eeb3bb39a145f23ede197cd3620f1d98a</td><td>svs</td><td>SVS</td><td><a href="papers/488e475eeb3bb39a145f23ede197cd3620f1d98a.html" target="_blank">Pedestrian Attribute Classification in Surveillance: Database and Evaluation</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision Workshops</td><td></td><td></td><td></td><td></td><td></td><td>71%</td><td>28</td><td>20</td><td>8</td><td>0</td><td>13</td><td>15</td></tr><tr><td>2a4bbee0b4cf52d5aadbbc662164f7efba89566c</td><td>peta</td><td>PETA</td><td><a href="papers/2a4bbee0b4cf52d5aadbbc662164f7efba89566c.html" target="_blank">Pedestrian Attribute Recognition At Far Distance</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>75%</td><td>88</td><td>66</td><td>22</td><td>1</td><td>50</td><td>36</td></tr><tr><td>f72f6a45ee240cc99296a287ff725aaa7e7ebb35</td><td>caltech_pedestrians</td><td>Caltech Pedestrians</td><td><a href="papers/f72f6a45ee240cc99296a287ff725aaa7e7ebb35.html" target="_blank">Pedestrian Detection: An Evaluation of the State of the Art</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td>edu</td><td>California Institute of Technology</td><td>United States</td><td>34.13710185</td><td>-118.12527487</td><td>60%</td><td>999</td><td>597</td><td>402</td><td>70</td><td>527</td><td>466</td></tr><tr><td>1dc35905a1deff8bc74688f2d7e2f48fd2273275</td><td>caltech_pedestrians</td><td>Caltech Pedestrians</td><td><a href="papers/1dc35905a1deff8bc74688f2d7e2f48fd2273275.html" target="_blank">Pedestrian detection: A benchmark</a></td><td><span class="gray">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td><td>tud_campus</td><td>TUD-Campus</td><td><a href="papers/3316521a5527c7700af8ae6aef32a79a8b83672c.html" target="_blank">People-tracking-by-detection and people-detection-by-tracking</a></td><td><span class="gray">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>59%</td><td>545</td><td>324</td><td>220</td><td>37</td><td>330</td><td>218</td></tr><tr><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td><td>tud_crossing</td><td>TUD-Crossing</td><td><a href="papers/3316521a5527c7700af8ae6aef32a79a8b83672c.html" target="_blank">People-tracking-by-detection and people-detection-by-tracking</a></td><td><span class="gray">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>59%</td><td>545</td><td>324</td><td>220</td><td>37</td><td>330</td><td>218</td></tr><tr><td>3316521a5527c7700af8ae6aef32a79a8b83672c</td><td>tud_pedestrian</td><td>TUD-Pedestrian</td><td><a href="papers/3316521a5527c7700af8ae6aef32a79a8b83672c.html" target="_blank">People-tracking-by-detection and people-detection-by-tracking</a></td><td><span class="gray">[pdf]</a></td><td>2008 IEEE Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>59%</td><td>545</td><td>324</td><td>220</td><td>37</td><td>330</td><td>218</td></tr><tr><td>27a2fad58dd8727e280f97036e0d2bc55ef5424c</td><td>duke_mtmc</td><td>Duke MTMC</td><td><a href="papers/27a2fad58dd8727e280f97036e0d2bc55ef5424c.html" target="_blank">Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking</a></td><td><a href="https://arxiv.org/pdf/1609.01775.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Duke University</td><td>United States</td><td>35.99905220</td><td>-78.92906290</td><td>85%</td><td>169</td><td>144</td><td>25</td><td>3</td><td>113</td><td>54</td></tr><tr><td>27a2fad58dd8727e280f97036e0d2bc55ef5424c</td><td>mot</td><td>MOT</td><td><a href="papers/27a2fad58dd8727e280f97036e0d2bc55ef5424c.html" target="_blank">Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking</a></td><td><a href="https://arxiv.org/pdf/1609.01775.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Duke University</td><td>United States</td><td>35.99905220</td><td>-78.92906290</td><td>85%</td><td>169</td><td>144</td><td>25</td><td>3</td><td>113</td><td>54</td></tr><tr><td>16c7c31a7553d99f1837fc6e88e77b5ccbb346b8</td><td>prid</td><td>PRID</td><td><a href="papers/16c7c31a7553d99f1837fc6e88e77b5ccbb346b8.html" target="_blank">Person Re-identification by Descriptive and Discriminative Classification</a></td><td><a href="https://pdfs.semanticscholar.org/4c1b/f0592be3e535faf256c95e27982db9b3d3d3.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>68%</td><td>386</td><td>263</td><td>123</td><td>23</td><td>204</td><td>180</td></tr><tr><td>98bb029afe2a1239c3fdab517323066f0957b81b</td><td>ilids_mcts_vid</td><td>iLIDS-VID</td><td><a href="papers/98bb029afe2a1239c3fdab517323066f0957b81b.html" target="_blank">Person Re-identification by Video Ranking</a></td><td><a href="https://pdfs.semanticscholar.org/98bb/029afe2a1239c3fdab517323066f0957b81b.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>68%</td><td>209</td><td>143</td><td>66</td><td>8</td><td>111</td><td>97</td></tr><tr><td>98bb029afe2a1239c3fdab517323066f0957b81b</td><td>sdu_vid</td><td>SDU-VID</td><td><a href="papers/98bb029afe2a1239c3fdab517323066f0957b81b.html" target="_blank">Person Re-identification by Video Ranking</a></td><td><a href="https://pdfs.semanticscholar.org/98bb/029afe2a1239c3fdab517323066f0957b81b.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>68%</td><td>209</td><td>143</td><td>66</td><td>8</td><td>111</td><td>97</td></tr><tr><td>0b84f07af44f964817675ad961def8a51406dd2e</td><td>prw</td><td>PRW</td><td><a href="papers/0b84f07af44f964817675ad961def8a51406dd2e.html" target="_blank">Person Re-identification in the Wild</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>68%</td><td>77</td><td>52</td><td>25</td><td>1</td><td>47</td><td>27</td></tr><tr><td>a0cc5f73a37723a6dd465924143f1cb4976d0169</td><td>msmt_17</td><td>MSMT17</td><td><a href="papers/a0cc5f73a37723a6dd465924143f1cb4976d0169.html" target="_blank">Person Transfer GAN to Bridge Domain Gap for Person Re-identification</a></td><td><span class="gray">[pdf]</a></td><td>2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>92%</td><td>24</td><td>22</td><td>2</td><td>1</td><td>20</td><td>4</td></tr><tr><td>1c2802c2199b6d15ecefe7ba0c39bfe44363de38</td><td>youtube_poses</td><td>YouTube Pose</td><td><a href="papers/1c2802c2199b6d15ecefe7ba0c39bfe44363de38.html" target="_blank">Personalizing Human Video Pose Estimation</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>Oxford University</td><td>United Kingdom</td><td>51.75208490</td><td>-1.25166460</td><td>64%</td><td>36</td><td>23</td><td>13</td><td>2</td><td>30</td><td>8</td></tr><tr><td>2830fb5282de23d7784b4b4bc37065d27839a412</td><td>h3d</td><td>H3D</td><td><a href="papers/2830fb5282de23d7784b4b4bc37065d27839a412.html" target="_blank">Poselets: Body part detectors trained using 3D human pose annotations</a></td><td><span class="gray">[pdf]</a></td><td>2009 IEEE 12th International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>58%</td><td>716</td><td>415</td><td>301</td><td>59</td><td>492</td><td>222</td></tr><tr><td>3765df816dc5a061bc261e190acc8bdd9d47bec0</td><td>rafd</td><td>RaFD</td><td><a href="papers/3765df816dc5a061bc261e190acc8bdd9d47bec0.html" target="_blank">Presentation and validation of the Radboud Faces Database</a></td><td><a href="https://pdfs.semanticscholar.org/3765/df816dc5a061bc261e190acc8bdd9d47bec0.