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  • Title: Face Recognition Homepage
    Descriptive info: .. FACE RECOGNITION HOMEPAGE.. Face Recognition Homepage / Relevant information in the the area of face recognition / Information pool.. for the face recognition community / Entry point for novices as well as a centralized information resource.. General Info.. New @ face-rec.. org.. Research Groups.. Newsgroup.. Interesting Papers.. New Papers.. Databases.. Algorithms.. Source Codes.. Conferences.. Journals Books.. Vendors.. Related Links.. Contact Info.. 2005-2013 VCL.. Visited 1246232 times since 01 March 2005.. Last update: 10 November 2013..

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  • Title: Face Recognition Homepage - General Info
    Descriptive info: GENERAL INFO.. Over the last ten years or so, face recognition has become a popular area of research in computer vision and one of the most successful applications of image analysis and understanding.. Because of the nature of the problem, not only computer science researchers are interested in it, but neuroscientists and psychologists also.. It is the general opinion that advances in computer vision research will provide useful insights to neuroscientists and psychologists into how human brain works, and vice versa.. A general statement of the face recognition problem (in computer vision) can be formulated as follows: Given still or video images of a scene, identify or verify one or more persons in the scene using a stored database of faces.. Research directions (according to Face Recognition Vendor Test - FRVT 2002):.. Recognition from outdoor facial images.. Recognition from non-frontal facial images.. Recognition at low false accept/alarm rates.. Understanding why males are easier to recognize than females.. Greater understanding of the effects of demographic factors on performance.. Development of better statistical methods for understanding performance.. Develop improved models for predicting identification performance on very large galleries.. Effect of algorithm and system training on covariate performance.. Integration of morphable models into face recognition performance.. Understanding the video sequences in FRVT 2002 did not improve performance.. Since the former Face Recognition Homepage of Peter Kruizinga is no longer active and we believe that face recognition is an important research area, we wanted to give prospective researchers a place to start on the Internet.. Our Face Recognition Homepage aims to provide scientists  ...   Homepage;.. links to research groups that work in the area of face recognition;.. link to Face Recognition Research Community newsgroup (established in January 2007), where researchers can post questions and exchange ideas;.. interesting papers that deal with the face recognition (general papers, standards, cognitive vision / psychology / neuroscience papers, highly cited papers, published items vs.. citations);.. new papers with most recent advances (the state-of-the-art) in face recognition;.. face databases often used by researchers;.. face recognition algorithms (image-based and video based) with a few most representative papers for each algorithm;.. source codes contributed by users (Matlab, C/C++, Java);.. list of conferences, where face recognition is the conference topic;.. links to high impact factor journals which scope covers face recognition; information about some journal special issues; books on face recognition; other related books;.. links to vendors developing face recognition technology;.. other related links;.. contact information.. Please feel free to inform all your colleagues who might be interested in this web-site.. If you like the web-site yourself, please feel free to put a link to it on your web-page as well.. Hopefully, this effort will increase the number of researchers in this area and improve the state-of-the-art in face recognition.. We believe that interdisciplinary approach will further improve the research, so by putting some psychology and cognitive science papers on the subject we would like to encourage that as well.. Besides, we would be most grateful if you could send us any comments, suggestions and corrections that could improve Face Recognition Homepage.. Mislav Grgic.. Kresimir Delac.. Last update: 18 January 2007..

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  • Title: Face Recognition Homepage - New @ face-rec.org
    Descriptive info: NEW @ FACE-REC.. ORG.. 26 August 2013.. New Database:.. 3D Mask Attack Database (3DMAD).. added to.. page.. 11 June 2013.. Three New Databases.. page:.. YMU (YouTube Makeup) Dataset.. VMU (Virtual Makeup) Dataset.. MIW (Makeup in the wild ) Dataset.. 12 December 2012.. Four New Databases.. PhotoFace: Face recognition using photometric stereo.. The EURECOM Kinect Face Dataset (EURECOM KFD).. Labeled Faces in the Wild-a (LFW-a).. YouTube Faces Database.. 18 November 2012.. Long Distance Heterogeneous Face Database (LDHF-DB).. 4 November 2012.. Two new books added to.. Journal and Books.. 20 May 2012.. MORPH Database (Craniofacial Longitudinal Morphological Face Database).. 09 April 2012.. Several links to Philipp Wagner's implementation methods and guides added to.. Source Codes.. 26 February 2012.. The PhD (Pretty helpful Development) functions for face recognition toolbox (author: Vitomir Struc) added to.. 15 January 2012.. Link to Philipp Wagner's implementation of Eigenfaces and Fisherfaces methods with the OpenCV2 C++ API added to.. 30 November 2011.. Two New Databases:.. UMB database of 3D occluded faces.. and.. VADANA: Vims Appearance Dataset for facial ANAlysis.. 20 October 2011.. Update (version 2.. 0) of the INFace toolbox - a collection of Matlab functions for illumination invariant face recognition available on.. 12 July 2011.. ChokePoint.. 6 June 2011.. FEI Face Database.. 17 April 2011.. Natural Visible and Infrared facial Expression database (USTC-NVIE).. 16 January 2011.. Face.. com.. has been added to.. developers.. face.. is available with a free REST API for software developers interested in face recognition.. 28 December 2010.. Texas 3D Face Recognition Database (Texas 3DFRD).. The Hong Kong Polytechnic University NIR Face Database.. The Hong Kong Polytechnic University Hyperspectral Face Database (PolyU-HSFD).. MOBIO - Mobile Biometry Face and Speech Database.. 5 December 2010.. Call for Book Proposals:.. DIGITAL IMAGING AND COMPUTER VISION.. Book Series Editor: Dr.. Rastislav Lukac Foveon, Inc.. / Sigma Corp.. San Jose, California, USA.. CRC Press / Taylor Francis Book Series.. 21 November 2010.. Call for Papers:.. Pattern Recognition Letters - Special Issue on.. Novel Pattern Recognition-Based Methods for Reidentification in Biometric Context.. 26 September 2010.. New papers.. page updated (titles, authors and links to.. 130 new papers.. ).. 06 September 2010.. The Iranian Face Database (IFDB).. 29 June 2010.. Plastic Surgery Face Database.. 16 June 2010.. Link to Shervin Emami's tutorial and freeware C++ source-code for face detection and face recognition (using eigenfaces) added to.. 29 January 2010.. The INFace toolbox - a collection of Matlab functions for illumination invariant face recognition added to.. 22 January 2010.. SCface - Surveillance Cameras Face Database.. 17 September 2009.. The Basel Face Model (BFM) is added to.. 4 December 2009.. page updated (title, authors and link to.. 140 new papers.. 24 September 2009.. PUT Face Database is added to.. 07 August 2009.. The LFWcrop database is added to.. 06 August 2009.. page updated.. 29 April 2009.. Highly Cited Papers.. subsection updated on.. 01 April 2009.. Face Recognition featured as the.. Special Topic of ScienceWatch.. in April 2009.. It is  ...   2007.. Face Recognition.. (published July 2007) added to.. 18 January 2007.. page established.. Two new subsections available on.. Published Items vs.. Citations.. Top header on all subpages redesigned.. 07 January 2007.. Here you can find information about new.. Face Recognition Research Community newsgroup.. 94 papers added!.. Research Groups.. page slightly redesigned.. 26 December 2006.. Call for Papers.. : IJPRAI Special Issue on.. Facial Image Processing and Analysis.. 04 December 2006.. Face Video Database of the Max Planck Institute for Biological Cybernetics is added to.. 17 August 2006.. New journal special issue added to.. page updated (special thanks to Justas Kranauskas).. 10 June 2006.. New book added to.. 21 March 2006.. Changes on.. page - Boosting Ensemble Solutions added.. Important Editorial published in the Pattern Recognition Letters.. More information on.. 03 March 2006.. Submission deadlines added.. 30 January 2006.. Standards sub-section added to.. Georgia Tech Face Database is added to.. 21 January 2006.. pages updated.. UCD VALID and UCD Color Image Database are added to.. 08 January 2006.. EQUINOX HID Face Database is added to.. 14 November 2005.. 29 August 2005.. 16 August 2005.. 28 July 2005.. CAS-PEAL Face Database is added to.. 18 July 2005.. page - Trace Transform added and Algorithms Comparisons updated.. 23 June 2005.. page updated and redesigned.. 17 June 2005.. A good initiative:.. The official ad:.. -----------------------------.. The Center of Biological and Computational Learning at the Massachusetts Institute of Technology is conducting research on systems for automatic face recognition.. We ask you to support our effort in building a face database by submitting your face image to our website.. The submission deadline is end of June.. Every participant will enter our prize drawing at the end of the study.. The winner has the choice between the new NOKIA 770 Internet Tablet, the Canon PowerShot SD400, and the iPod Photo 30GB.. If you are interested please go to.. http://capraia.. mit.. edu/.. Thanks for your help!.. Vendros.. 25 May 2005.. 11 May 2005.. 18 April 2005.. IEEE International Workshop on Analysis and Modeling of Faces and Gestures included on.. Workshop will be held on 16 October 2005, Beijing, China, in conjunction with ICCV-2005.. 25 March 2005.. 17 March 2005.. MIT-CBCL Face Recognition Database is added to.. 15 March 2005.. 11 March 2005.. Three new pages are added to Face Recognition Homepage:.. 1.. - most recent advances in face recognition published in the last six months or so in high impact factor journals;.. 2.. - vendors developing face recognition technology;.. 3.. - what has been changed on Face Recognition Homepage.. Cohn-Kanade database is added to.. 10 March 2005.. page, section Related Books.. 07 March 2005.. Several papers added to.. pages.. 02 March 2005.. IEEE Workshop on Face Recognition Grand Challenge Experiments included on.. Workshop will be held on 21 June 2005, San Diego, CA, USA, in conjunction with CVPR 2005.. 01 March 2005.. Face Recognition Homepage launched.. Web-site:.. http://www.. face-rec.. org/.. Last update: 26 August 2013..

