LITERATURE SURVEY OF AUTOMATIC FACE RECOGNITION ...

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LIST <strong>OF</strong> FIGURES Figure Page 1.1 Bees recognizing Human face……………………………………………………3 2.1 Face query from the database……………………………………………………4 2.2 Process of face recognition…………………………………………………….…6 4.1 some Face recognition methods……………………………………………..…14 4.3.1 Principal Components (PC) of a twodimensional set of points. The first principal component provides an optimal linear dimension reduction from 2D to 1D, in the sense of the mean square error……………………….….16 4.10.1 Jet……………………………………………………………………………….18 4.10.2 Labeled Graph………………………………………………………………...18 4.11.1. The training image is split into shape and shapenormalized texture…..19 4.15.1. HMM for face recognition image…………………………………………....21 4.15.2. Block extraction form ………………………………………………………..21 4.17.2.Generalizing two prototype feature points x1 and x2………………...……22 5.1. Examples of faces in the Weizmann data base…………………………….…25 6.1. The reduction in error rate for stateoftheart face recognition algorithms as documented through the FERET, the FRVT 2002, and the FRVT 2006 evaluations……………………………………………………………………………..30 7.1. Face Recognition Core Paper…………………………………………………..34 8
12.1. Functioning principle of the eigenfacebased face recognition algorithm….42 21.1 (a) The image with ‘salt and pepper’ noise of 0.35 noise density. (b) Output of the standard median filter. ……………………………………53 21.2 Output of the adaptive median filter shows the improvement in performance with respect to the standard median filter…………………………………….53 21.3 (a) The image with ‘speckle’ noise of 0.6 variance. (b) Output of Adaptive Median Filter……………………………………..….54 21.4 (a) Image with ‘Gaussian’ noise with 0 mean and 0.02 variance (b) Output of Adaptive Median Filter………………………………………...54 9
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: LIST OF TABLES Table Page 5.2.1 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 and 44: CHAPTER VIII FOR PROSPECTIVE RESEAR
Page 45 and 46: Table 7.1 Internet Resources for Re
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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