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>48%</td><td>487</td><td>234</td><td>253</td><td>39</td><td>342</td><td>144</td></tr><tr><td>636b8ffc09b1b23ff714ac8350bb35635e49fa3c</td><td>caltech_10k_web_faces</td><td>Caltech 10K Web Faces</td><td><a href="papers/636b8ffc09b1b23ff714ac8350bb35635e49fa3c.html" target="_blank">Pruning training sets for learning of object categories</a></td><td><span class="gray">[pdf]</a></td><td>2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)</td><td></td><td></td><td></td><td></td><td></td><td>70%</td><td>63</td><td>44</td><td>19</td><td>4</td><td>42</td><td>20</td></tr><tr><td>3531332efe19be21e7401ba1f04570a142617236</td><td>ufdd</td><td>UFDD</td><td><a href="papers/3531332efe19be21e7401ba1f04570a142617236.html" target="_blank">Pushing the Limits of Unconstrained Face Detection: a Challenge Dataset and Baseline Results</a></td><td><a href="https://arxiv.org/pdf/1804.10275.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>75%</td><td>4</td><td>3</td><td>1</td><td>0</td><td>4</td><td>0</td></tr><tr><td>140c95e53c619eac594d70f6369f518adfea12ef</td><td>ijb_a</td><td>IJB-A</td><td><a href="papers/140c95e53c619eac594d70f6369f518adfea12ef.html" target="_blank">Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>66%</td><td>237</td><td>156</td><td>81</td><td>14</td><td>159</td><td>76</td></tr><tr><td>c72a2ea819df9b0e8cd267eebcc6528b8741e03d</td><td>megaage</td><td>MegaAge</td><td><a href="papers/c72a2ea819df9b0e8cd267eebcc6528b8741e03d.html" target="_blank">Quantifying Facial Age by Posterior of Age Comparisons</a></td><td><a href="https://arxiv.org/pdf/1708.09687.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>4</td><td>4</td><td>0</td><td>0</td><td>4</td><td>0</td></tr><tr><td>922e0a51a3b8c67c4c6ac09a577ff674cbd28b34</td><td>v47</td><td>V47</td><td><a href="papers/922e0a51a3b8c67c4c6ac09a577ff674cbd28b34.html" target="_blank">Re-identification of pedestrians with variable occlusion and scale</a></td><td><span class="gray">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td>edu</td><td>Kingston University</td><td>United Kingdom</td><td>51.42930860</td><td>-0.26840440</td><td>56%</td><td>9</td><td>5</td><td>4</td><td>1</td><td>5</td><td>4</td></tr><tr><td>6f3c76b7c0bd8e1d122c6ea808a271fd4749c951</td><td>ward</td><td>WARD</td><td><a href="papers/6f3c76b7c0bd8e1d122c6ea808a271fd4749c951.html" target="_blank">Re-identify people in wide area camera network</a></td><td><span class="gray">[pdf]</a></td><td>2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops</td><td>edu</td><td>University of Udine</td><td>Italy</td><td>46.08107230</td><td>13.21194740</td><td>60%</td><td>60</td><td>36</td><td>24</td><td>1</td><td>38</td><td>21</td></tr><tr><td>54983972aafc8e149259d913524581357b0f91c3</td><td>reseed</td><td>ReSEED</td><td><a href="papers/54983972aafc8e149259d913524581357b0f91c3.html" target="_blank">ReSEED: social event dEtection dataset</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>67%</td><td>6</td><td>4</td><td>2</td><td>1</td><td>1</td><td>5</td></tr><tr><td>65355cbb581a219bd7461d48b3afd115263ea760</td><td>complex_activities</td><td>Ongoing Complex Activities</td><td><a href="papers/65355cbb581a219bd7461d48b3afd115263ea760.html" target="_blank">Recognition of ongoing complex activities by sequence prediction over a hierarchical label space</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Winter Conference on Applications of Computer Vision (WACV)</td><td></td><td></td><td></td><td></td><td></td><td>33%</td><td>3</td><td>1</td><td>2</td><td>0</td><td>3</td><td>0</td></tr><tr><td>e8de844fefd54541b71c9823416daa238be65546</td><td>visual_phrases</td><td>Phrasal Recognition</td><td><a href="papers/e8de844fefd54541b71c9823416daa238be65546.html" target="_blank">Recognition using visual phrases</a></td><td><span class="gray">[pdf]</a></td><td>CVPR 2011</td><td>edu</td><td>University of Illinois, Urbana-Champaign</td><td>United States</td><td>40.11116745</td><td>-88.22587665</td><td>58%</td><td>246</td><td>143</td><td>103</td><td>17</td><td>170</td><td>68</td></tr><tr><td>356b431d4f7a2a0a38cf971c84568207dcdbf189</td><td>wider</td><td>WIDER</td><td><a href="papers/356b431d4f7a2a0a38cf971c84568207dcdbf189.html" target="_blank">Recognize complex events from static images by fusing deep channels</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>70%</td><td>44</td><td>31</td><td>13</td><td>1</td><td>29</td><td>15</td></tr><tr><td>25474c21613607f6bb7687a281d5f9d4ffa1f9f3</td><td>faceplace</td><td>Face Place</td><td><a href="papers/25474c21613607f6bb7687a281d5f9d4ffa1f9f3.html" target="_blank">Recognizing disguised faces</a></td><td><a href="https://pdfs.semanticscholar.org/d936/7ceb0be378c3a9ddf7cb741c678c1a3c574c.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>34%</td><td>29</td><td>10</td><td>19</td><td>0</td><td>18</td><td>10</td></tr><tr><td>4053e3423fb70ad9140ca89351df49675197196a</td><td>bio_id</td><td>BioID Face</td><td><a href="papers/4053e3423fb70ad9140ca89351df49675197196a.html" target="_blank">Robust Face Detection Using the Hausdorff Distance</a></td><td><a href="https://pdfs.semanticscholar.org/4053/e3423fb70ad9140ca89351df49675197196a.