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  • Title: Face Recognition Homepage - Research Groups
    Descriptive info: RESEARCH GROUPS.. Australia.. |.. Austria.. Belgium.. Brazil.. Canada.. Chile.. China.. Croatia.. Finland.. France.. Germany.. Israel.. Italy.. Korea.. Poland.. Slovenia.. Singapore.. Spain.. Switzerland.. The Netherlands.. Turkey.. United Kingdom.. Uruguay.. USA.. AUSTRALIA.. Biometric Recognition at The University of Western Australia.. Intelligent Real-Time Imaging and Sensing Group (IRIS), School of Information Technology and Electrical Engineering, The University of Queensland.. back to top.. AUSTRIA.. Institute for Computer Graphics and Vision, Graz University of Technology.. Pattern Recognition and Image Processing Group, Vienna University of Technology.. BELGIUM.. Face Categorization Lab.. BRAZIL.. Creative Vision Research Group, Department of Computer Science, Institute of Mathematics and Statistics - IME, University of Sao Paulo.. IMAGO Research Group.. CANADA.. Chaudhuri Vision Lab, Department of Psychology, McGill University, Montreal.. Cognition and Perception Lab, University of Ottawa, Ottawa, Ontario.. CHILE.. Computational Vision Group, Department of Electrical Engineering, Universidad de Chile.. CHINA.. Center for Biometrics and Security Research, Institute of Automation, Chinese Academy of Sciences.. ICT-ISVISION Joint Research Development Laboratory for Face Recognition.. LAMDA Group, Nanjing University.. CROATIA.. Video Communications Laboratory, University of Zagreb.. FINLAND.. Machine Vision Group, Dept.. of Electrical and Inf.. Eng.. , University of Oulu.. FRANCE.. Face and Gesture Recognition Working group.. GERMANY.. Computer Vision und Pattern Recognition Group, University of Muenster.. Facial Image Processing and Analysis Group in Karlsruhe Institute of Technology (KIT).. Humanscan AG (Dr.. Frischholz).. Institute for Vision and Graphics, University of Siegen.. Research Group Neural Computation , Institute for Theoretical Biology,.. Humboldt-Universitat zu Berlin.. Systems Biophysics Group, Institut fur Neuroinformatik, Ruhr-Universitat Bochum.. ISRAEL.. Cognitive Elecrophysiology Laboratory, Department of Psychology, Hebrew University of Jerusalem.. Geometric Image Processing (GIP) Laboratory, Computer Science Department.. Technion - Israel Institute of Technology.. ITALY.. Biometric System Lab, DEIS, University of Bologna.. KOREA.. Image and Video System Laboratory, Korea Advanced Institute of Science and Technology (KAIST).. Intelligent  ...   Intelligence Laboratory, Dept.. of Computer Engineering, Bogazici University.. UNITED KINGDOM.. Cognition and Brain Sciences Unit, Medical Research Council, Cambridge.. Computer Vision and Medical Imaging, Department of Informatics, University of Sussex.. Division of Imaging Science Biomedical Engineering, University of Manchester.. Face Research Lab, School of Psychology, King's College, University of Aberdeen, Scotland.. Glasgow Face Recognition Group, Department of Psychology, University of Glasgow.. Machine Vision Laboratory, University of West England, Bristol.. Perception Group, School of Psychology, Cardiff University.. Perception Laboratory, School of Psychology, University of St Andrews, Scotland.. Queen Mary Vision Laboratory, Queen Mary University of London.. URUGUAY.. Image Processing Group, Institute of Electrical Engineering, Engineering School, University of the Republic, Montevideo.. Advanced Multimedia Processing Lab, Carnegie Mellon University.. Biometrics Research, Michigan State University.. Center for Automation Research, University of Maryland.. Center for Distributed and Intelligent Computation, George Mason University.. Computational Biomedicine Lab, Dept.. of Computer Science, University of Houston.. Computer Vision and Robotics Laboratory, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign.. Computer Vision Laboratory, Computer Science Department, University of Massachusetts at Amherst.. Face Group, Robotics Institute, Carnegie Mellon University.. Face Perception and Research Laboratories, University of Texas at Dallas.. Face Recognition Group, Department of Electrical and Computer Engineering, University of Wisconsin-Madison.. Four Eyes Lab, University of California, Santa Barbara.. Laboratory for Computational Neuroscience, School of Biomedical Engineering, Science and Health Systems, Drexel University.. Laboratory of Computational Neuroscience, The Rockefeller University.. Laboratory for Computational Vision, Florida State University.. Mitsubishi Electric Research Laboratories, Cambridge, MA.. Machine Learning Systems, Jet Propulsion Laboratory, California Institute of Technology.. Machine Perception Laboratory, University of California, San Diego.. PEN - Perceptual Expertise Network.. Perception and Decision Laboratory, University of Illinois at Urbana-Champaign.. Perceptual Science Laboratory, University of California - Santa Cruz.. Last update: 2 September 2013..

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  • Title: Face Recognition Homepage - Newsgroup
    Descriptive info: NEWSGROUP.. Face Recognition Research Community.. To help and speed up communication between researchers in face recognition area, we established the.. newsgroup, where researchers can post questions and exchange ideas:.. Last update: 07 January 2007..

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  • Title: Face Recognition Homepage - Interesting Papers
    Descriptive info: INTERESTING PAPERS.. General Papers.. Standards.. Cog.. Here are some excellent papers that every researcher in this area should read.. They present a logical introductory material into the field and describe latest achievements as well as currently unsolved issues of face recognition.. W.. Zhao, R.. Chellappa, A.. Rosenfeld, P.. J.. Phillips, Face Recognition: A Literature Survey, ACM Computing Surveys, 2003, pp.. 399-458.. download here.. , 3.. 88 MB.. R.. Brunelli, T.. Poggio, Face Recognition: Features versus Templates, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.. 15, No.. 10, October 1993, pp.. 1042-1052.. link.. M.. Kirby, L.. Sirovich, Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.. 12, No.. 1, January 1990, pp.. 103-108.. L.. Sirovich, M.. Kirby, Low-dimensional Procedure for the Characterization of Human Faces, Journal of the Optical Society of America A - Optics, Image Science and Vision, Vol.. 4, No.. 3, March 1987, pp.. 519-524.. , 4.. 58 MB.. Meytlis, Symmetry, Probability, and Recognition in Face Space, PNAS - Proceedings of the National Academy of Sciences, Vol.. 106, No.. 17, 28 April 2009, pp.. 6895-6899.. , 741 kB.. Sinha, B.. Balas, Y.. Ostrovsky, R.. Russell, Face Recognition by Humans: 19 Results All Computer Vision Researchers Should Know About, Proceedings of the IEEE, Vol.. 94, No.. 11, November 2006, pp.. 1948-1962.. Gross, S.. Baker, I.. Matthews, T.. Kanade, Face Recognition Across Pose and Illumination, Handbook of Face Recognition, Stan Z.. Li and Anil K.. Jain, ed.. , Springer-Verlag, June, 2004, 27 pages.. G.. Shakhnarovich, B.. Moghaddam, Face Recognition in Subspaces, Handbook of Face Recognition, Eds.. Stan Z.. Jain, Springer-Verlag, December 2004, 35 pages.. , 475 kB.. T.. De Bie, N.. Cristianini, R.. Rosipal, Eigenproblems in Pattern Recognition, Handbook of Computational Geometry for Pattern Recognition, Computer Vision, Neurocomputing and Robotics, E.. Bayro-Corrochano (editor), Springer-Verlag, Heidelberg, April 2004.. , 305 kB.. Gross, J.. Shi, J.. Cohn, Quo vadis Face Recognition? - The current state of the art in Face Recognition, Technical Report, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA, 25 pages.. , 2.. Torres, Is there any hope for face recognition?, Proc.. of the 5th International Workshop on Image Analysis for Multimedia Interactive Services, WIAMIS 2004, 21-23 April 2004, Lisboa, Portugal.. , 304 kB.. -F.. Chen, H.. -Y.. M.. Liao, J.. -C.. Lin, C.. Han, Why Recognition in a Statistics-based Face Recognition System Should be based on the Pure Face Portion: a Probabilistic Decision-based Proof, Pattern Recognition, Vol.. 34, No.. 5, 2001, pp.. 1393-1403.. , 568 kB.. X.. Lu, Image Analysis for Face Recognition, personal notes, May 2003, 36 pages.. , 1.. 24 MB.. B.. Moghaddam, Principal Manifolds and Probabilistic Subspaces for Visual Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.. 24, Issue 6, June 2002, pp.. 780-788.. Moghaddam, A.. Pentland, Probabilistic Visual Learning for Object Representation, IEEE Trans.. on Pattern Analysis and Machine Intelligence, Vol.. 19, No.. 7, July 1997, pp.. 696-710.. Turk, A Random Walk through Eigenspace, IEICE Transactions on Information and Systems, Vol.. E84-D, No.. 12, December 2001, pp.. 1586-1595.. 32 MB.. Y.. Chellappa, Image-based Face Recognition: Issues and Methods, Image Recognition and Classification , Ed.. Javidi, M.. Dekker, 2002, pp.. 375-402.. , 386 kB.. S.. Yambor, Analysis of PCA-based and Fisher Discriminant-Based Image Recognition Algorithms, M.. Thesis, Technical Report CS-00-103, Computer Science Department, Colorado State University, July 2000.. , 924 kB.. Nixon, Eye Spacing Measurement for Facial Recognition, Proceedings of the Society of Photo-Optical Instrument Engineers, SPIE, Vol.. 575, No.. 37, August 1985, pp.. 279-285.. 55 MB.. J.. Ruiz-del-Solar, P.. Navarrete, Eigenspace-based face recognition: a comparative study of different approaches, IEEE Transactions on Systems, Man and Cybernetics, Part C, Vol.. 35, Issue 3, August 2005, pp.. 315-325.. Moghaddam, Principal Manifolds and Probabilistic Subspaces for Visual Recognition, IEEE Trans.. 24, No.. 6, June 2002, pp.. Face Recognition Format for Data Interchange.. This standard specifies definitions of photographic (environment, subject pose, focus, etc.. ) properties, digital image attributes and a face interchange format for relevant applications, including human examination and computer automated face recognition.. Biometric data interchange formats - Part 5: Face image data.. ISO/IEC 19794-5:2005 specifies scene, photographic, digitization and format requirements for images of faces to be used in the context of both human verification and computer automated recognition.. The approach to specifying scene  ...   Google Scholar database are presented for completeness only.. More information about the search conditions that were used to generate the results are presented below.. Disclaimer:.. The choice of the data sources reflects personal opinion of the Face Recognition Homepage administrators.. Ranking used here should be treated as a guide-to-the-eye only.. No comparison of the scientific merit of the included papers was intended.. HIGHLY CITED PAPERS.. Cited By (SCOPUS).. Times Cited (WoS).. Cited By (Google Scholar).. Turk, A.. Pentland, Eigenfaces for Recognition, Journal of Cognitive Neurosicence, Vol.. 3, No.. 1, Win.. 1991, pp.. 71-86.. 2977.. 2411.. 5332.. N.. Belhumeur, J.. P.. Hespanha, D.. Kriegman, Eigenfaces vs.. Fisherfaces: Recognition Using Class Specific Linear Projection, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.. 711-720.. 2031.. 1794.. 3181.. K.. Jain, R.. Duin, J.. Mao, Statistical Pattern Recognition: A Review, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.. 22, No.. 1, January 2000, pp.. 4-37.. 1169.. 968.. 1849.. -H.. Yang, D.. Kriegman, N.. Ahuja, Detecting Faces in Images: A Survey, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.. 1, January 2002, pp.. 34-58.. 906.. 745.. 1474.. Chellappa, C.. L.. Wilson, S.. Sirohey, Human and Machine Recognition of Faces: A Survey, Proceedings of the IEEE, Vol.. 83, Issue 5, May 1995, pp.. 705-740.. 891.. 704.. 1653.. Phillips, H.. Moon, S.. A.. Rizvi, P.. Rauss, The FERET Evaluation Methodology for Face-Recognition Algorithms, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.. 10, October 2000, pp.. 1090-1104.. 852.. 753.. 1373.. Chellappa, P.. Phillips, A.. Rosenfeld, Face Recognition: A Literature Survey, ACM Computing Surveys, Vol.. 35, No.. 4, 2003, pp.. 793.. 626.. 1548.. Wiskott, J.. -M.. , Fellous, N.. Kruger, C.. D.. Von Malsburg, Face Recognition by Elastic Bunch Graph Matching, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.. 775-779.. 761.. 701.. 1410.. V.. Bruce, A.. Young, Understanding Face Recognition, The British Journal of Psychology, Vol.. 77, No.. 3, August 1986, pp.. 305-327.. 755.. ---.. 1178.. Viola, M.. Jones, Robust Real-Time Face Detection, International Journal of Computer Vision, Vol.. 57, No.. 2, 2004, pp.. 137-154.. 664.. 528.. 921.. 640.. 684.. 1459.. 1, 1990, pp.. 602.. 1193.. Sergent, S.. Ohta, B.. MacDonald, Functional Neuroanatomy of Face and Object Processing, A Positron Emission Tomography Study, Brain, Vol.. 115, No.. 1, February 1992, pp.. 15-36.. 594.. 682.. 689.. S.. Bentin, T.. Allison, A.. Puce, E.. Perez, G.. McCarthy, Electrophysiological Studies of Face Perception in Humans, Journal of Cognitive Neuroscience, Vol.. 8, No.. 6, 1996, pp.. 551-565.. 566.. 559.. 635.. Pentland, Probabilistic Visual Learning for Object Representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.. 582.. 595.. 986.. Diamond, S.. Carey, Why Faces Are and Are Not Special.. An Effect of Expertise, Journal of Experimental Psychology: General, Vol.. 2, 1986, pp.. 107-117.. 516.. 674.. Tanaka, M.. Farah, Parts and Wholes in Face Recognition, Quarterly Journal of Experimental Psychology Section A: Human Experimental Psychology, Vol.. 46, No.. 2, 1993, pp.. 225-245.. 512.. 604.. Swets, J.. Weng, Using Discriminant Eigenfeatures for Image Retrieval, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.. 18, No.. 8, 1996, pp.. 831-836.. 509.. 460.. 806.. SEARCH CONDITIONS.. :.. SCOPUS.. (.. scopus.. com/.. ), Elsevier B.. V.. Search for:.. ( face recognition OR facial recognition ) in (Article Title, Abstract, Keywords).. Date Range:.. Published (All years) to (Present).. Document Type:.. (All).. Subject Areas:.. (Life Sciences, Physical Sciences, Health Sciences, Social Sciences).. Sort:.. Cited By.. Status:.. WoS - Web of Science.. http://isiknowledge.. ) The Thomson Corporation.. Current Limits - Citation Databases:.. Science Citation Index Expanded (SCI-EXPANDED).. Timespan:.. All years (updated 2009-04-28).. Topic:.. face recognition OR facial recognition.. Sort by:.. Times Cited.. Google Scholar.. http://scholar.. google.. ) Google.. Search Query:.. Paper Title author:Surname.. Left graph shows how many items on.. face recognition.. were published between 1991 and 2006, and right graph shows the number of citations to source items indexed within WoS - Web of Science.. The Thomson Corporation.. The downloadable publications on this web-site are presented to ensure timely dissemination of scholarly and technical work.. Copyright and all rights therein are retained by authors or by other copyright holders.. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright.. These works may not be reposted without the explicit permission of the copyright holder.. Last update: 29 April 2009..

