LITERATURE SURVEY OF AUTOMATIC FACE RECOGNITION ...

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Step 4: Calculate the eigenvectors and eigenvalues of the covariance matrix In this step, the eigenvectors (eigenfaces) ui and the corresponding eigenvalues λi should be calculated. The eigenvectors (eigenfaces) must be normalized so that they are unit vectors, i.e. of length 1. The description of the exact algorithm for determination of eigenvectors and eigenvalues is omitted here, as it belongs to the standard arsenal of most math programming libraries. Step 5: Select the principal components From M eigenvectors (eigenfaces) ui, only M ′ should be chosen, which have the highest eigenvalues. The higher the eigenvalue, the more characteristic features of a face does the particular eigenvector describe. Eigenfaces with low eigenvalues can be omitted, as they explain only a small part of characteristic features of the faces. After M ′ eigenfaces ui are determined, the ‘training” phase of the algorithm is finished. 54
CHAPTER XIV IMPROVEMENT <strong>OF</strong> THE ALGORITHM The covariance matrix C in step 3 has a dimensionality of N2 × N2, so one would have N2 eigenfaces and eigenvalues. For a 256 × 256 image that means that one must compute a 65, 536 × 65, 536 matrix and calculate 65, 536 eigenfaces. Computationally, this is not very efficient as most of those eigenfaces are not useful for our task. So, the step 3 and 4 is replaced by the following steps: Where L is a M × M matrix, v are M eigenvectors of L and u are eigenfaces. Note that the covariance matrix C is calculated using the formula C = AA T . The advantage of this method is that one has to evaluate only M numbers and not N 2 . Usually, M
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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 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 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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