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>57%</td><td>511</td><td>289</td><td>222</td><td>49</td><td>329</td><td>182</td></tr><tr><td>2724ba85ec4a66de18da33925e537f3902f21249</td><td>cofw</td><td>COFW</td><td><a href="papers/2724ba85ec4a66de18da33925e537f3902f21249.html" target="_blank">Robust Face Landmark Estimation under Occlusion</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision</td><td>edu</td><td>California Institute of Technology</td><td>United States</td><td>34.13710185</td><td>-118.12527487</td><td>72%</td><td>325</td><td>233</td><td>92</td><td>12</td><td>194</td><td>133</td></tr><tr><td>c570d1247e337f91e555c3be0e8c8a5aba539d9f</td><td>mcgill</td><td>McGill Real World</td><td><a href="papers/c570d1247e337f91e555c3be0e8c8a5aba539d9f.html" target="_blank">Robust semi-automatic head pose labeling for real-world face video sequences</a></td><td><span class="gray">[pdf]</a></td><td>Multimedia Tools and Applications</td><td>edu</td><td>McGill University</td><td>Canada</td><td>45.50397610</td><td>-73.57496870</td><td>44%</td><td>18</td><td>8</td><td>10</td><td>0</td><td>13</td><td>7</td></tr><tr><td>e27ef52c641c2b5100a1b34fd0b819e84a31b4df</td><td>sarc3d</td><td>Sarc3D</td><td><a href="papers/e27ef52c641c2b5100a1b34fd0b819e84a31b4df.html" target="_blank">SARC3D: A New 3D Body Model for People Tracking and Re-identification</a></td><td><a href="https://pdfs.semanticscholar.org/e27e/f52c641c2b5100a1b34fd0b819e84a31b4df.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>74%</td><td>34</td><td>25</td><td>9</td><td>2</td><td>21</td><td>12</td></tr><tr><td>bd26dabab576adb6af30484183c9c9c8379bf2e0</td><td>scut_fbp</td><td>SCUT-FBP</td><td><a href="papers/bd26dabab576adb6af30484183c9c9c8379bf2e0.html" target="_blank">SCUT-FBP: A Benchmark Dataset for Facial Beauty Perception</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Systems, Man, and Cybernetics</td><td></td><td></td><td></td><td></td><td></td><td>47%</td><td>19</td><td>9</td><td>10</td><td>2</td><td>6</td><td>13</td></tr><tr><td>29a705a5fa76641e0d8963f1fdd67ee4c0d92d3d</td><td>scface</td><td>SCface</td><td><a href="papers/29a705a5fa76641e0d8963f1fdd67ee4c0d92d3d.html" target="_blank">SCface – surveillance cameras face database</a></td><td><a href="http://scface.org/SCface%20-%20Surveillance%20Cameras%20Face%20Database.pdf" target="_blank">[pdf]</a></td><td>Multimedia Tools and Applications</td><td></td><td></td><td></td><td></td><td></td><td>56%</td><td>179</td><td>101</td><td>78</td><td>15</td><td>88</td><td>89</td></tr><tr><td>d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9</td><td>stair_actions</td><td>STAIR Action</td><td><a href="papers/d3f5a1848b0028d8ab51d0b0673732cad2e3c8c9.html" target="_blank">STAIR Actions: A Video Dataset of Everyday Home Actions</a></td><td><a href="https://arxiv.org/pdf/1804.04326.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><td>833fa04463d90aab4a9fe2870d480f0b40df446e</td><td>sun_attributes</td><td>SUN</td><td><a href="papers/833fa04463d90aab4a9fe2870d480f0b40df446e.html" target="_blank">SUN attribute database: Discovering, annotating, and recognizing scene attributes</a></td><td><span class="gray">[pdf]</a></td><td>2012 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>Brown University</td><td>United States</td><td>41.82686820</td><td>-71.40123146</td><td>60%</td><td>264</td><td>159</td><td>105</td><td>27</td><td>206</td><td>56</td></tr><tr><td>4308bd8c28e37e2ed9a3fcfe74d5436cce34b410</td><td>market_1501</td><td>Market 1501</td><td><a href="papers/4308bd8c28e37e2ed9a3fcfe74d5436cce34b410.html" target="_blank">Scalable Person Re-identification: A Benchmark</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td>company</td><td>Microsoft</td><td>United States</td><td>47.64233180</td><td>-122.13693020</td><td>77%</td><td>460</td><td>355</td><td>105</td><td>9</td><td>263</td><td>185</td></tr><tr><td>9c23859ec7313f2e756a3e85575735e0c52249f4</td><td>facebook_100</td><td>Facebook100</td><td><a href="papers/9c23859ec7313f2e756a3e85575735e0c52249f4.html" target="_blank">Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook</a></td><td><span class="gray">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>Harvard University</td><td>United States</td><td>42.36782045</td><td>-71.12666653</td><td>62%</td><td>52</td><td>32</td><td>20</td><td>3</td><td>38</td><td>13</td></tr><tr><td>9c23859ec7313f2e756a3e85575735e0c52249f4</td><td>pubfig_83</td><td>pubfig83</td><td><a href="papers/9c23859ec7313f2e756a3e85575735e0c52249f4.html" target="_blank">Scaling up biologically-inspired computer vision: A case study in unconstrained face recognition on facebook</a></td><td><span class="gray">[pdf]</a></td><td>CVPR 2011 WORKSHOPS</td><td>edu</td><td>Harvard University</td><td>United States</td><td>42.36782045</td><td>-71.12666653</td><td>62%</td><td>52</td><td>32</td><td>20</td><td>3</td><td>38</td><td>13</td></tr><tr><td>51eba481dac6b229a7490f650dff7b17ce05df73</td><td>imsitu</td><td>imSitu</td><td><a href="papers/51eba481dac6b229a7490f650dff7b17ce05df73.html" target="_blank">Situation Recognition: Visual Semantic Role Labeling for Image Understanding</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>52</td><td>34</td><td>18</td><td>1</td><td>46</td><td>6</td></tr><tr><td>570f37ed63142312e6ccdf00ecc376341ec72b9f</td><td>stanford_drone</td><td>Stanford Drone</td><td><a href="papers/570f37ed63142312e6ccdf00ecc376341ec72b9f.html" target="_blank">Social LSTM: Human Trajectory Prediction in Crowded Spaces</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>56%</td><td>224</td><td>125</td><td>99</td><td>3</td><td>140</td><td>81</td></tr><tr><td>23e824d1dfc33f3780dd18076284f07bd99f1c43</td><td>mifs</td><td>MIFS</td><td><a href="papers/23e824d1dfc33f3780dd18076284f07bd99f1c43.html" target="_blank">Spoofing faces using makeup: An investigative study</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE International Conference on Identity, Security and Behavior Analysis (ISBA)</td><td>edu</td><td>INRIA Méditerranée</td><td>France</td><td>43.61581310</td><td>7.06838000</td><td>67%</td><td>6</td><td>4</td><td>2</td><td>0</td><td>1</td><td>5</td></tr><tr><td>1a40092b493c6b8840257ab7f96051d1a4dbfeb2</td><td>sports_videos_in_the_wild</td><td>SVW</td><td><a href="papers/1a40092b493c6b8840257ab7f96051d1a4dbfeb2.html" target="_blank">Sports Videos in the Wild (SVW): A video dataset for sports analysis</a></td><td><span class="gray">[pdf]</a></td><td>2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)</td><td></td><td></td><td></td><td></td><td></td><td>86%</td><td>7</td><td>6</td><td>1</td><td>1</td><td>5</td><td>2</td></tr><tr><td>9361b784e73e9238d5cefbea5ac40d35d1e3103f</td><td>oxford_town_centre</td><td>TownCentre</td><td><a href="papers/9361b784e73e9238d5cefbea5ac40d35d1e3103f.html" target="_blank">Stable multi-target tracking in real-time surveillance video</a></td><td><span class="gray">[pdf]</a></td><td>CVPR 2011</td><td>edu</td><td>University of Oxford</td><td>United Kingdom</td><td>51.75345380</td><td>-1.25400997</td><td>67%</td><td>328</td><td>221</td><td>107</td><td>13</td><td>186</td><td>140</td></tr><tr><td>2306b2a8fba28539306052764a77a0d0f5d1236a</td><td>qmul_surv_face</td><td>QMUL-SurvFace</td><td><a href="papers/2306b2a8fba28539306052764a77a0d0f5d1236a.html" target="_blank">Surveillance Face Recognition Challenge</a></td><td><a href="https://arxiv.org/pdf/1804.09691.