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  • Title: Face Recognition Homepage - New Papers
    Descriptive info: NEW PAPERS.. On this page most recent advances (the state-of-the-art) in face recognition will be presented.. Here you can find:.. paper title,.. author(s),.. link to other paper details.. for papers published in the last six months or so in high impact factor journals (these journals can be found under.. page).. Papers on this page will be constantly updated and older links will be removed when newer papers are published.. IEEE Transactions on Pattern Analysis and Machine Intelligence.. 3D Face Recognition Using Simulated Annealing and the Surface Interpenetration Measure.. Queirolo, C.. ; Silva, L.. ; Bellon, O.. R.. ; Segundo, M.. [.. ].. 3D Face Reconstruction from a Single Image Using a Single Reference Face Shape.. Kemelmacher-Shlizerman, I; Basri, R.. Cost-Sensitive Face Recognition.. Zhang, Y; Zhou, Z.. Age Synthesis and Estimation via Faces: A Survey.. Fu, Y.. ; Guo, G.. ; Huang, T.. Features versus Context: An Approach for Precise and Detailed Detection and Delineation of Faces and Facial Features.. Ding, L.. ; Martinez, A.. A Compositional and Dynamic Model for Face Aging.. Jinli Suo; Song-Chun Zhu; Shiguang Shan; Xilin Chen.. Active Testing for Face Detection and Localization.. Sznitman, R; Jedynak, B.. Linear Regression for Face Recognition.. Naseem, I.. ; Togneri, R.. ; Bennamoun, M.. Non-Lambertian Reflectance Modeling and Shape Recovery for Faces Using Tensor Splines.. Kumar, R; Barmpoutis, A; Banerjee, A; Vemuri, B.. Age-Invariant Face Recognition.. Unsang Park; Yiying Tong; Jain, A.. Decomposition of Complex Line Drawings with Hidden Lines for 3D Planar-Faced Manifold Object Reconstruction.. Liu, J; Chen, Y; Tang, X.. 3D Face Recognition Using Iso-geodesic Stripes.. Berretti, S; Del Bimbo, A; Pala, P.. Robust 3D Face Recognition by Local Shape Difference Boosting.. Wang, Y; Liu, J; Tang, X.. A Unified Probabilistic Framework for Spontaneous Facial Action Modeling and Understanding.. Yan Tong; Jixu Chen; Qiang Ji.. A Dynamic Texture Based Approach to Recognition of Facial Actions and their Temporal Models.. Koelstra, S; Pantic, M; Patras, I.. IEEE Transactions on Information Forensics and Security.. Face Matching and Retrieval Using Soft Biometrics.. Park, U.. ; Jain, A.. K.. A Hybrid Approach for Generating Secure and Discriminating Face Template.. Feng, Y.. ; Yuen, P.. Face Verification Across Age Progression Using Discriminative Methods.. Haibin Ling; Soatto, S.. ; Ramanathan, N.. ; Jacobs, D.. Plastic Surgery: A New Dimension to Face Recognition.. Singh, R; Vatsa, M; Bhatt, H; Baradwaj, S; Noore, A; Nooreyezdan, S.. Regional Registration for Expression Resistant 3D Face Recognition.. Alyuz, N; Gokberk, B; Akarun, L.. Face Recognition in Global Harmonic Subspaces.. Jiang, M.. ; Crookes, D; Luo, N.. ].. IEEE Transactions on Image Processing.. Misalignment-Robust Face Recognition.. Shuicheng Yan; Huan Wang; Jianzhuang Liu; Xiaoou Tang; Huang, T.. Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions.. Xiaoyang Tan; Triggs, B.. An Optical Flow-Based Approach to Robust Face Recognition Under Expression Variations.. Chao-Kuei Hsieh; Shang-Hong Lai; Yung-Chang Chen.. On-line Learning of Mutually Orthogonal Subspaces for Face Recognition by Image Sets.. Tae-Kyun Kim; Kittler, J.. ; Cipolla, R.. Extracting Multiple Features in the CID Color Space for Face Recognition.. Liu, Z.. ; Yang, J.. ; Liu, C.. Face Recognition by Exploring Information Jointly in Space, Scale and Orientation.. Lei, Z.. ; Liao, S.. ; Pietikainen, M.. ; Li, S.. Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor.. Baochang Zhang; Yongsheng Gao; Sanqiang Zhao; Jianzhuang Liu.. Fusing Local Patterns of Gabor Magnitude and Phase for Face Recognition.. Shufu Xie; Shiguang Shan; Xilin Chen; Jie Chen.. Cluster-Based Distributed Face Tracking in Camera Networks.. Yoder, J.. ; Medeiros, H.. ; Park, J.. ; Kak, A.. IEEE Transactions on Neural Networks.. Conformation-Based Hidden Markov Models: Application to Human Face Identification.. Bouchaffra, D.. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics.. Image Ratio Features for Facial Expression Recognition Application.. Mingli Song; Dacheng Tao; Zicheng Liu; Xuelong Li; Mengchu Zhou.. Automatic Location of Facial Feature Points and Synthesis of Facial Sketches Using Direct Combined Model.. Tu, C.. -T.. ; Lien, J.. Face Transformation With Harmonic Models by the Finite-Volume Method With Delaunay Triangulation.. Li, Z.. ; Chiang, J.. ; Suen, C.. Automatic Face Segmentation and Facial Landmark Detection in Range Images.. Pamplona Segundo, M.. ; Queirolo, C.. C.. Regularized Locality Preserving Projections and Its Extensions for Face Recognition.. Jiwen Lu; Yap-Peng Tan.. A Component-Based Framework for Generalized Face Alignment.. Huang, Y.. ; Liu, Q.. ; Metaxas, D.. N.. Graph-Preserving Sparse Nonnegative Matrix Factorization With Application to Facial Expression Recognition.. Zhi, R.. ; Flierl, M.. ; Ruan, Q.. ; Kleijn, W.. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications.. Incremental Embedding and Learning in the Local Discriminant Subspace With Application to Face Recognition.. Cheng, M.. ; Fang, B.. ; Tang, Y.. ; Zhang, T.. ; Wen, J.. Combining Perceptual Features With Diffusion Distance for Face Recognition.. Zhou, H.. ; Sadka, A.. International Journal of Computer Vision.. Estimating Facial Reflectance Properties Using Shape-from-Shading.. William A.. Smith and Edwin R.. Hancock.. Pattern Recognition.. Discriminability and reliability indexes: Two new measures to enhance multi-image face recognition.. Weiwen Zou, Pong C.. Yuen.. Face recognition using discriminant locality preserving projections based on maximum margin criterion.. Gui-Fu Lu, Zhong Lin, Zhong Jin.. Face recognition using  ...   a combined approach for recognizing face images.. Ruba Soundar and K.. Murugesan.. 3D modeling of faces from near infrared images using statistical learning.. Ying Zheng, Stan Z.. Li, Jianglong Chang and Zengfu Wang.. Computer Vision and Image Understanding.. 3D face reconstructions from photometric stereo using near infrared and visible light.. Mark F.. Hansen, Gary A.. Atkinson, Lyndon N.. Smith, Melvyn L.. Smith.. Fusing bio-inspired vision data for simplified high level scene interpretation: Application to face motion analysis.. Benoit, A.. Caplier.. A video-based door monitoring system using local appearance-based face models.. Hazim Kemal Ekenel, Johannes Stallkamp, Rainer Stiefelhagen.. Multi-view face segmentation using fusion of statistical shape and appearance models.. Constantine Butakoff, Alejandro F.. Comparing and combining lighting insensitive approaches for face recognition.. Raghuraman Gopalan, David Jacobs.. Image and Vision Computing.. Biometric classifier update using online learning: A case study in near infrared face verification.. Richa Singh, Mayank Vatsa, Arun Ross, Afzel Noore.. Learning a generic 3D face model from 2D image databases using incremental Structure-from-Motion.. Jose Gonzalez-Mora, Fernando De la Torre, Nicolas Guil, Emilio L.. Zapata.. Video-based face model fitting using Adaptive Active Appearance Model.. Xiaoming Liu.. A new ranking method for principal components analysis and its application to face image analysis.. Carlos Eduardo Thomaz, Gilson Antonio Giraldi.. Facial gender classification using shape-from-shading.. Jing Wu, William A.. Smith, Edwin R.. FRVT 2006: Quo Vadis face quality.. Ross Beveridge, Geof H.. Givens, P.. Jonathon Phillips, Bruce A.. Draper, David S.. Bolme, Yui Man Lui.. Probabilistic learning for fully automatic face recognition across pose.. Saquib Sarfraz, Olaf Hellwich.. Adjusted pixel features for robust facial component classification.. Christoph Mayer, Matthias Wimmer, Bernd Radig.. Facial feature localization using weighted vector concentration approach.. Tatsuo Kozakaya, Tomoyuki Shibata, Mayumi Yuasa, Osamu Yamaguchi.. Non-rigid face tracking with enforced convexity and local appearance consistency constraint.. Simon Lucey, Yang Wang, Jason Saragih, Jeffery F.. Cohn.. Modelling human perception of static facial expressions.. Sorci, G.. Antonini, J.. Cruz, T.. Robin, M.. Bierlaire, J.. -Ph.. Thiran.. Multi-PIE.. Ralph Gross, Iain Matthews, Jeffrey Cohn, Takeo Kanade, Simon Baker.. Recognizing faces using Adaptively Weighted Sub-Gabor Array from a single sample image per enrolled subject.. Hamidreza Rashidy Kanan, Karim Faez.. A novel statistical generative model dedicated to face recognition.. Guillaume Heusch, Sébastien Marcel.. Gabor texture representation method for face recognition using the Gamma and generalized Gaussian models.. Lei Yu, Zhongshi He, Qi Cao.. Optical Engineering.. Locality projection discriminant analysis with an application to face recognition.. Xuchu Wang and Yanmin Niu.. Neighborhood discriminant embedding in face recognition.. Dexing Zhong, Jiuqiang Han, Xinman Zhang, and Yongli Liu.. Face recognition based on fringe pattern analysis.. Hong Guo and Peisen Huang.. Empirical mode decomposition-based facial pose estimation inside video sequences.. Chunmei Qing, Jianmin Jiang, and Zhijing Yang.. Machine Vision and Applications.. On design and optimization of face verification systems that are smart-card based.. Thirimachos Bourlai, Josef Kittler and Kieron Messer.. Using bidimensional regression to assess face similarity.. Sarvani Kare, Ashok Samal and David Marx.. Fusing continuous spectral images for face recognition under indoor and outdoor illuminants.. Chang, A.. Koschan, B.. Abidi and M.. Abidi.. Multimedia Tools and Applications.. Aligned texture map creation for pose invariant face recognition.. Antonio Rama, Francesc Tarrés and Jürgen Rurainsky.. Multimodal information fusion application to human emotion recognition from face and speech.. Muharram Mansoorizadeh and Nasrollah Moghaddam Charkari.. Decimation of human face model for real-time animation in intelligent multimedia systems.. Soo-Kyun Kim, Syung-Og An, Min Hong, Doo-Soon Park and Shin-Jin Kang.. Journal of Vision.. Lateralization of kin recognition signals in the human face.. Maria F.. Dal Martello and Laurence T.. Maloney.. Frames of reference for biological motion and face perception.. Dorita H.. F.. Chang, Laurence R.. Harris, and Nikolaus F.. Troje.. Face inversion impairs holistic perception: Evidence from gaze-contingent stimulation.. Goedele Van Belle, Peter De Graef, Karl Verfaillie, Bruno Rossion, and Philippe Lefevre.. Distributed representations of dynamic facial expressions in the superior temporal sulcus.. Christopher P.. Said, Christopher D.. Moore, Andrew D.. Engell, Alexander Todorov, and James V.. Haxby.. Four-to-six-year-old children use norm-based coding in face-space.. Linda Jeffery, Elinor McKone, Rebecca Haynes, Eloise Firth, Elizabeth Pellicano, and Gillian Rhodes.. Estimating predictive stimulus features from psychophysical data: The decision image technique applied to human faces.. Jakob H.. Macke and Felix A.. Wichmann.. Perceptual expertise with objects predicts another hallmark of face perception.. Rankin Williams McGugin and Isabel Gauthier.. Fast saccades toward faces: Face detection in just 100 ms.. Sebastien M.. Crouzet, Holle Kirchner, and Simon J.. Thorpe.. Perceptual consequences of face viewpoint adaptation: Face viewpoint aftereffect, changes of differential sensitivity to face view, and their relationship.. Juan Chen, Hua Yang, Aobing Wang, and Fang Fang.. Looking away from faces: Influence of high-level visual processes on saccade programming.. Stéphanie M.. Morand, Marie-Hélene Grosbras, Roberto Caldara, and Monika Harvey.. Holistic perception of individual faces in the right middle fusiform gyrus as evidenced by the composite face illusion.. Christine Schiltz, Laurence Dricot, Rainer Goebel, and Bruno Rossion.. The face-in-the-crowd effect: When angry faces are just cross(es).. Carlos M.. Coelho, Steven Cloete, and Guy Wallis.. Inverting faces elicits sensitivity to race on the N170 component: A cross-cultural study.. Luca Vizioli, Kay Foreman, Guillaume A.. Rousselet, and Roberto Caldara.. Last update: 26 September 2010..

