LITERATURE SURVEY OF AUTOMATIC FACE RECOGNITION ...

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Fig.7.1.Face Recognition Core Paper 44
Table 7.1 Internet Resources for Research and Databases Research pointers Home page www.facerec.org Face recognition homepage www.cs.rug.nl/~peterkr/<strong>FACE</strong>/frhp.html Face detection homepage home.tonline.de/home/Robert.Frischholz/face.htm Facial analysis homepage mambo.ucsc.edu/psl/fanl.html Facial animiation homepage Face databases mambo.ucsc.edu/psl/fan.html FERET database http://www.itl.nist.gov/iad/humanid/feret/ XM2TVS database http://www.ee.surrey.ac.uk/Research/VSSP/xm2vtsdb/ UT Dallas database http://www.utdallas.edu/dept/bbs/FACULTY PAGES/otoole/database.htm Notre Dame database http://www.nd.edu/~cvrl/HIDdata.html MIT face databases ftp://whitechapel.media.mit.edu/pub/images/ Shimon Edelman’s face database ftp://ftp.wisdom.weizmann.ac.il/pub/FaceBase/ CMU face detection database www.ius.cs.cmu.edu/IUS/dylan usr0/har/faces/test/ CMU PIE database www.ri.cmu.edu/projects/project 418.html Stirling face database pics.psych.stir.ac.uk M2VTS multimodal database www.tele.ucl.ac.be/M2VTS/ Yale face database cvc.yale.edu/projects/yalefaces/yalefaces.html Yale face database B cvc.yale.edu/projects/yalefacesB/yalefacesB.html Harvard face database hrl.harvard.edu/pub/faces Weizmann face database www.wisdom.weizmann.ac.il/~yael/ UMIST face database images.ee.umist.ac.uk/danny/database.html Purdue University face database rvl1.ecn.purdue.edu/~aleix/aleix face DB.html Olivetti face database www.camorl.co.uk/facedatabase.html Oulu physicsbased face database www.ee.oulu.fi/research/imag/color/pbfd.html
Page 1 and 2: LITERATURE SURVEY OF AUTOMATIC FACE
Page 3 and 4: ABSTRACT There are three fundamenta
Page 5 and 6: 2.3.2 Processing Steps before Face
Page 7 and 8: LIST OF TABLES Table Page 5.2.1 The
Page 9 and 10: 12.1. Functioning principle of the
Page 11 and 12: the brain recognizes a face has lon
Page 13 and 14: CHAPTER II HOW A COMPUTER CAN PERFO
Page 15 and 16: Fig.2.2. Process of face recognitio
Page 17 and 18: non convex variations of face manif
Page 19 and 20: 3.1 Some Major difficulty for AFR C
Page 21 and 22: Gender researchers found surprising
Page 23 and 24: The idea of this method comes from
Page 25 and 26: Fig.4.3.1. Principal Components (PC
Page 27 and 28: and orientations (phase, amplitude)
Page 29 and 30: 4.14 Bayesian Framework A probabili
Page 31 and 32: feature line of that class. The que
Page 33 and 34: oughly the same scale), scale norma
Page 35 and 36: Table 5.2.4 Result for combined dat
Page 37 and 38: performance of the AutoAssociatio
Page 39 and 40: Furthermore, three of the seven alg
Page 41 and 42: Table 6.1 Various application of au
Page 43: CHAPTER VIII FOR PROSPECTIVE RESEAR
Page 47 and 48: etween the computer vision, signal
Page 49 and 50: So, in order to reconstruct the ori
Page 51: CHAPTER XII EIGENVECTORS AND EIGENV
Page 54 and 55: Step 4: Calculate the eigenvectors
Page 56 and 57: CHAPTER XV CLASSIFYING THE FACES Th
Page 58 and 59: CHAPTER XVII INTRODUCTION Images ar
Page 60: CHAPTER XIX THE ALGORITHM As in all
Page 63 and 64: If the condition Zmin < Zmed < Zmax
Page 65 and 66: (a) (b) Fig 21.3: (c) The image wit
Page 67 and 68: [5] M. Yang, D. J. Kriegman, and N.
Page 69: [25] Adaptive median filters: new a

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