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td>edu</td><td>Queen Mary University of London</td><td>United Kingdom</td><td>51.52472720</td><td>-0.03931035</td><td>100%</td><td>1</td><td>1</td><td>0</td><td>0</td><td>1</td><td>0</td></tr><tr><td>f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f</td><td>pku_reid</td><td>PKU-Reid</td><td><a href="papers/f6c8d5e35d7e4d60a0104f233ac1a3ab757da53f.html" target="_blank">Swiss-System Based Cascade Ranking for Gait-Based Person Re-Identification</a></td><td><a href="https://pdfs.semanticscholar.org/f6c8/d5e35d7e4d60a0104f233ac1a3ab757da53f.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>4</td><td>2</td><td>2</td><td>0</td><td>1</td><td>2</td></tr><tr><td>4d58f886f5150b2d5e48fd1b5a49e09799bf895d</td><td>texas_3dfrd</td><td>Texas 3DFRD</td><td><a href="papers/4d58f886f5150b2d5e48fd1b5a49e09799bf895d.html" target="_blank">Texas 3D Face Recognition Database</a></td><td><span class="gray">[pdf]</a></td><td>2010 IEEE Southwest Symposium on Image Analysis & Interpretation (SSIAI)</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>66</td><td>40</td><td>26</td><td>3</td><td>40</td><td>27</td></tr><tr><td>6d96f946aaabc734af7fe3fc4454cf8547fcd5ed</td><td>ar_facedb</td><td>AR Face</td><td><a href="papers/6d96f946aaabc734af7fe3fc4454cf8547fcd5ed.html" target="_blank">The AR face database</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>58%</td><td>999</td><td>579</td><td>420</td><td>58</td><td>458</td><td>530</td></tr><tr><td>2485c98aa44131d1a2f7d1355b1e372f2bb148ad</td><td>cas_peal</td><td>CAS-PEAL</td><td><a href="papers/2485c98aa44131d1a2f7d1355b1e372f2bb148ad.html" target="_blank">The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans</td><td></td><td></td><td></td><td></td><td></td><td>59%</td><td>429</td><td>254</td><td>175</td><td>38</td><td>198</td><td>234</td></tr><tr><td>47662d1a368daf70ba70ef2d59eb6209f98b675d</td><td>fia</td><td>CMU FiA</td><td><a href="papers/47662d1a368daf70ba70ef2d59eb6209f98b675d.html" target="_blank">The CMU Face In Action (FIA) Database</a></td><td><a href="https://pdfs.semanticscholar.org/4766/2d1a368daf70ba70ef2d59eb6209f98b675d.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>48%</td><td>54</td><td>26</td><td>28</td><td>5</td><td>40</td><td>16</td></tr><tr><td>4d423acc78273b75134e2afd1777ba6d3a398973</td><td>cmu_pie</td><td>CMU PIE</td><td><a href="papers/4d423acc78273b75134e2afd1777ba6d3a398973.html" target="_blank">The CMU Pose, Illumination, and Expression (PIE) Database</a></td><td><a href="http://www.comp.nus.edu.sg/~tsim/piedb.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>59%</td><td>760</td><td>449</td><td>310</td><td>49</td><td>404</td><td>345</td></tr><tr><td>4d423acc78273b75134e2afd1777ba6d3a398973</td><td>multi_pie</td><td>MULTIPIE</td><td><a href="papers/4d423acc78273b75134e2afd1777ba6d3a398973.html" target="_blank">The CMU Pose, Illumination, and Expression (PIE) Database</a></td><td><a href="http://www.comp.nus.edu.sg/~tsim/piedb.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>59%</td><td>760</td><td>449</td><td>310</td><td>49</td><td>404</td><td>345</td></tr><tr><td>4df3143922bcdf7db78eb91e6b5359d6ada004d2</td><td>cfd</td><td>CFD</td><td><a href="papers/4df3143922bcdf7db78eb91e6b5359d6ada004d2.html" target="_blank">The Chicago face database: A free stimulus set of faces and norming data.</a></td><td><a href="https://pdfs.semanticscholar.org/4df3/143922bcdf7db78eb91e6b5359d6ada004d2.pdf" target="_blank">[pdf]</a></td><td>Behavior research methods</td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>99</td><td>59</td><td>40</td><td>1</td><td>73</td><td>21</td></tr><tr><td>20388099cc415c772926e47bcbbe554e133343d1</td><td>cafe</td><td>#N/A</td><td><a href="papers/20388099cc415c772926e47bcbbe554e133343d1.html" target="_blank">The Child Affective Facial Expression (CAFE) set: validity and reliability from untrained adults</a></td><td><a href="https://pdfs.semanticscholar.org/2038/8099cc415c772926e47bcbbe554e133343d1.pdf" target="_blank">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>37</td><td>20</td><td>17</td><td>3</td><td>30</td><td>7</td></tr><tr><td>4e6ee936eb50dd032f7138702fa39b7c18ee8907</td><td>dartmouth_children</td><td>Dartmouth Children</td><td><a href="papers/4e6ee936eb50dd032f7138702fa39b7c18ee8907.html" target="_blank">The Dartmouth Database of Children’s Faces: Acquisition and Validation of a New Face Stimulus Set</a></td><td><a href="https://pdfs.semanticscholar.org/4e6e/e936eb50dd032f7138702fa39b7c18ee8907.pdf" target="_blank">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>21</td><td>11</td><td>10</td><td>2</td><td>18</td><td>3</td></tr><tr><td>9e31e77f9543ab42474ba4e9330676e18c242e72</td><td>imdb_face</td><td>IMDb Face</td><td><a href="papers/9e31e77f9543ab42474ba4e9330676e18c242e72.html" target="_blank">The Devil of Face Recognition is in the Noise</a></td><td><a href="https://arxiv.org/pdf/1807.11649.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>edu</td><td>Nanyang Technological University</td><td>Singapore</td><td>1.34841040</td><td>103.68297965</td><td>50%</td><td>6</td><td>3</td><td>3</td><td>0</td><td>4</td><td>1</td></tr><tr><td>71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6</td><td>umd_faces</td><td>UMD</td><td><a href="papers/71b7fc715e2f1bb24c0030af8d7e7b6e7cd128a6.html" target="_blank">The Do’s and Don’ts for CNN-Based Face Verification</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE International Conference on Computer Vision Workshops (ICCVW)</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>26</td><td>16</td><td>10</td><td>2</td><td>16</td><td>8</td></tr><tr><td>72a155c987816ae81c858fddbd6beab656d86220</td><td>europersons</td><td>EuroCity Persons</td><td><a href="papers/72a155c987816ae81c858fddbd6beab656d86220.html" target="_blank">The EuroCity Persons Dataset: A Novel Benchmark for Object Detection</a></td><td><a href="https://arxiv.org/pdf/1805.07193.