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  • Title: Face Recognition Homepage - Databases
    Descriptive info: DATABASES.. When benchmarking an algorithm it is recommendable to use a standard test data set for researchers to be able to directly compare the results.. While there are many databases in use currently, the choice of an appropriate database to be used should be made based on the task given (aging, expressions, lighting etc).. Another way is to choose the data set specific to the property to be tested (e.. g.. how algorithm behaves when given images with lighting changes or images with different facial expressions).. If, on the other hand, an algorithm needs to be trained with more images per class (like LDA), Yale face database is probably more appropriate than FERET.. Read more:.. Gross, Face Databases, Handbook of Face Recognition, Stan Z.. , Springer-Verlag, February 2005, 22 pages.. Here are some face data sets often used by researchers:.. The Color FERET Database, USA.. The FERET program set out to establish a large database of facial images that was gathered independently from the algorithm developers.. Dr.. Harry Wechsler at George Mason University was selected to direct the collection of this database.. The database collection was a collaborative effort between Dr.. Wechsler and Dr.. Phillips.. The images were collected in a semi-controlled environment.. To maintain a degree of consistency throughout the database, the same physical setup was used in each photography session.. Because the equipment had to be reassembled for each session, there was some minor variation in images collected on different dates.. The FERET database was collected in 15 sessions between August 1993 and July 1996.. The database contains 1564 sets of images for a total of 14,126 images that includes 1199 individuals and 365 duplicate sets of images.. A duplicate set is a second set of images of a person already in the database and was usually taken on a different day.. For some individuals, over two years had elapsed between their first and last sittings, with some subjects being photographed multiple times.. This time lapse was important because it enabled researchers to study, for the first time, changes in a subject's appearance that occur over a year.. SCface - Surveillance Cameras Face Database.. SCface is a database of static images of human faces.. Images were taken in uncontrolled indoor environment using five video surveillance cameras of various qualities.. Database contains 4160 static images (in visible and infrared spectrum) of 130 subjects.. Images from different quality cameras mimic the real-world conditions and enable robust face recognition algorithms testing, emphasizing different law enforcement and surveillance use case scenarios.. SCface database is freely available to research community.. The paper describing the database is available.. here.. Multi-PIE.. A close relationship exists between the advancement of face recognition algorithms and the availability of face databases varying factors that affect facial appearance in a controlled manner.. The PIE database, collected at Carnegie Mellon University in 2000, has been very influential in advancing research in face recognition across pose and illumination.. Despite its success the PIE database has several shortcomings: a limited number of subjects, a single recording session and only few expressions captured.. To address these issues researchers at Carnegie Mellon University collected the Multi-PIE database.. It contains 337 subjects, captured under 15 view points and 19 illumination conditions in four recording sessions for a total of more than 750,000 images.. The Yale Face Database.. Contains 165 grayscale images in GIF format of 15 individuals.. There are 11 images per subject, one per different facial expression or configuration: center-light, w/glasses, happy, left-light, w/no glasses, normal, right-light, sad, sleepy, surprised, and wink.. The Yale Face Database B.. Contains 5760 single light source images of 10 subjects each seen under 576 viewing conditions (9 poses x 64 illumination conditions).. For every subject in a particular pose, an image with ambient (background) illumination was also captured.. PIE Database, CMU.. A database of 41,368 images of 68 people, each person under 13 different poses, 43 different illumination conditions, and with 4 different expressions.. Project - Face In Action (FIA) Face Video Database, AMP, CMU.. Capturing scenario mimics the real world applications, for example, when a person is going through the airport check-in point.. Six cameras capture human faces from three different angles.. Three out of the six cameras have smaller focus length, and the other three have larger focus length.. Plan to capture 200 subjects in 3 sessions in different time period.. For one session, both in-door and out-door scenario will be captured.. User-dependent pose and expression variation are expected from the video sequences.. AT T The Database of Faces (formerly The ORL Database of Faces ).. Ten different images of each of 40 distinct subjects.. For some subjects, the images were taken at different times, varying the lighting, facial expressions (open / closed eyes, smiling / not smiling) and facial details (glasses / no glasses).. All the images were taken against a dark homogeneous background with the subjects in an upright, frontal position (with tolerance for some side movement).. Cohn-Kanade AU Coded Facial Expression Database.. Subjects in the released portion of the Cohn-Kanade AU-Coded Facial Expression Database are 100 university students.. They ranged in age from 18 to 30 years.. Sixty-five percent were female, 15 percent were African-American, and three percent were Asian or Latino.. Subjects were instructed by an experimenter to perform a series of 23 facial displays that included single action units and combinations of action units.. Image sequences from neutral to target display were digitized into 640 by 480 or 490 pixel arrays with 8-bit precision for grayscale values.. Included with the image files are sequence files; these are short text files that describe the order in which images should be read.. MIT-CBCL Face Recognition Database.. The MIT-CBCL face recognition database contains face images of 10 subjects.. It provides two training sets: 1.. High resolution pictures, including frontal, half-profile and profile view; 2.. Synthetic images (324/subject) rendered from 3D head models of the 10 subjects.. The head models were generated by fitting a morphable model to the high-resolution training images.. The 3D models are not included in the database.. The test set consists of 200 images per subject.. We varied the illumination, pose (up to about 30 degrees of rotation in depth) and the background.. Image Database of Facial Actions and Expressions - Expression Image Database.. 24 subjects are represented in this database, yielding between about 6 to 18 examples of the 150 different requested actions.. Thus, about 7,000 color images are included in the database, and each has a matching gray scale image used in the neural network analysis.. Face Recognition Data, University of Essex, UK.. 395 individuals (male and female), 20 images per individual.. Contains images of people of various racial origins, mainly of first year undergraduate students, so the majority of indivuals are between 18-20 years old but some older individuals are also present.. Some individuals are wearing glasses and beards.. NIST Mugshot Identification Database.. There are images of 1573 individuals (cases) 1495 male and 78 female.. The database contains both front and side (profile) views when available.. Separating front views and profiles, there are 131 cases with two or more front views and 1418 with only one front view.. Profiles have 89 cases with two or more profiles and 1268 with only one profile.. Cases with both fronts and profiles have 89 cases with two or more of both fronts and profiles, 27 with two or more fronts and one profile, and 1217 with only one front and one profile.. NLPR Face Database.. 450 face images.. 896 x 592 pixels.. JPEG format.. 27 or so unique people under with different lighting/expressions/backgrounds.. M2VTS Multimodal Face Database (Release 1.. 00).. Database is made up from 37 different faces and provides 5 shots for each person.. These shots were taken at one week intervals or when drastic face changes occurred in the meantime.. During each shot, people have been asked to count from '0' to '9' in their native language (most of the people are French speaking), rotate the head from 0 to -90 degrees, again to 0, then to +90 and back to 0 degrees.. Also, they have been asked to rotate the head once again without glasses if they wear any.. The Extended M2VTS Database, University of Surrey, UK.. Contains four recordings of 295 subjects taken over a period of four months.. Each recording contains a speaking head shot and a rotating head shot.. Sets of data taken from this database are available including high quality colour images, 32 KHz 16-bit sound files, video sequences and a 3D model.. The AR Face Database, Purdue University, USA.. 4,000 color images corresponding to 126 people's faces (70 men and 56 women).. Images feature frontal view faces with different facial expressions, illumination conditions, and occlusions (sun glasses and scarf).. The University of Oulu Physics-Based Face Database.. Contains 125 different faces each in 16 different camera calibration and illumination condition, an additional 16 if the person has glasses.. Faces in frontal position captured under Horizon, Incandescent, Fluorescent and Daylight illuminant.. Includes 3 spectral reflectance of skin per person measured from both cheeks and forehead.. Contains RGB spectral response of camera used and spectral power distribution of illuminants.. CAS-PEAL Face Database.. The CAS-PEAL face database has been constructed under the sponsors of National Hi-Tech Program and ISVISION.. The goals to create the PEAL face database include: providing the worldwide researchers of FR community a large-scale Chinese face database for training and evaluating their algorithms; facilitating the development of FR by providing large-scale face images with different sources of variations, especially Pose, Expression, Accessories, and Lighting (PEAL); advancing the state-of-the-art face recognition technologies aiming at practical applications especially for the oriental.. Japanese Female Facial Expression (JAFFE) Database.. The database contains 213 images of 7 facial expressions (6 basic facial expressions + 1 neutral) posed by 10 Japanese female models.. Each image has been rated on 6 emotion adjectives by 60 Japanese subjects.. BioID Face DB - HumanScan AG, Switzerland.. The dataset consists of 1521 gray level images with a resolution of 384x286 pixel.. Each one shows the frontal view of a face of one out of 23 different test persons.. For comparison reasons the set also contains manually set eye postions.. Psychological Image Collection at Stirling (PICS).. This is a collection of images useful for research in Psychology, such as sets of faces and objects.. The images in the database are organised into SETS, with each set often representing a separate experimental study.. The Sheffield Face Database (previously: The UMIST Face Database).. Consists of 564 images of 20 people.. Each covering a range of poses from profile to frontal views.. Subjects cover a range of race/sex/appearance.. Each subject exists in their own directory labelled 1a, 1b,.. 