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>0%</td><td>2</td><td>0</td><td>2</td><td>0</td><td>2</td><td>0</td></tr><tr><td>4d9a02d080636e9666c4d1cc438b9893391ec6c7</td><td>cohn_kanade_plus</td><td>CK+</td><td><a href="papers/4d9a02d080636e9666c4d1cc438b9893391ec6c7.html" target="_blank">The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression</a></td><td><span class="gray">[pdf]</a></td><td>2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops</td><td>edu</td><td>University of Pittsburgh</td><td>United States</td><td>40.44415295</td><td>-79.96243993</td><td>61%</td><td>999</td><td>607</td><td>392</td><td>57</td><td>470</td><td>518</td></tr><tr><td>0f0fcf041559703998abf310e56f8a2f90ee6f21</td><td>feret</td><td>FERET</td><td><a href="papers/0f0fcf041559703998abf310e56f8a2f90ee6f21.html" target="_blank">The FERET Evaluation Methodology for Face-Recognition Algorithms</a></td><td><a href="https://pdfs.semanticscholar.org/5099/7a5605c1f61e09e9a96789ed7495be6625aa.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>34%</td><td>29</td><td>10</td><td>19</td><td>3</td><td>18</td><td>9</td></tr><tr><td>0c4a139bb87c6743c7905b29a3cfec27a5130652</td><td>feret</td><td>FERET</td><td><a href="papers/0c4a139bb87c6743c7905b29a3cfec27a5130652.html" target="_blank">The FERET Verification Testing Protocol for Face Recognition Algorithms</a></td><td><a href="https://pdfs.semanticscholar.org/8d2a/1c768fce6f71584dd993fb97e7b6419aaf60.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td>edu</td><td>City University of New York</td><td>United States</td><td>40.87228250</td><td>-73.89489171</td><td>51%</td><td>115</td><td>59</td><td>56</td><td>8</td><td>75</td><td>37</td></tr><tr><td>dc8b25e35a3acb812beb499844734081722319b4</td><td>feret</td><td>FERET</td><td><a href="papers/dc8b25e35a3acb812beb499844734081722319b4.html" target="_blank">The FERET database and evaluation procedure for face-recognition algorithms</a></td><td><a href="http://biometrics.nist.gov/cs_links/face/frvt/feret/FERET_Database_evaluation_procedure.pdf" target="_blank">[pdf]</a></td><td>Image Vision Comput.</td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>999</td><td>521</td><td>478</td><td>103</td><td>591</td><td>421</td></tr><tr><td>8f02ec0be21461fbcedf51d864f944cfc42c875f</td><td>hda_plus</td><td>HDA+</td><td><a href="papers/8f02ec0be21461fbcedf51d864f944cfc42c875f.html" target="_blank">The HDA+ Data Set for Research on Fully Automated Re-identification Systems</a></td><td><a href="https://pdfs.semanticscholar.org/8f02/ec0be21461fbcedf51d864f944cfc42c875f.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>16</td><td>8</td><td>8</td><td>1</td><td>10</td><td>6</td></tr><tr><td>8be57cdad86fdf8c8290df4ca3149592f3c46dd3</td><td>m2vts</td><td>m2vts</td><td><a href="papers/8be57cdad86fdf8c8290df4ca3149592f3c46dd3.html" target="_blank">The M2VTS Multimodal Face Database (Release 1.00)</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>45%</td><td>73</td><td>33</td><td>40</td><td>2</td><td>39</td><td>33</td></tr><tr><td>ea050801199f98a1c7c1df6769f23f658299a3ae</td><td>mpi_large</td><td>Large MPI Facial Expression</td><td><a href="papers/ea050801199f98a1c7c1df6769f23f658299a3ae.html" target="_blank">The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</a></td><td><a href="https://pdfs.semanticscholar.org/ea05/0801199f98a1c7c1df6769f23f658299a3ae.pdf" target="_blank">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>33</td><td>17</td><td>16</td><td>4</td><td>29</td><td>4</td></tr><tr><td>ea050801199f98a1c7c1df6769f23f658299a3ae</td><td>mpi_small</td><td>Small MPI Facial Expression</td><td><a href="papers/ea050801199f98a1c7c1df6769f23f658299a3ae.html" target="_blank">The MPI Facial Expression Database — A Validated Database of Emotional and Conversational Facial Expressions</a></td><td><a href="https://pdfs.semanticscholar.org/ea05/0801199f98a1c7c1df6769f23f658299a3ae.pdf" target="_blank">[pdf]</a></td><td></td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>33</td><td>17</td><td>16</td><td>4</td><td>29</td><td>4</td></tr><tr><td>578d4ad74818086bb64f182f72e2c8bd31e3d426</td><td>mr2</td><td>MR2</td><td><a href="papers/578d4ad74818086bb64f182f72e2c8bd31e3d426.html" target="_blank">The MR2: A multi-racial, mega-resolution database of facial stimuli.</a></td><td><a href="http://www.mpmlab.org/The%20MR2%20face%20database.pdf" target="_blank">[pdf]</a></td><td>Behavior research methods</td><td></td><td></td><td></td><td></td><td></td><td>43%</td><td>7</td><td>3</td><td>4</td><td>0</td><td>7</td><td>0</td></tr><tr><td>f1af714b92372c8e606485a3982eab2f16772ad8</td><td>mug_faces</td><td>MUG Faces</td><td><a href="papers/f1af714b92372c8e606485a3982eab2f16772ad8.html" target="_blank">The MUG facial expression database</a></td><td><span class="gray">[pdf]</a></td><td>11th International Workshop on Image Analysis for Multimedia Interactive Services WIAMIS 10</td><td>edu</td><td>Aristotle University of Thessaloniki</td><td>Greece</td><td>40.62984145</td><td>22.95889350</td><td>55%</td><td>82</td><td>45</td><td>37</td><td>4</td><td>34</td><td>47</td></tr><tr><td>79828e6e9f137a583082b8b5a9dfce0c301989b8</td><td>mapillary</td><td>Mapillary</td><td><a href="papers/79828e6e9f137a583082b8b5a9dfce0c301989b8.html" target="_blank">The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>61%</td><td>61</td><td>37</td><td>24</td><td>0</td><td>43</td><td>16</td></tr><tr><td>96e0cfcd81cdeb8282e29ef9ec9962b125f379b0</td><td>megaface</td><td>MegaFace</td><td><a href="papers/96e0cfcd81cdeb8282e29ef9ec9962b125f379b0.html" target="_blank">The MegaFace Benchmark: 1 Million Faces for Recognition at Scale</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td></td><td></td><td></td><td></td><td></td><td>76%</td><td>139</td><td>106</td><td>33</td><td>5</td><td>100</td><td>37</td></tr><tr><td>0ee1916a0cb2dc7d3add086b5f1092c3d4beb38a</td><td>voc</td><td>VOC</td><td><a href="papers/0ee1916a0cb2dc7d3add086b5f1092c3d4beb38a.html" target="_blank">The Pascal Visual Object Classes (VOC) Challenge</a></td><td><a href="http://eprints.pascal-network.org/archive/00006187/01/PascalVOC_IJCV2009.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td>company</td><td>Microsoft</td><td>United States</td><td>47.64233180</td><td>-122.13693020</td><td>61%</td><td>999</td><td>607</td><td>391</td><td>28</td><td>557</td><td>422</td></tr><tr><td>66e6f08873325d37e0ec20a4769ce881e04e964e</td><td>sun_attributes</td><td>SUN</td><td><a href="papers/66e6f08873325d37e0ec20a4769ce881e04e964e.html" target="_blank">The SUN Attribute Database: Beyond Categories for Deeper Scene Understanding</a></td><td><a href="http://www.cc.gatech.edu/~hays/papers/attribute_ijcv.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>116</td><td>70</td><td>46</td><td>14</td><td>84</td><td>31</td></tr><tr><td>8b2dd5c61b23ead5ae5508bb8ce808b5ea266730</td><td>10k_US_adult_faces</td><td>10K US Adult Faces</td><td><a href="papers/8b2dd5c61b23ead5ae5508bb8ce808b5ea266730.html" target="_blank">The intrinsic memorability of face photographs.</a></td><td><a href="https://pdfs.semanticscholar.org/8b2d/d5c61b23ead5ae5508bb8ce808b5ea266730.pdf" target="_blank">[pdf]</a></td><td>Journal of experimental psychology. General</td><td></td><td></td><td></td><td></td><td></td><td>56%</td><td>52</td><td>29</td><td>23</td><td>2</td><td>36</td><td>14</td></tr><tr><td>d178cde92ab3dc0dd2ebee5a76a33d556c39448b</td><td>jiku_mobile</td><td>Jiku Mobile Video Dataset</td><td><a href="papers/d178cde92ab3dc0dd2ebee5a76a33d556c39448b.html" target="_blank">The jiku mobile video dataset</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td>edu</td><td>National University of Singapore</td><td>Singapore</td><td>1.29620180</td><td>103.77689944</td><td>71%</td><td>24</td><td>17</td><td>7</td><td>0</td><td>6</td><td>19</td></tr><tr><td>ae0aee03d946efffdc7af2362a42d3750e7dd48a</td><td>put_face</td><td>Put Face</td><td><a href="papers/ae0aee03d946efffdc7af2362a42d3750e7dd48a.html" target="_blank">The put face database</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>55%</td><td>99</td><td>54</td><td>45</td><td>5</td><td>55</td><td>48</td></tr><tr><td>19d1b811df60f86cbd5e04a094b07f32fff7a32a</td><td>york_3d</td><td>UOY 3D Face Database</td><td><a href="papers/19d1b811df60f86cbd5e04a094b07f32fff7a32a.html" target="_blank">Three-dimensional face recognition: an eigensurface approach</a></td><td><span class="gray">[pdf]</a></td><td>2004 International Conference on Image Processing, 2004. ICIP '04.</td><td></td><td></td><td></td><td></td><td></td><td>42%</td><td>38</td><td>16</td><td>22</td><td>4</td><td>24</td><td>13</td></tr><tr><td>2edb87494278ad11641b6cf7a3f8996de12b8e14</td><td>qmul_grid</td><td>GRID</td><td><a href="papers/2edb87494278ad11641b6cf7a3f8996de12b8e14.html" target="_blank">Time-Delayed Correlation Analysis for Multi-Camera Activity Understanding</a></td><td><a href="http://www.eecs.qmul.ac.uk/~ccloy/files/ijcv_2010.pdf" target="_blank">[pdf]</a></td><td>International Journal of Computer Vision</td><td>edu</td><td>Queen Mary University of London</td><td>United Kingdom</td><td>51.52472720</td><td>-0.03931035</td><td>63%</td><td>84</td><td>53</td><td>31</td><td>4</td><td>51</td><td>33</td></tr><tr><td>64e0690dd176a93de9d4328f6e31fc4afe1e7536</td><td>duke_mtmc</td><td>Duke MTMC</td><td><a href="papers/64e0690dd176a93de9d4328f6e31fc4afe1e7536.html" target="_blank">Tracking Multiple People Online and in Real Time</a></td><td><a href="https://pdfs.semanticscholar.org/64e0/690dd176a93de9d4328f6e31fc4afe1e7536.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>78%</td><td>23</td><td>18</td><td>5</td><td>1</td><td>12</td><td>10</td></tr><tr><td>298cbc3dfbbb3a20af4eed97906650a4ea1c29e0</td><td>ferplus</td><td>FER+</td><td><a href="papers/298cbc3dfbbb3a20af4eed97906650a4ea1c29e0.html" target="_blank">Training deep networks for facial expression recognition with crowd-sourced label distribution</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>74%</td><td>34</td><td>25</td><td>9</td><td>0</td><td>18</td><td>16</td></tr><tr><td>4eab317b5ac436a949849ed286baa3de2a541eef</td><td>laofiw</td><td>LAOFIW</td><td><a href="papers/4eab317b5ac436a949849ed286baa3de2a541eef.html" target="_blank">Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings</a></td><td><a href="https://arxiv.org/pdf/1809.02169.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>2</td><td>2</td><td>0</td><td>0</td><td>2</td><td>0</td></tr><tr><td>b5f2846a506fc417e7da43f6a7679146d99c5e96</td><td>ucf_101</td><td>UCF101</td><td><a href="papers/b5f2846a506fc417e7da43f6a7679146d99c5e96.html" target="_blank">UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild</a></td><td><a href="https://arxiv.org/pdf/1212.0402.pdf" target="_blank">[pdf]</a></td><td>CoRR</td><td></td><td></td><td></td><td></td><td></td><td>64%</td><td>999</td><td>643</td><td>356</td><td>56</td><td>628</td><td>362</td></tr><tr><td>16e8b0a1e8451d5f697b94c0c2b32a00abee1d52</td><td>umb</td><td>UMB</td><td><a href="papers/16e8b0a1e8451d5f697b94c0c2b32a00abee1d52.html" target="_blank">UMB-DB: A database of partially occluded 3D faces</a></td><td><span class="gray">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td></td><td></td><td></td><td></td><td></td><td>66%</td><td>47</td><td>31</td><td>16</td><td>2</td><td>22</td><td>24</td></tr><tr><td>31b05f65405534a696a847dd19c621b7b8588263</td><td>umd_faces</td><td>UMD</td><td><a href="papers/31b05f65405534a696a847dd19c621b7b8588263.html" target="_blank">UMDFaces: An annotated face dataset for training deep networks</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE International Joint Conference on Biometrics (IJCB)</td><td>edu</td><td>University of Maryland</td><td>United States</td><td>39.28996850</td><td>-76.62196103</td><td>79%</td><td>42</td><td>33</td><td>9</td><td>2</td><td>30</td><td>11</td></tr><tr><td>8627f019882b024aef92e4eb9355c499c733e5b7</td><td>used</td><td>USED Social Event Dataset</td><td><a href="papers/8627f019882b024aef92e4eb9355c499c733e5b7.html" target="_blank">USED: a large-scale social event detection dataset</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td>edu</td><td>University of Trento</td><td>Italy</td><td>46.06588360</td><td>11.11598940</td><td>86%</td><td>7</td><td>6</td><td>1</td><td>0</td><td>3</td><td>4</td></tr><tr><td>d4f1eb008eb80595bcfdac368e23ae9754e1e745</td><td>uccs</td><td>UCCS</td><td><a href="papers/d4f1eb008eb80595bcfdac368e23ae9754e1e745.html" target="_blank">Unconstrained Face Detection and Open-Set Face Recognition Challenge</a></td><td><span class="gray">[pdf]</a></td><td>2017 IEEE International Joint Conference on Biometrics (IJCB)</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>5</td><td>5</td><td>0</td><td>0</td><td>4</td><td>1</td></tr><tr><td>4b4106614c1d553365bad75d7866bff0de6056ed</td><td>ufi</td><td>UFI</td><td><a href="papers/4b4106614c1d553365bad75d7866bff0de6056ed.html" target="_blank">Unconstrained Facial Images: Database for Face Recognition Under Real-World Conditions</a></td><td><a href="http://home.zcu.cz/~pkral/papers/kral_micai15.