1t and images are numbered consequetively as they were taken.. The files are all in PGM format, approximately 220 x 220 pixels in 256 shades of grey.. Face Video Database of the Max Planck Institute for Biological Cybernetics.. This database contains short video sequences of facial Action Units recorded simultaneously from six different viewpoints, recorded in 2003 at the Max Planck Institute for Biological Cybernetics.. The video cameras were arranged at 18 degrees intervals in a semi-circle around the subject at a distance of roughly 1.. 3m.. The cameras recorded 25 frames/sec at 786x576 video resolution, non-interlaced.. In order to facilitate the recovery of rigid head motion, the subject wore a headplate with 6 green markers.. The website contains a total of 246 video sequences in MPEG1 format.. Caltech Faces.. EQUINOX HID Face Database.. Human identification from facial features has been studied primarily using imagery from visible video cameras.. Thermal imaging sensors are one of the most innovative emerging techonologies in the market.. Fueled by ever lowering costs and improved sensitivity and resolution, our sensors provide exciting new oportunities for biometric identification.. As part of our involvement in this effort, Equinox is collecting an extensive database of face imagery in the following modalities: coregistered broadband-visible/LWIR (8-12 microns), MWIR (3-5 microns), SWIR (0.. 9-1.. 7 microns).. This data collection is made available for experimentation and statistical performance evaluations.. VALID Database.. With the aim to facilitate the development of robust audio, face, and multi-modal person recognition systems, the large and realistic multi-modal (audio-visual) VALID database was acquired in a noisy real world office scenario with no control on illumination or acoustic noise.. The database consists of five recording sessions of 106 subjects over a period of one month.. One session is recorded in a studio with controlled lighting and no background noise, the other 4 sessions are recorded in office type scenarios.. The database contains uncompressed JPEG Images at resolution of 720x576 pixels.. The UCD Colour Face Image Database for Face Detection.. The database has two parts.. Part one contains colour pictures of faces having a high degree of variability in scale, location, orientation, pose, facial expression and lighting conditions, while part two has manually segmented results for each of the images in part one of the database.. These images are acquired from a wide variety of sources such as digital cameras, pictures scanned using photo-scanner, other face databases and the World Wide Web.. The database is intended for distribution to researchers.. Georgia Tech Face Database.. The database contains images of 50 people and is stored in JPEG format.. For each individual, there are 15 color images captured between 06/01/99 and 11/15/99.. Most of the images were taken in two different sessions to take into account the variations in illumination conditions, facial expression, and appearance.. In addition to this, the faces were captured at different scales and orientations.. Indian Face Database.. The database contains a set of face images taken in February, 2002 in the IIT Kanpur campus..  ...   computer.. The camera used is a JAI camera, which is sensitive to NIR band.. The active light source is in the NIR spectrum between 780nm - 1,100 nm.. The peak wavelength is 850 nm.. The strength of the total LED lighting is adjusted to ensure a good quality of the NIR face images when the camera face distance is between 80 cm - 120 cm, which is convenient for the users.. By using the data acquisition device described above, we collected NIR face images from 335 subjects.. During the recording, the subject was first asked to sit in front of the camera, and the normal frontal face images of him/her were collected.. Then the subject was asked to make expression and pose changes and the corresponding images were collected.. To collect face images with scale variations, we asked the subjects to move near to or away from the camera in a certain range.. At last, to collect face images with time variations, samples from 15 subjects were collected at two different times with an interval of more than two months.. In each recording, we collected about 100 images from each subject, and in total about 34,000 images were collected in the PolyU-NIRFD database.. The Biometric Research Centre at The Hong Kong Polytechnic University established a Hyperspectral Face database.. The indoor hyperspectral face acquisition system was built which mainly consists of a CRI's VariSpec LCTF and a Halogen Light, and includes a hyperspectral dataset of 300 hyperspectral image cubes from 25 volunteers with age range from 21 to 33 (8 female and 17 male).. For each individual, several sessions were collected with an average time space of 5 month.. The minimal interval is 3 months and the maximum is 10 months.. Each session consists of three hyperspectral cubes - frontal, right and left views with neutral-expression.. The spectral range is from 400 nm to 720 nm with a step length of 10 nm, producing 33 bands in all.. Since the database was constructed over a long period of time, significant appearance variations of the subjects, e.. changes of hair style and skin condition, are presented in the data.. In data collection, positions of the camera, light and subject are fixed, which allows us to concentrate on the spectral characteristics for face recognition without masking from environmental changes.. The MOBIO database consists of bi-modal (audio and video) data taken from 152 people.. The database has a female-male ratio or nearly 1:2 (100 males and 52 females) and was collected from August 2008 until July 2010 in six different sites from five different countries.. This led to a diverse bi-modal database with both native and non-native English speakers.. In total 12 sessions were captured for each client: 6 sessions for Phase I and 6 sessions for Phase II.. The Phase I data consists of 21 questions with the question types ranging from: Short Response Questions, Short Response Free Speech, Set Speech, and Free Speech.. The Phase II data consists of 11 questions with the question types ranging from: Short Response Questions, Set Speech, and Free Speech.. The database was recorded using two mobile devices: a mobile phone and a laptop computer.. The mobile phone used to capture the database was a NOKIA N93i mobile while the laptop computer was a standard 2008 MacBook.. The laptop was only used to capture part of the first session, this first session consists of data captured on both the laptop and the mobile phone.. Texas 3D Face Recognition database (Texas 3DFRD) contains 1149 pairs of facial color and range images of 105 adult human subjects.. The images were acquired at the company Advanced Digital Imaging Research (ADIR), LLC (Friendswood, TX), formerly a subsidiary of Iris International, Inc.. (Chatsworth, CA), with assistance from research students and faculty from the Laboratory for Image and Video Engineering (LIVE) at The University of Texas at Austin.. This project was sponsored by the Advanced Technology Program of the National Institute of Standards and Technology (NIST).. The database is being made available by Dr.. Alan C Bovik at UT Austin.. The images were acquired using a stereo imaging system at a high spatial resolution of 0.. 32 mm.. The color and range images were captured simultaneously and thus are perfectly registered to each other.. All faces have been normalized to the frontal position and the tip of the nose is positioned at the center of the image.. The images are of adult humans from all the major ethnic groups and both genders.. For each face, is also available information about the subjects' gender, ethnicity, facial expression, and the locations 25 anthropometric facial fiducial points.. These fiducial points were located manually on the facial color images using a computer based graphical user interface.. Specific data partitions (training, gallery, and probe) that were employed at LIVE to develop the.. Anthropometric 3D Face Recognition algorithm.. are also available.. The database contains both spontaneous and posed expressions of more than 100 subjects, recorded simultaneously by a visible and an infrared thermal camera, with illumination provided from three different directions.. The posed database also includes expression images with and without glasses.. The FEI face database is a Brazilian face database that contains a set of face images taken between June 2005 and March 2006 at the Artificial Intelligence Laboratory of FEI in Sao Bernardo do Campo, Sao Paulo, Brazil.. There are 14 images for each of 200 individuals, a total of 2800 images.. All images are colourful and taken against a white homogenous background in an upright frontal position with profile rotation of up to about 180 degrees.. Scale might vary about 10% and the original size of each image is 640x480 pixels.. All faces are mainly represented by students and staff at FEI, between 19 and 40 years old with distinct appearance, hairstyle, and adorns.. The number of male and female subjects are exactly the same and equal to 100.. ChokePoint video dataset is designed for experiments in person identification/verification under real-world surveillance conditions using existing technologies.. An array of three cameras was placed above several portals (natural choke points in terms of pedestrian traffic) to capture subjects walking through each portal in a natural way.. While a person is walking through a portal, a sequence of face images (ie.. a face set) can be captured.. Faces in such sets will have variations in terms of illumination conditions, pose, sharpness, as well as misalignment due to automatic face localisation/detection.. Due to the three camera configuration, one of the cameras is likely to capture a face set where a subset of the faces is near-frontal.. The dataset consists of 25 subjects (19 male and 6 female) in portal 1 and 29 subjects (23 male and 6 female) in portal 2.. In total, the dataset consists of 54 video sequences and 64,204 labelled face images.. The University of Milano Bicocca 3D face database is a collection of multimodal (3D + 2D colour images) facial acquisitions.. The database is available to universities and research centers interested in face detection, face recognition, face synthesis, etc.. The UMB-DB has been acquired with a particular focus on facial occlusions, i.. scarves, hats, hands, eyeglasses and other types of occlusion wich can occur in real-world scenarios.. The primary use of VADANA is for the problems of face verification and recognition across age progression.. The main characteristics of VADANA, which distinguish it from current benchmarks, is the large number of intra-personal pairs (order of 168 thousand); natural variations in pose, expression and illumination; and the rich set of additional meta-data provided along with standard partitions for direct comparison and bench-marking