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>50%</td><td>12</td><td>6</td><td>6</td><td>0</td><td>4</td><td>6</td></tr><tr><td>08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7</td><td>kin_face</td><td>UB KinFace</td><td><a href="papers/08f6745bc6c1b0fb68953ea61054bdcdde6d2fc7.html" target="_blank">Understanding Kin Relationships in a Photo</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Multimedia</td><td></td><td></td><td></td><td></td><td></td><td>63%</td><td>94</td><td>59</td><td>35</td><td>1</td><td>33</td><td>61</td></tr><tr><td>5a4df9bef1872865f0b619ac3aacc97f49e4a035</td><td>cuhk_train_station</td><td>CUHK Train Station Dataset</td><td><a href="papers/5a4df9bef1872865f0b619ac3aacc97f49e4a035.html" target="_blank">Understanding collective crowd behaviors: Learning a Mixture model of Dynamic pedestrian-Agents</a></td><td><span class="gray">[pdf]</a></td><td>2012 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>Chinese University of Hong Kong</td><td>China</td><td>22.41626320</td><td>114.21093180</td><td>58%</td><td>141</td><td>82</td><td>59</td><td>5</td><td>60</td><td>75</td></tr><tr><td>21d9d0deed16f0ad62a4865e9acf0686f4f15492</td><td>images_of_groups</td><td>Images of Groups</td><td><a href="papers/21d9d0deed16f0ad62a4865e9acf0686f4f15492.html" target="_blank">Understanding images of groups of people</a></td><td><span class="gray">[pdf]</a></td><td>2009 IEEE Conference on Computer Vision and Pattern Recognition</td><td>edu</td><td>Carnegie Mellon University</td><td>United States</td><td>40.44416190</td><td>-79.94272826</td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>15e1af79939dbf90790b03d8aa02477783fb1d0f</td><td>duke_mtmc</td><td>Duke MTMC</td><td><a href="papers/15e1af79939dbf90790b03d8aa02477783fb1d0f.html" target="_blank">Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro</a></td><td><a href="https://arxiv.org/pdf/1701.07717.pdf" target="_blank">[pdf]</a></td><td>2017 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>fd8168f1c50de85bac58a8d328df0a50248b16ae</td><td>nd_2006</td><td>ND-2006</td><td><a href="papers/fd8168f1c50de85bac58a8d328df0a50248b16ae.html" target="_blank">Using a Multi-Instance Enrollment Representation to Improve 3D Face Recognition</a></td><td><span class="gray">[pdf]</a></td><td>2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems</td><td>edu</td><td>University of Notre Dame</td><td>United States</td><td>41.70456775</td><td>-86.23822026</td><td>63%</td><td>35</td><td>22</td><td>13</td><td>3</td><td>18</td><td>15</td></tr><tr><td>4563b46d42079242f06567b3f2e2f7a80cb3befe</td><td>vadana</td><td>VADANA</td><td><a href="papers/4563b46d42079242f06567b3f2e2f7a80cb3befe.html" target="_blank">VADANA: A dense dataset for facial image analysis</a></td><td><span class="gray">[pdf]</a></td><td>2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)</td><td>edu</td><td>University of Delaware</td><td>United States</td><td>39.68103280</td><td>-75.75401840</td><td>67%</td><td>15</td><td>10</td><td>5</td><td>0</td><td>5</td><td>10</td></tr><tr><td>70c59dc3470ae867016f6ab0e008ac8ba03774a1</td><td>vgg_faces2</td><td>VGG Face2</td><td><a href="papers/70c59dc3470ae867016f6ab0e008ac8ba03774a1.html" target="_blank">VGGFace2: A Dataset for Recognising Faces across Pose and Age</a></td><td><a href="https://arxiv.org/pdf/1710.08092.pdf" target="_blank">[pdf]</a></td><td>2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)</td><td></td><td></td><td></td><td></td><td></td><td>80%</td><td>83</td><td>66</td><td>17</td><td>3</td><td>61</td><td>20</td></tr><tr><td>01959ef569f74c286956024866c1d107099199f7</td><td>vqa</td><td>VQA</td><td><a href="papers/01959ef569f74c286956024866c1d107099199f7.html" target="_blank">VQA: Visual Question Answering</a></td><td><span class="gray">[pdf]</a></td><td>2015 IEEE International Conference on Computer Vision (ICCV)</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>b6c293f0420f7e945b5916ae44269fb53e139275</td><td>erce</td><td>ERCe</td><td><a href="papers/b6c293f0420f7e945b5916ae44269fb53e139275.html" target="_blank">Video Synopsis by Heterogeneous Multi-source Correlation</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>29</td><td>15</td><td>14</td><td>2</td><td>14</td><td>13</td></tr><tr><td>b6c293f0420f7e945b5916ae44269fb53e139275</td><td>tisi</td><td>Times Square Intersection</td><td><a href="papers/b6c293f0420f7e945b5916ae44269fb53e139275.html" target="_blank">Video Synopsis by Heterogeneous Multi-source Correlation</a></td><td><span class="gray">[pdf]</a></td><td>2013 IEEE International Conference on Computer Vision</td><td></td><td></td><td></td><td></td><td></td><td>52%</td><td>29</td><td>15</td><td>14</td><td>2</td><td>14</td><td>13</td></tr><tr><td>5194cbd51f9769ab25260446b4fa17204752e799</td><td>violent_flows</td><td>Violent Flows</td><td><a href="papers/5194cbd51f9769ab25260446b4fa17204752e799.html" target="_blank">Violent flows: Real-time detection of violent crowd behavior</a></td><td><span class="gray">[pdf]</a></td><td>2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops</td><td>edu</td><td>Open University of Israel</td><td>Israel</td><td>32.77824165</td><td>34.99565673</td><td>65%</td><td>88</td><td>57</td><td>31</td><td>6</td><td>45</td><td>44</td></tr><tr><td>026e3363b7f76b51cc711886597a44d5f1fd1de2</td><td>kitti</td><td>KITTI</td><td><a href="papers/026e3363b7f76b51cc711886597a44d5f1fd1de2.html" target="_blank">Vision meets robotics: The KITTI dataset</a></td><td><a href="https://pdfs.semanticscholar.org/026e/3363b7f76b51cc711886597a44d5f1fd1de2.pdf" target="_blank">[pdf]</a></td><td>I. J. Robotics Res.