efforts.. MORPH database is the largest publicly available longitudinal face database.. The MORPH database contains 55,000 images of more than 13,000 people within the age ranges of 16 to 77.. There are an average of 4 images per individual with the time span between each image being an average of 164 days.. This data set was comprised for research on facial analytics and facial recognition.. LDHF database contains both visible (VIS) and near-infrared (NIR) face images at distances of 60 m, 100 m and 150 m outdoors and at a 1 m distance indoors.. Face images of 100 subjects (70 males and 30 females) were captured; for each subject one image was captured at each distance in daytime and nighttime.. All the images of individual subjects are frontal faces without glasses and collected in a single sitting.. This unique 3D face database is amongst the largest currently available, containing 3187 sessions of 453 subjects, captured in two recording periods of approximately six months each.. The Photoface device was located in an unsupervised corridor allowing real-world and unconstrained capture.. Each session comprises four differently lit colour photographs of the subject, from which surface normal and albedo estimations can be calculated (photometric stereo Matlab code implementation included).. This allows for many testing scenarios and data fusion modalities.. Eleven facial landmarks have been manually located on each session for alignment purposes.. Additionally, the Photoface Query Tool is supplied (implemented in Matlab), which allows for subsets of the database to be extracted according to selected metadata e.. gender, facial hair, pose, expression.. The Dataset consists of multimodal facial images of 52 people (14 females, 38 males) acquired with a Kinect sensor.. The data is captured in two sessions at different intervals (of about two weeks).. In each session, 9 facial images are collected from each person according to different facial expressions, lighting and occlusion conditions: neutral, smile, open mouth, left profile, right profile, occluded eyes, occluded mouth, side occlusion with a sheet of paper and light on.. An RGB color image, a depth map (provided both as a bitmap depth image and a text file containing the original depth levels sensed by Kinect) as well as the associated 3D data are provided for all samples.. In addition, the dataset includes 6 manually labeled landmark positions for every face: left eye, right eye, tip of the nose, left side of mouth, right side of mouth and the chin.. Other information, such as gender, year of birth, ethnicity, glasses (whether a person wears glasses or not) and the time of each session are also available.. The data set contains 3,425 videos of 1,595 different people.. All the videos were downloaded from YouTube.. An average of 2.. 15 videos are available for each subject.. The shortest clip duration is 48 frames, the longest clip is 6,070 frames, and the average length of a video clip is 181.. 3 frames.. In designing our video data set and benchmarks we follow the example of the 'Labeled Faces in the Wild' LFW image collection.. Specifically, our goal is to produce a large scale collection of videos along with labels indicating the identities of a person appearing in each video.. In addition, we publish benchmark tests, intended to measure the performance of video pair-matching techniques on these videos.. Finally, we provide descriptor encodings for the faces appearing in these videos, using well established descriptor methods.. The dataset consists of 151 subjects, specifically Caucasian females, from YouTube makeup tutorials.. Images of the subjects before and after the application of makeup were captured.. There are four shots per subject: two shots before the application of makeup and two shots after the application of makeup.. For a few subjects, three shots each before and after the application of makeup were obtained.. The makeup in these face images varies from subtle to heavy.. The cosmetic alteration is mainly in the ocular area, where the eyes have been accentuated by diverse eye makeup products.. Additional changes are on the quality of the skin due to the application of foundation and change in lip color.. This dataset includes some variations in expression and pose.. The illumination condition is reasonably constant over multiple shots of the same subject.. In few cases, the hair style before and after makeup changes drastically.. The VMU dataset was assembled by synthetically adding makeup to 51 female Caucasian subjects in the FRGC dataset.. We added makeup by using a publicly available tool from Taaz.. Three virtual makeovers were created: (a) application of lipstick only; (b) application of eye makeup only; and (c) application of a full makeup consisting of lipstick, foundation, blush and eye makeup.. Hence, the assembled dataset contains four images per subject: one before-makeup shot and three aftermakeup shots.. The MIW dataset contains 125 subjects with 1-2 images per subject.. Total number of images is 154 (77 with makeup and 77 without makeup).. The images are obtained from the internet and the faces are unconstrained.. The 3D Mask Attack Database (3DMAD) is a biometric (face) spoofing database.. It currently contains 76500 frames of 17 persons, recorded using Kinect for both real-access and spoofing attacks.. Each frame consists of: (1) a depth image (640x480 pixels 1x11 bits); (2) the corresponding RGB image (640x480 pixels 3x8 bits); (3) manually annotated eye positions (with respect to the RGB image).. The data is collected in 3 different sessions for all subjects and for each session 5 videos of 300 frames are captured.. The recordings are done under controlled conditions, with frontal-view and neutral expression.. The first two sessions are dedicated to the real access samples, in which subjects are recorded with a time delay of ~2 weeks between the acquisitions.. In the third session, 3D mask attacks are captured by a single operator (attacker).. If you use this database please cite this publication: N.. Erdogmus and S.. Marcel.. Spoofing in 2D Face Recognition with 3D Masks and Anti-spoofing with Kinect.. , in IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), 2013.. Source code to reproduce experiments in the paper:.. https://pypi.. python.. org/pypi/maskattack.. lbp..

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  • Title: Face Recognition Homepage - Algorithms
    Descriptive info: Image-Based.. Face Recognition Algorithms.. Video-Based.. Click here.. Image-Based Face Recognition Algorithms.. PCA.. ICA.. LDA.. EP.. EBGM.. Kernel Methods.. Trace Transform.. AAM.. 3-D Morphable Model.. 3-D Face Recognition.. Bayesian Framework.. SVM.. HMM.. Boosting Ensemble.. Algorithms Comparisons.. Derived from Karhunen-Loeve's transformation.. Given an s-dimensional vector representation of each face in a training set of images, Principal Component Analysis (PCA) tends to find a t-dimensional subspace whose basis vectors correspond to the maximum variance direction in the original image space.. This new subspace is normally lower dimensional (t s).. If the image elements are considered as random variables, the PCA basis vectors are defined as eigenvectors of the scatter matrix.. Read more:.. 1, 1991, pp.. , 10.. 6 MB.. Pentland, Face Recognition Using Eigenfaces, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3-6 June 1991, Maui, Hawaii, USA, pp.. 586-591.. Pentland, B.. Moghaddam, T.. Starner, View-Based and Modular Eigenspaces for Face Recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 21-23 June 1994, Seattle, Washington, USA, pp.. 84-91.. Moon, P.. Phillips, Computational and Performance aspects of PCA-based Face Recognition Algorithms, Perception, Vol.. 30, 2001, pp.. 303-321.. 61 MB.. Independent Component Analysis (ICA) minimizes both second-order and higher-order dependencies in the input data and attempts to find the basis along which the data (when projected onto them) are -.. statistically independent.. Bartlett et al.. provided two architectures of ICA for face recognition task:.. Architecture I.. - statistically independent basis images, and.. Architecture II.. - factorial code representation.. Bartlett, J.. Movellan, T.. Sejnowski, Face Recognition by Independent Component Analysis, IEEE Trans.. on Neural Networks, Vol.. 13, No.. 6, November 2002, pp.. 1450-1464.. |.. source code.. Liu, H.. Wechsler, Comparative Assessment of Independent Component Analysis (ICA) for Face Recognition, Proc.. of the Second International Conference on Audio- and Video-based Biometric Person Authentication, AVBPA'99, 22-24 March 1999, Washington D.. 211-216.. , 158 kB.. Linear Discriminant Analysis (LDA) finds the vectors in the underlying space that best discriminate among classes.. For all samples of all classes the between-class scatter matrix.. and the within-class scatter matrix.. are defined.. The goal is to maximize.. while minimizing.. , in other words, maximize the ratio det|.. |/det|.. This ratio is maximized when the column vectors of the projection matrix are the eigenvectors of (.. ^-1 × S.. Etemad, R.. Chellappa, Discriminant Analysis for Recognition of Human Face Images, Journal of the Optical Society of America A, Vol.. 14, No.. 8, August 1997, pp.. 1724-1733.. 04 MB.. Fisherfaces: Recognition using Class Specific Linear Projection, Proc.. of the 4th European Conference on Computer Vision, ECCV'96, 15-18 April 1996, Cambridge, UK, pp.. 45-58.. , 662 kB.. Krishnaswamy, Discriminant Analysis of Principal Components for Face Recognition, Proc.. of the 3rd IEEE International Conference on Face and Gesture Recognition, FG'98, 14-16 April 1998, Nara, Japan, pp.. 336-341.. Martinez, A.. Kak, PCA versus LDA, IEEE Trans.. 2, 2001, pp.. 228-233.. Zhao, A.. Krishnaswamy, R.. Chellappa, D.. Weng, Discriminant Analysis of Principal Components for Face Recognition, Face Recognition: From Theory to Applications, H.. Wechsler, P.. Phillips, V.. Bruce, F.. F.. Soulie, and T.. Huang, eds.. , Springer-Verlag, Berlin, 1998, pp.. 73-85.. , 338 kB.. Lu, K.. Plataniotis, A.. Venetsanopoulos, Face Recognition Using LDA-Based Algorithms, IEEE Trans.. 1, January 2003, pp.. 195-200.. , 652 kB.. Aa eigenspace-based adaptive approach that searches for the best set of projection axes in order to maximize a fitness function, measuring at the same time the classification accuracy and generalization ability of the system.. Because the dimension of the solution space of this problem is too big, it is solved using a specific kind of genetic algorithm called Evolutionary Pursuit (EP).. Wechsler, Evolutionary Pursuit and Its Application to Face Recognition, IEEE Trans.. 6, June 2000, pp.. 570-582.. Wechsler, Face Recognition Using Evolutionary Pursuit, Proc.. of the Fifth European Conference on Computer Vision, ECCV'98, Vol II, 02-06 June 1998, Freiburg, Germany, pp.. 596-612.. , 785 kB.. Elastic Bunch Graph Matching (EBGM).. All human faces share a similar topological structure.. Faces are represented as graphs, with nodes positioned at fiducial points.. (exes, nose.. ) and edges labeled with 2-D distance vectors.. Each node contains a set of 40 complex Gabor wavelet coefficients at different scales and orientations (phase, amplitude).. They are called jets.. Recognition is based on labeled graphs.. A labeled graph is a set of nodes connected by edges, nodes are labeled with jets, edges are labeled with distances.. Fellous, N.. Krueuger, C.. von der Malsburg, Face Recognition by Elastic Bunch Graph Matching, Chapter 11 in Intelligent Biometric Techniques in Fingerprint and Face Recognition, eds.. Jain et al.. , CRC Press, 1999, pp.. 355-396.. , 735 kB.. von der Malsburg, Face Recognition by Elastic Bunch Graph Matching, IEEE Trans.. 