</td><td></td><td></td><td></td><td></td><td></td><td>60%</td><td>999</td><td>602</td><td>397</td><td>36</td><td>553</td><td>462</td></tr><tr><td>066000d44d6691d27202896691f08b27117918b9</td><td>psu</td><td>PSU</td><td><a href="papers/066000d44d6691d27202896691f08b27117918b9.html" target="_blank">Vision-Based Analysis of Small Groups in Pedestrian Crowds</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>168</td><td>90</td><td>78</td><td>10</td><td>85</td><td>79</td></tr><tr><td>dd65f71dac86e36eecbd3ed225d016c3336b4a13</td><td>families_in_the_wild</td><td>FIW</td><td><a href="papers/dd65f71dac86e36eecbd3ed225d016c3336b4a13.html" target="_blank">Visual Kinship Recognition of Families in the Wild</a></td><td><span class="gray">[pdf]</a></td><td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td><td>edu</td><td>University of Massachusetts Dartmouth</td><td>United States</td><td>41.62772475</td><td>-71.00724501</td><td>80%</td><td>5</td><td>4</td><td>1</td><td>0</td><td>2</td><td>3</td></tr><tr><td>8875ae233bc074f5cd6c4ebba447b536a7e847a5</td><td>voxceleb2</td><td>VoxCeleb2</td><td><a href="papers/8875ae233bc074f5cd6c4ebba447b536a7e847a5.html" target="_blank">VoxCeleb2: Deep Speaker Recognition.</a></td><td><a href="https://pdfs.semanticscholar.org/8875/ae233bc074f5cd6c4ebba447b536a7e847a5.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>41%</td><td>34</td><td>14</td><td>19</td><td>2</td><td>17</td><td>2</td></tr><tr><td>52d7eb0fbc3522434c13cc247549f74bb9609c5d</td><td>wider_face</td><td>WIDER FACE</td><td><a href="papers/52d7eb0fbc3522434c13cc247549f74bb9609c5d.html" target="_blank">WIDER FACE: A Face Detection Benchmark</a></td><td><span class="gray">[pdf]</a></td><td>2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</td><td>edu</td><td>Chinese University of Hong Kong</td><td>China</td><td>22.41626320</td><td>114.21093180</td><td>66%</td><td>178</td><td>118</td><td>60</td><td>11</td><td>112</td><td>66</td></tr><tr><td>36bccfb2ad847096bc76777e544f305813cd8f5b</td><td>wildtrack</td><td>WildTrack</td><td><a href="papers/36bccfb2ad847096bc76777e544f305813cd8f5b.html" target="_blank">WILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection</a></td><td><a href="http://openaccess.thecvf.com/content_cvpr_2018/Supplemental/1562-supp.pdf" target="_blank">[pdf]</a></td><td>2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>5ad4e9f947c1653c247d418f05dad758a3f9277b</td><td>wlfdb</td><td>WLFDB</td><td><a href="papers/5ad4e9f947c1653c247d418f05dad758a3f9277b.html" target="_blank">WLFDB : Weakly Labeled Face Databases</a></td><td><a href="https://pdfs.semanticscholar.org/5ad4/e9f947c1653c247d418f05dad758a3f9277b.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>100%</td><td>1</td><td>1</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><td>0dc11a37cadda92886c56a6fb5191ded62099c28</td><td>stickmen_family</td><td>We Are Family Stickmen</td><td><a href="papers/0dc11a37cadda92886c56a6fb5191ded62099c28.html" target="_blank">We Are Family: Joint Pose Estimation of Multiple Persons</a></td><td><a href="http://eprints.pascal-network.org/archive/00007964/01/eichner10eccv.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>65%</td><td>78</td><td>51</td><td>27</td><td>5</td><td>54</td><td>23</td></tr><tr><td>0c91808994a250d7be332400a534a9291ca3b60e</td><td>graz</td><td>Graz Pedestrian</td><td><a href="papers/0c91808994a250d7be332400a534a9291ca3b60e.html" target="_blank">Weak Hypotheses and Boosting for Generic Object Detection and Recognition</a></td><td><a href="https://pdfs.semanticscholar.org/0c91/808994a250d7be332400a534a9291ca3b60e.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>56%</td><td>236</td><td>131</td><td>105</td><td>17</td><td>161</td><td>77</td></tr><tr><td>2a75f34663a60ab1b04a0049ed1d14335129e908</td><td>mmi_facial_expression</td><td>MMI Facial Expression Dataset</td><td><a href="papers/2a75f34663a60ab1b04a0049ed1d14335129e908.html" target="_blank">Web-based database for facial expression analysis</a></td><td><span class="gray">[pdf]</a></td><td>2005 IEEE International Conference on Multimedia and Expo</td><td></td><td></td><td></td><td></td><td></td><td>54%</td><td>464</td><td>250</td><td>214</td><td>45</td><td>282</td><td>188</td></tr><tr><td>9b9bf5e623cb8af7407d2d2d857bc3f1b531c182</td><td>who_goes_there</td><td>WGT</td><td><a href="papers/9b9bf5e623cb8af7407d2d2d857bc3f1b531c182.html" target="_blank">Who goes there?: approaches to mapping facial appearance diversity</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td>edu</td><td>University of Kentucky</td><td>United States</td><td>38.03337420</td><td>-84.50177580</td><td>100%</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>b62628ac06bbac998a3ab825324a41a11bc3a988</td><td>m2vtsdb_extended</td><td>xm2vtsdb</td><td><a href="papers/b62628ac06bbac998a3ab825324a41a11bc3a988.html" target="_blank">XM2VTSDB : The extended M2VTS database</a></td><td><a href="https://pdfs.semanticscholar.org/b626/28ac06bbac998a3ab825324a41a11bc3a988.pdf" target="_blank">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>62%</td><td>864</td><td>539</td><td>325</td><td>37</td><td>493</td><td>404</td></tr><tr><td>010f0f4929e6a6644fb01f0e43820f91d0fad292</td><td>yfcc_100m</td><td>YFCC100M</td><td><a href="papers/010f0f4929e6a6644fb01f0e43820f91d0fad292.html" target="_blank">YFCC100M: the new data in multimedia research</a></td><td><span class="gray">[pdf]</a></td><td>Commun. ACM</td><td>edu</td><td>Carnegie Mellon University</td><td>United States</td><td>40.44416190</td><td>-79.94272826</td><td>64%</td><td>274</td><td>176</td><td>98</td><td>23</td><td>172</td><td>100</td></tr><tr><td>a94cae786d515d3450d48267e12ca954aab791c4</td><td>yawdd</td><td>YawDD</td><td><a href="papers/a94cae786d515d3450d48267e12ca954aab791c4.html" target="_blank">YawDD: a yawning detection dataset</a></td><td><span class="gray">[pdf]</a></td><td>Unknown</td><td></td><td></td><td></td><td></td><td></td><td>80%</td><td>15</td><td>12</td><td>3</td><td>1</td><td>2</td><td>13</td></tr></table></body></html>
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