7, 1997, pp.. The face manifold in subspace need not be linear.. Kernel methods are a generalization of linear methods.. Direct non-linear manifold schemes are explored to learn this non-linear manifold.. Yang, Kernel Eigenfaces vs.. Kernel Fisherfaces: Face Recognition Using Kernel Methods, Proc.. of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, 20-21 May 2002, Washington D.. 215-220.. Bach, M.. I.. Jordan, Kernel Independent Component Analysis, Journal of Machine Learning Research, Vol.. 3, 2002, pp.. 1-48.. , 482 kB.. Scholkopf, A.. Smola, K.. -R.. Muller, Nonlinear Component Analysis as a Kernel Eigenvalue Problem, Technical Report No.. 44, December 1996, 18 pages.. , 422 kB.. Yang, Face Recognition Using Kernel Methods, Advances in Neural Information Processing Systems, T.. Diederich, S.. Becker, Z.. Ghahramani, Eds.. , 2002, vol.. 14, 8 pages.. , 265 kB.. Zhou, R.. Chellappa, B.. Moghaddam, Intra-personal kernel space for face recognition, Proc.. of the 6th International Conference on Automatic Face and Gesture Recognition, FGR2004, 17-19 May 2004, Seoul, Korea, pp.. 235-240.. , 104 kB.. Chellappa, Multiple-exemplar discriminant analysis for face recognition, Proc.. of the 17th International Conference on Pattern Recognition, ICPR'04, 23-26 August 2004, Cambridge, UK, pp.. 191-194.. , 101 kB.. Venetsanopoulos, Face Recognition Using Kernel Direct Discriminant Analysis Algorithms, IEEE Trans.. 117-126.. , 749 kB.. Trace Transform.. The Trace transform, a generalization of the Radon transform, is a new tool for image processing which can be used for recognizing objects under transformations, e.. rotation, translation and scaling.. To produce the Trace transform one computes a functional along tracing lines of an image.. Different Trace transforms can be produced from an image using different trace functionals.. , 149 kB.. Kadyrov, M.. Petrou, The Trace Transform and Its Applications, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.. 8, August 2001, pp..  ...   International Conference on Image Processing, ICIP 2000, Vol.. 1, 10-13 September 2000, Vancouver, BC, Canada, pp.. 33-36.. Nefian, Embedded Bayesian networks for face recognition, Proc.. of the IEEE International Conference on Multimedia and Expo, Vol.. 2, 26-29 August 2002, Lusanne, Switzerland, pp.. 133-136.. Boosting Ensemble Solutions.. The idea behind Boosting is to sequentially employ a weak learner on a weighted version of a given training sample set to generalize a set of classifiers of its kind.. Although any individual classifier may perform slightly better than random guessing, the formed ensemble can provide a very accurate (strong) classifier.. Viola and Jones build the first real-time face detection system by using AdaBoost, which is considered a dramatic breakthrough in the face detection research.. On the other hand, papers by Guo et al.. are the first approaches on face recogntion using the AdaBoost methods.. Freund, R.. E.. Schapire, A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting, Journal of Computer and System Sciences, Vol.. 55, No.. 1, 1997, pp.. 119-139.. , 642 kB.. Meir, G.. Raetsch.. An Introduction to Boosting and Leveraging, In S.. Mendelson and A.. Smola, Editors, Advanced Lectures on Machine Learning, LNAI 2600, pp.. 118-183, Springer, 2003.. , 902 kB.. 2, May 2004, pp.. , 328 kB.. Venetsanopoulos, S.. Li, Ensemble-based Discriminant Learning with Boosting for Face Recognition, IEEE Transactions on Neural Networks, Vol.. 17, No.. 1, January 2006, pp.. 166-178.. , 766 kB.. -D.. Guo, H.. -J.. Zhang, S.. Li, Pairwise Face Recognition, Proc.. 282-287.. Zhang, Boosting for Fast Face Recognition, Second International Workshop on Recognition, Analysis and Tracking of Faces and Gestures in Real-time Systems, RATFG-RTS'01, in conjunction with ICCV 2001, 13 July 2001, Vancouver, Canada, pp.. 96-100.. , 130 kB.. Experimental design is an important (yet often neglected) part of face recognition research.. Reporting detailed experimental setup parameters like:.. identification/verification performance,.. database/protocol used,.. number of images/classes in the training, gallery and probe set,.. possible overlap of images in gallery and training set,.. statistical significance of the reported improvements (preferably with hypothesis testing as well), etc.. ,.. will make papers more readable and will make it possible for other researchers to easily evaluate reported results.. Also, results will become independently reproducible.. Here are some papers that will help you design and conduct your experiments, and subsequently, improve the quality or your papers as they will be put on solid scientific basis.. Face Recognition Vendor Test.. Evaluation of Face Recognition Algorithms.. Beveridge, K.. She, B.. Draper, and G.. Givens.. Parametric and Non-parametric Methods for the Statistical Evaluation of HumanID Algorithms, Third Workshop on Empirical Evaluation Methods in Computer Vision, Kauai, HI, December 2001.. Givens, A Nonparametric Statistical Comparison of Principal Component and Linear Discriminant Subspaces for Face Recognition, Proc.. of the IEEE Conference on Computer Vision and Pattern Recognition, December 2001, Kaui, HI, USA, pp.. 535-542.. Draper, K.. Baek, M.. Bartlett, and J.. Beveridge, Recognizing Faces with PCA and ICA, Computer Vision and Image Understanding (Special Issue on Face Recognition), Vol.. 91, Issues 1-2, July-August 2003, pp.. 115-137.. , 577 kB.. Rauss, The FERET Evaluation Methodology for Face-Recognition Algorithms, IEEE Trans.. on Pattern Recognition and Machince Intelligence, Vol.. Phillips, E.. Newton, Meta-Analysis of Face Recognition Algorithms, Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition (FRG'02), 20-21 May 2002, Washington, D.. 224-230.. Moon, The FERET Verification Testing Protocol for Face Recognition Algorithms, Technical Report NISTIR 6218, Nat'l Inst.. Standards and Technology, 1998.. , 282 kB.. Moon, The FERET Verification Testing Protocol for Face Recognition Algorithms, Third IEEE International Conference on Automatic Face and Gesture Recognition, 14-16 April 1998, Nara, Japan, pp.. 48-53.. Delac, M.. Grgic, S.. Grgic, Statistics in Face Recognition: Analyzing Probability Distributions of PCA, ICA and LDA Performance Results, Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, ISPA 2005, Zagreb, Croatia, 15-17 September 2005, pp.. 289-294.. , 79 kB.. Grgic, Generalization Abilities of Appearance-Based Subspace Face Recognition Algorithms, Proceedings of the 12th International Workshop on Systems, Signals and Image Processing, IWSSIP 2005, Chalkida, Greece, 22-24 September 2005, pp.. 273-276.. , 337 kB.. Grgic, Independent Comparative Study of PCA, ICA, and LDA on the FERET Data Set, International Journal of Imaging Systems and Technology, Vol.. 15, Issue 5, pp.. 252-260.. , 412 kB.. Ruiz-del-Solar, J.. Quinteros.. Illumination compensation and normalization in eigenspace-based face recognition: A comparative study of different pre-processing approaches, Pattern Recognition Letters, Vol.. 29, Issue 14, October 2008, pp.. 1966-1979.. 58 MB.. More algorithms comparisons are also available on our Vendors page.. Video-Based Face Recognition Algorithms.. During the last couple of years more and more research has been done in the area of face recognition from image sequences.. Recognizing humans from real surveillance video is difficult because of the low quality of images and because face images are small.. Still, a lot of improvement has been made.. Edwards, C.. Taylor, T.. Cootes, Improving Identification Performance by Integrating Evidence from Sequences, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol.. 1, 23-25 June 1999, Ft.. Collins, CO, USA, pp.. 486-491.. Gorodnichy, Video-Based Framework for Face Recognition in Video, Second Workshop on Face Processing in Video (FPiV'05), Proc.. of the Second Canadian Conference on Computer and Robot Vision (CRV'05), 09-11 May 2005, Victoria, British Columbia, Canada, pp.. 330-338.. , 827 kB.. Zhou, V.. Krueger, R.. Chellappa, Probabilistic recognition of human faces from video, Computer Vision and Image Understanding, Vol.. 91, 2003, pp.. 214-245.. , 994 kB.. Moghaddam, Visual tracking and recognition using appearance-adaptive models in particle filters, IEEE Trans.. on Image Processing, Vol.. 11, November 2004, pp.. 1491-1506.. Chellappa, Probabilistic identity characterization for face recognition, Proc.. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR, 27 June - 02 July 2004, Washington, DC, USA, pp.. II-805 - II-812.. Z.. Biuk, S.. Loncaric, Face recognition from multi-pose image sequence, Proc.. of the 2nd IEEE R8-EURASIP Symposium on Image and Signal Processing and Analysis, ISPA'01, 19-21 June 2001, Pula, Croatia, pp.. 319-324.. Lee, J.. Ho, M.. Kriegman, Video-Based Face Recognition Using Probabilistic Appearance Manifolds, Proc.. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2003, Vol.. I, 16-22 June 2003, Madison, Wisconsin, USA, pp.. 313-320.. Liu, T.. Chen, Video-Based Face Recognition Using Adaptive Hidden Markov Models, Proc.. 340-345.. Aggarwal, A.. Roy-Chowdhury, R.. Chellappa, A System Identification Approach for Video-based Face Recognition, Proc.. of the International Conference on Pattern Recognition, 23-26 August 2004, Cambridge, UK.. , 70 kB.. Last update: 7 November 2008..

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  • Title: Face Recognition Homepage - Source Codes
    Descriptive info: SOURCE CODES.. On this page you can find source codes contributed by users.. For the contributed materials to be useful to a wide audience with various levels of expertise, we would like to encourage extensive commenting of the codes and detailed header at the beginning of each file.. Please follow this link for an example of the header.. Please send the source codes you wish to publish to.. Feel free to include any additional material that could be useful (e.. if you developed a new method, include your paper explaining the method along with the source code).. This material is distributed in hope that it will be useful, but.. without any warranty.. No author or distributor accepts responsibility to anyone for the consequences of using it or for whether it serves any particular purpose or works at all, unless she/he says so in writing.. Everyone is granted permission to copy and redistribute the  ...   January 2007.. Multilinear Principal Component Analysis (MPCA).. (2.. 09 MB) |.. Haiping LU |.. 24 June 2008.. The INface toolbox for illumination invariant face recognition (v2.. 0).. -.. ChangeLog.. (749 kB) |.. Vitomir Struc |.. PhD (Pretty helpful Development) functions for face recognition toolbox.. (2,66 MB) |.. OTHER.. FaceRecLib - designed to run face recognition experiments.. in a comparable and reproducible manner.. Biometrics group - Idiap Research Institute.. FaceRecLib.. FaceRecognizer - Face Recognition with OpenCV.. Philipp Wagner.. OpenCV Face Recognition Module.. Guide to face recognition with Python:.. Detailed Explanation and Complete Source Code Examples.. Guide to face recognition with MATLAB/GNU Octave:.. It is assumed that the source codes are provided under.. GNU license.. and will be used for research purposes only.. Although basic virus checking will be performed on each contributed file, this site and its administrators take no responsibility for any damage that materials downloaded from this site may do to your computer..

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  • Title: Face Recognition Homepage - Conferences
    Descriptive info: CONFERENCES.. 2012.. 2011.. 2010.. 2009.. 2008.. 2007.. 2006.. 2005.. ICB2012.. , 5th International Conference on Biometrics,.. 30 March -1 April 2012, New Delhi, India.. Deadline:.. September 15, 2011.. IICAI-11.. , 5th Indian International Conference on Artificial Intelligence,.. December 14-16, 2011, Tumkur (near Bangalore), India.. May 9, 2011.. IJCB 2011.. , International Joint Conference on Biometrics,.. October 11-13, 2011, Washington, DC, USA.. May 27, 2011.. ICDAR 2011.. , 12th International Conference on Document Analysis and Recognition,.. September 18-21, 2011, Beijing, China.. March 1, 2011.. ICIP 2011.. , IEEE International Conference on Image Processing,.. September 11-14, 2011, Brussels, Belguim.. January 14, 2011.. REACTS 2011.. , Workshop on Recognition and Action for Scene Understanding,.. September 1-2, 2011, Malaga, Spain.. March 15, 2011.. IPCV 2011.. , The 2011 International Conference on Image Processing, Computer Vision, and Pattern Recognition,.. July 18-21, 2011, Las Vegas, Nevada, USA.. March 10, 2011.. ICIAR 2011.. , International Conference on Image Analysis and Recognition,.. June 22-24, 2011, Burnaby, BC, Canada.. January 17, 2011.. CVPR 2011.. , IEEE Computer Vision and Pattern Recognition,.. June 21-25, 2011, Colorado Springs, USA.. November 11, 2010.. ICASSP 2011.. , IEEE International Conference on Acoustics, Speech and Signal Processing,.. May 22-27, 2011, Prague, Czech Republic.. October 20, 2010.. EmoSPACE 2011.. , 1st International Workshop on Emotion Synthesis, rePresentation, and Analysis in Continuous spacE,.. May 21-25, 2011, Santa Barbara, CA, USA.. December 19, 2010.. FG 2011.. , IEEE Conference on Face and Gesture Recognition,.. March 21-24, 2011, Santa Barbara, USA.. October 13, 2010.. WACV 2011.. , IEEE Workshop on Applications of Computer Vision,.. January 5-7, 2011, Kona, Hawaii, USA.. August 19, 2010.. ICIP 2010.. , 17th IEEE International Conference on Image Processing,.. September 12-15, 2010, Hong Kong.. January 11, 2010.. ECCV 2010.. , 11th European Conference on Computer Vision,.. September 5-11, 2010, Hersonissos, Heraklion, Crete, Greece.. March 10, 2010.. ICEBT 2010.. , International Conference and Exhibition on Biometrics Technology,.. September 3-4, 2010, Coimbatore, India.. June 2, 2010.. ICPR 2010.. , 20th International Conference on Pattern Recognition,.. August 23-26, 2010, Istanbul, Turkey.. January 15, 2010.. IVPCV-10.. , 2010 International Conference on Image and Video Processing and Computer Vision,.. July 12-14, 2010, Orlando, FL, USA.. February 1, 2010.. AMFG 2010.. , IEEE International Workshop on Analysis and Modeling of Faces and Gestures,.. June 14, 2010, Hyatt Regency, San Francisco, California, USA.. March 10, 2010.. CVPR 2010.. , IEEE Computer Society Conference on Computer Vision and Pattern Recognition,.. June 13-18, 2010, Hyatt Regency, San Francisco, California, USA.. November 19, 2009.. VISAPP 2010.. , 5th International Conference on Computer Vision Theory and Applications,.. May 17-21, 2010, Angers, France.. January 12, 2010.. SPIE DS108.. , Biometric Technology for Human Identification VI,.. April 5-9, 2010, Orlando, FL, USA.. September 21, 2009.. ICASSP 2010.. , IEEE International Conference on Acoustics, Speech, and Signal Processing,.. March 14-19, 2010, Dallas, Texas, USA.. September 14, 2009.. ICDIP 2010.. , 2nd International Conference on Digital Image Processing,.. February 26-28, 2010, Singapore.. September 15, 2009.. WIFS 2009.. , First IEEE Workshop on Information Forensics and Security,.. December 6-9, 2009, London, United Kingdom.. May 22, 2009.. ICIP 2009.. , 16th IEEE International Conference on Image Processing,.. November 7-11, 2009, Cairo, Egypt.. January 30, 2009.. ICCV 2009.. , 12th IEEE International Conference on Computer Vision,.. September 27 - October 4, 2009, Kyoto, Japan.. March 1, 2009.. ICIAP 2009.. , 15th International Conference on Image Analysis and Processing,.. September 8-11, 2009, Vietri sul Mare, Salerno, Italy.. January 31, 2009.. CAIP 2009.. , 13th International Conference on Computer Analysis of Images and Patterns,.. September 2-4, 2009, Muenster (North Rhine-Westphalia), Germany.. March 31, 2009.. ICIAR 2009.. July 6-8, 2009, Halifax, Canada.. December 8, 2008.. CVPR 2009.. June 20-26, 2009, Fontainebleau Resort, Miami Beach, FL, USA.. November 13, 2008.. ICB 2009.. , 3rd IAPR/IEEE International Conference on Biometrics,.. June 2-5, 2009, University of Sassari, Italy.. November 1, 2008.. CORES 2009.. , 6th International Conference on Computer Recognition Systems,.. May 25-28, 2009, Paulinum Palace, Jelenia Góra, Poland.. January 14, 2009.. CRV 2009.. , Sixth Canadian Conference on Computer and Robot Vision,.. May 25-27, 2009, Kelowna, British Columbia.. MVA 2009.. , IAPR Conference on Machine Vision Applications.. May 20-22, 2009, Hiyoshi Campus, Keio University, Japan.. December 15, 2008.. PRIP'2009.. , Tenth International Conference on Pattern Recognition and Information Processing,.. May 19-21, 2009, Minsk, Belarus.. ICASSP 2009.. April 19-24, 2009, Taipei, Taiwan, R.. O.. September 29, 2008.. SPIE 7306B.. April 13, 2009, Orlando, FL, USA.. ICDIP 2009.. , International Conference on Digital Image Processing,.. March 7-9, 2009, Bangkok, Thailand.. November 25, 2008.. VISAPP 2009.. , International Conference on Computer Vision Theory and Applications,.. February 5-8, 2009, Lisboa, Portugal.. October 27, 2008.. IHCI 2009.. , First International Conference on Intelligent Human Computer Interaction,.. Januaury 20-23, 2009,  ...   Indian International Conference on Artificial Intelligence,.. December 17-19, 2007, Pune, India.. April 02, 2007.. CBITE 2007.. , 2007 China International Biometric Identification Technology & Application Exhibition,.. November 14-17, 2007, Shanghai, China.. ---.. VipIMAGE.. , I ECCOMAS Thematic Conference on Computational Vision and Medical Image Processing,.. October 17-19, 2007, Porto, Portugal.. March 15, 2007.. AMFG 2007.. , Third IEEE International Workshop on Analysis and Modeling of Faces and Gestures,.. October 20, 2007, Rio de Janeiro, Brazil.. May 18, 2007.. ICCV 2007.. , 11th IEEE International Conference on Computer Vision,.. October 14-21, 2007, Rio de Janeiro, Brazil.. April 10, 2007.. ICIP 2007.. , 14th IEEE International Conference on Image Processing,.. September 16-19, 2007, San Antonio, Texas, USA.. January 19, 2007.. bsym2007.. , Biometrics Symposium 2007,.. September 11-13, 2007, Baltimore, Maryland, USA.. April 15, 2007.. ICIAP 2007.. , 14th International Conference on Image Analysis and Processing,.. September 10-13, 2007, Modena, Italy.. February 16, 2007.. Biometrics2007.. , Conference on Biometrical Feature Identification and Analysis,.. September 06-08, 2007, University of Göttingen, Germany.. July 31, 2007.. AVSS 2007.. , 2007 IEEE International Conference on Advanced Video and Signal based Surveillance,.. September 05-07, 2007, London, United Kingdom.. CAIP 2007.. , The 12th International Conference on Computer Analysis of Images and Patterns,.. September 03-05, 2007, Vienna, Austria.. March 30, 2007.. ICB 2007.. , International Conference on Biometrics,.. August 27-29, 2007, Seoul, Korea.. January 31, 2007.. AIPR-07.. , International Conference on Artificial Intelligence and Pattern Recognition,.. July 09-12, 2007, Orlando, FL, USA.. February 01, 2007.. CIVR 2007.. , ACM International Conference on Image and Video Retrieval,.. July 09-11, 2007, Amsterdam, The Netherlands.. February 05, 2007.. ICME07.. , 2007 IEEE International Conference on Multimedia,.. July 02-05, 2007, Beijing, China.. February 02, 2007.. CVPR 2007.. June 17-22, 2007, Chicago, USA.. November 27, 2006.. OTCBVS 2007.. , 4th IEEE Workshop on Object Tracking and Classification in and Beyond Visual Spectrum, June 22, 2007 (in conjunction with CVPR2007), Minneapolis, MN, USA.. March 20, 2007.. CVR 2007.. , Fourth Canadian Conference on Computer and Robot Vision,.. May 28-30, 2007, Montreal, QC, Canada.. January 19, 2007.. VideoRec'07.. , International Workshop on Video Processing and Recognition,.. MVA 2007.. , IAPR Conference on Machine Vision Applications,.. May 16-18, 2007, Tokyo, Japan.. December 15, 2006.. ICASSP 2007.. April 15-20, 2007, Honolulu, Hawaii, USA.. September 29, 2006.. VISAPP 2007.. , International Conference on Computer Vision Theory and Applications.. March 08-11, 2007, Barcelona, Spain.. November 06, 2006.. CompIMAGE.. , Computational Modelling of Objects Represented in Images: Fundamentals, Methods and Applications,.. October 20-21, 2006, Coimbra, Portugal.. June 30, 2006.. ICIP 2006.. , 13th IEEE International Conference on Image Processing,.. October 08-11, 2006, Atlanta, GA, USA.. January 16, 2006.. BMVC 2006.. , The British Machine Vision Conference,.. September 04-07, 2006, Edinburgh, UK.. April 12, 2006.. ICPR 2006.. , International Conference on Pattern Recognition,.. August 20-24, 2006, Hong Kong.. January 16, 2006.. CVPR 2006.. June 17-22, 2006, New York, NY, USA.. November 15, 2005.. ELMAR-2006.. , 48th International Symposium ELMAR-2006 focused on Multimedia Signal Processing and Communications,.. June 07-10, 2006, Zadar, Croatia.. February 22, 2006.. VP4S-06.. , The First International Workshop on Video Processing for Security.. June 07-09, 2006, Quebec City, Canada.. February 12, 2006.. ECCV 2006.. , 9th European Conference on Computer Vision,.. May 07-13, 2006, Graz, Austria.. September 26, 2005.. FG 2006.. , 7th IEEE International Conference Automatic Face and Gesture Recognition,.. April 10-12, 2006, Southampton, UK.. November 01, 2005.. VISAPP 2006.. February 22-25, 2006, Setúbal, Portugal.. ICBA 2006.. , International Conference on Biometric Authentication,.. January 05-07, 2006, Hong Kong.. June 12, 2005.. PReMI'05.. , 1st First International Conference on Pattern Recognition and Machine Intelligence,.. December 18-22, 2005, Kolkata, India.. March 01, 2005.. AMFG 2005.. October 16, 2005, Beijing, China.. May 20, 2005.. ICCV 2005.. , 10th IEEE International Conference on Computer Vision,.. October 15-21, 2005, Beijing, China.. March 10, 2005.. ICIP 2005.. , 12th IEEE International Conference on Image Processing,.. September 11-14, 2005, Genova, Italy.. January 10, 2005.. ICAPR 2005.. , 3rd International Conference on Advances in Pattern Recognition,.. August 22-25, 2005, Bath, UK.. April 20, 2005.. AVBPA 2005.. , Audio- and Video-based Biometric Person Authentication 2005,.. July 20-22, 2005, Tarrytown, NY, USA.. December 10, 2004.. FRGC05.. , IEEE Workshop on Face Recognition Grand Challenge Experiments,.. June 21, 2005, San Diego, CA, USA.. November 01, 2004.. CVPR 2005.. , IEEE Computer Society International Conference on Computer Vision and Pattern Recognition,.. June 20-26, 2005, San Diego, CA, USA.. ELMAR-2005.. , 47th International Symposium ELMAR-2005 focused on Multimedia Systems and Applications,.. June 08-10, 2005, Zadar, Croatia.. February 11, 2005.. VIE 2005.. , The IEE International Conference on Visual Information Engineering - Convergence in Graphics and Vision,.. April 04-06, 2005, Glasgow, UK.. November 12, 2004.. Last update: 8 July 2011..

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